New AI Makes You Play Table Tennis…In a Virtual World! 🏓

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors" is available here: https://ift.tt/3mNw9vQ https://ift.tt/32QmGwB 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Peter Edwards, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Wish to watch these videos in early access? Join us here: https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

MirroredStrategy demo for distributed training

Google Cloud Developer Advocate Nikita Namjoshi demonstrates how to get started with distributed training on Google Cloud. Learn how to distribute training across multiple GPUs within a managed Jupyter Lab environment. Intro to distributed training → https://goo.gle/3FolcIz Creating and managing GCP projects → https://goo.gle/3mDZ4m7 Vertex AI → https://goo.gle/3FzfmUU Cloud Storage Quickstart → https://goo.gle/3qsNWcN Notebook Executor Codelab → https://goo.gle/3etrvPh Chapters: 00:00 - Introduction 00:40 - Get started with GCP 01:00 - Walking through the notebook’s code 02:46 - Creating a notebook on GCP 05:35 - Modifying the code for distributed training 06:59 - Running the distributed training job 08:41 - Another way to run distributed training 11:18 - The results 11:54 - Thanks for watching! Watch more ML Tech Talks → https://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow #TensorFlow #MachineLearning #ML product: TensorFlow - General;

A friendly introduction to distributed training (ML Tech Talks)

Google Cloud Developer Advocate Nikita Namjoshi introduces how distributed training models can dramatically reduce machine learning training times, explains how to make use of multiple GPUs with Data Parallelism vs Model Parallelism, and explores Synchronous vs Asynchronous Data Parallelism. Mesh TensorFlow → https://goo.gle/3sFPrHw Distributed Training with Keras tutorial → https://goo.gle/3FE6QEa GCP Reduction Server Blog → https://goo.gle/3EEznYB Multi Worker Mirrored Strategy tutorial → https://goo.gle/3JkQT7Y Parameter Server Strategy tutorial → https://goo.gle/2Zz3UrW Distributed training on GCP Demo → https://goo.gle/3pABNDE Chapters: 0:00 - Introduction 00:17 - Agenda 00:37 - Why distributed training? 1:49 - Data Parallelism vs Model Parallelism 6:05 - Synchronous Data Parallelism 18:20 - Asynchronous Data Parallelism 23:41 Thank you for watching Watch more ML Tech Talks → https://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow #TensorFlow #MachineLearning #ML product: TensorFlow - General;

Introduction to TensorFlow for Artificial Intelligence Machine Learning and Deep Learning

Deeplearning.ai If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Make object detection faster by using Coral

Learn how to make your object detection model run faster using Google Coral Edge TPU in this final episode of Machine Learning for Raspberry Pi. 00:00 Introduction 00:15 What is EdgeTPU? 01:24 Connecting Google Coral USB Accelerator 02:11 3 steps to run an object detection model on EdgeTPU 02:36 Step 1: Compile the TFLite model 04:15 Step 2: Install the EdgeTPU runtime 05:20 Step 3: Enable EdgeTPU when running the model 07:27 Demo 08:43 EdgeTPU model code 09:14 Coral’s repository of pretrained models 09:36 Thank you for watching! Colab notebook demonstrating how to compile a TensorFlow Lite model for Edge TPU → https://goo.gle/3G4RxUH Instructions to install the EdgeTPU runtime → https://goo.gle/3xY3oBb Sample app to run TensorFlow Lite object detection on Raspberry Pi → https://goo.gle/3GaABw3 Coral examples → https://goo.gle/3Dup355 Watch all Machine Learning for Raspberry Pi videos → https://goo.gle/ML-raspberrypi Subscribe to TensorFlow → https://goo.gle/TensorFlow #TensorFlow #MachineLearning #ML #RaspberryPi #EdgeAI product: TensorFlow - TensorFlow Lite, TensorFlow - General; fullname: Khanh LeViet;

Microsoft’s AI Understands Humans…But It Had Never Seen One! 👩‍💼

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Fake It Till You Make It - Face analysis in the wild using synthetic data alone " is available here: https://ift.tt/3A03APx ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Peter Edwards, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Wish to watch these videos in early access? Join us here: https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Machine Learning Holidays Live Stream

Chatting & Coding

TRAINING AN AI BOT TO SKI: PART 1 | AISTORY 069 #SHORTS

KEPT RUNNING INTO ISSUES SO DIDN'T REALLY GET MUCH DONE. TRYING TO CREATE A FUNCTION TO ESTIMATE WHERE THE PLAYER WILL END UP IN THE FUTURE. THAT IS PROVING MORE DIFFICULT THAN I THOUGHT #SHORTS Watch my journey to becoming an AI expert and game developer here: https://youtube.com/playlist?list=PLPHs6pLZ0TMaOr7stgGw908luFKvwl6bX --- Thank you for watching this video. I hope that you keep up with the daily videos I post on the channel, subscribe, and share your learnings/favourite moments with others. Your comments are highly valuable, so please take a second even to just say ‘Hi’! --- Subscribe to my channel here: https://www.youtube.com/channel/UCcOnxiBPLaOPxocfHWZOT1w?sub_confirmation=1 --- Follow me online here: Instagram: https://www.instagram.com/aisulymandotcom/ Facebook: https://www.facebook.com/AISulyman-103004018453254 Snapchat: https://www.snapchat.com/add/aisulyman Website: http://aisulyman.com Twitter: http://twitter.com/aisulyman TikTok: https://www.tiktok.com/@aisulyman Twitch: twitch.tv/aisulyman --- #gamedev #gamedevelopment #gamedeveloper #gamedevlife #gamedesign #gamedevs #indiegame #indiegames #indie #indiegamedev #indiegaming #indiegamedeveloper #videogames #videogame #videogaming #lua #love2d #atari #atari2600 #atarigames #ai #machinelearning #artificialintelligence

Build an Mario AI Model with Python | Gaming Reinforcement Learning

Teach AI to play Super Mario In this video you'll learn how to: Setup a Mario Environment Preprocess Mario for Applied Reinforcement Learning Build a Reinforcement Learning model to play Mario Take a look at the final results Get the code: https://github.com/nicknochnack/MarioRL Links: Super Mario RL: https://pypi.org/project/gym-super-mario-bros/ Nes Py: https://pypi.org/project/nes-py/ OpenAI Gym: https://gym.openai.com/ PyTorch: https://pytorch.org/get-started/locally/ PPO Algorithm: https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html Intro to RL Loss: https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html 0:00 - Start 0:18 - Introduction 0:44 - Explainer 1:58 - Client Interview 1 2:02 - Animation 1 2:30 - Tutorial Start 3:22 - Setting Up Mario 10:44 - Running the Game 18:26 - Understanding the Mario State and Reward 20:44 - Client Interview 2 21:38 - Preprocessing the Environment 26:22 - Installing the RL Libraries 31:11 - Applying Grayscaling 35:32 - Applying Vectorization 36:56 - Applying Frame Stacking 42:46 - Client Conversation 3 43:05 - Animation 3 44:00 - Importing the PPO Algorithm 47:33 - Setting Up the Training Callback 50:13 - Creating a Mario PPO Model 55:30 - Training the Reinforcement Learning Model 1:02:40 - Client Conversation 4 1:02:56 - Animation 4 1:04:01 - Loading the PPO Model 1:06:10 - Using the AI Model 1:15:56 - Client Conversation 5 1:16:37 - Ending Oh, and don't forget to connect with me! LinkedIn: https://bit.ly/324Epgo Facebook: https://bit.ly/3mB1sZD GitHub: https://bit.ly/3mDJllD Patreon: https://bit.ly/2OCn3UW Join the Discussion on Discord: https://bit.ly/3dQiZsV Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #ai #python

How to Use IF ELSE STATEMENT IN R | R TUTORIALS | BASICS OF DATA SCIENCE

How to Use IF ELSE STATEMENT IN R | R TUTORIALS | BASICS OF DATA SCIENCE #r #ifelse #ifelsestatementinR #computer #python #R #datascience #deeplearning #machine #machinelearning #tutorials #pythontutorial #Rtutorial #data #AI #pc #laptop #video #videoediting #photoshop #PhotoshopTutorial #AdobepremiereTutorials #MaskEffect #Annimation #AdobeText #Howto #LearnAdobePremiere #ExcelFormula #Exceltutorials #Howtocreatepivottable #excelformula #Excelcharts #technicalsurani Welcome to Technical Surani I am Mr. Surani ,Data Science Aspirant. I will teach you different technical hacks to horn up your expertise on Python, R , Data Science, Machine Learn, Tableau, Apache, MS Excel. Photoshop Tutorials, Adobe Premiere m Adobe After effects , MS Words, Power Points , Windows Hacks etc Please, Like , Share and hit bell icon in Channel. Thank you Excel Video, MS Office, MS Word, Excel Formula, Windows 7 Download, Pie Charts, Excel Bar Charts, Excel Tutorial, Pivot Table , excel dash board, Power Point Video, MS Word Video, Technology Video, How to, Photoshop tutorials for beginner, Adobe premiere video tutorial, Adobepremiere tutorials, Excel tutorials, excel charts, excel formulas , excel bar graphs, excel dash boards, how to create pivot table, how to create graph in excel, excel formulas and functions, excel tutorials , excel tutorials in HIndi and English

What is Machine Learning? #shorts #tutorial #ml #ai

AI Pathshala on Deep learning with MathWorks - Module II

Workshop Title - Development of Advanced Deep Neural Architecture and Embedded Deployment Speaker - Dr. Rishu Gupta Designation - Senior Application Engineer, MathWorks India

From Mesh To Yarn... In Real Time! 🧶

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd  📝 The paper "Mechanics-Aware Deformation of Yarn Pattern Geometry" is available here: https://ift.tt/3IGKio4 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Create an AI application in 5 minutes

Create an AI application in 5 minutes Use Teachable Machine to accelerate your AI or machine learning app development. #Petamind #AI #ML Visit petamind.com for more tutorials.

ARTIFICIAL INTELLIGENCE , MACHINE LEARNING TUTORIAL

ARTIFICIAL INTELLIGENCE , MACHINE LEARNING TUTORIAL

This Table Cloth Pull is Now Possible! 🍽

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Codimensional Incremental Potential Contact (C-IPC)" is available here: https://ift.tt/3y4oVbx ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #gamedev

Machine Learning Tutorial | Decision Tree Classifier - Part 1

In this video, we learn how to create a decision tree model using Scikit-learn and Python. It will use the horsepower and seats of a car to classify whether it is a sports car or minivan.

Machine Learning Tutorial | Decision Trees - Part 2

In this video, we learn how to apply a decision tree model to the titanic dataset, which is available on Kaggle.

Tutorial Flask Romana | Episodul 1 | Introducere in Flask

Comanda pentru instalarea Flask in terminal: pip install flask Tags Flask PYTHON TUTORIAL PYTHON ROMANA Numpy Machine Learning Artificial Intelligence AI ML Python Python Tutorial Tutorial de python Programare Informatica

AI ML LAB 3.1 | AWS | AICTE AI-ML and DATA ANALYTICS

AI ML LAB 3.1 | AWS | AICTE AI-ML and DATA ANALYTICS

This AI Makes Celebrities Old…For a Price! 👵

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Only a Matter of Style: Age Transformation Using a Style-based Regression Model" is available here: https://ift.tt/3dkcKh5 Demo: https://ift.tt/2Xt2fDN ▶️Our Twitter: https://twitter.com/twominutepapers 📝 Our material synthesis paper with the latent space: https://ift.tt/2HhNzx5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Object Detection Deep Learning Full Tutorial - Part 1 - Intro to Object Detection

Welcome to our Full Tutorial Video Series of - Object Detection with Deep Learning Pre-Requisites: Core Python : https://www.youtube.com/watch?v=3UzIGstC7E0&list=PL3Sk77w7CQs_aV7aaGgXBdbwPA8lAXI9- Numpy : https://www.youtube.com/watch?v=PCax_7Efg9Q&list=PL3Sk77w7CQs-F8mibBu1S9B6qTuuhDcol Pandas : https://www.youtube.com/watch?v=G3DeEQKp4zg&list=PL3Sk77w7CQs-VXMjV4MNpnRoXVXrIaXBK Matplotlib: https://www.youtube.com/watch?v=z1D-kaOe9tQ&list=PL3Sk77w7CQs8zbA-EuEREWIrKpYcAsh3Y Machine Learning Tutorial: https://www.youtube.com/watch?v=yf5STEdkiQs&list=PL3Sk77w7CQs-f5TSaLSyU1JXgm85BEPuZ Deep Learning with Tensorflow: https://www.youtube.com/watch?v=l8HaRZWHmfg&list=PL3Sk77w7CQs_VrXLP_Kmj3ufmhehMrfHp All the best

Sklearn Pipeline Tutorial Full - Advanced Machine Learning Tutorial Pipeline Creation

Source code: https://github.com/manifoldailearning/Youtube/blob/master/Sklearn_Pipeline.ipynb Hands-On ML Book Series - https://www.youtube.com/playlist?list=PL3Sk77w7CQs-f5TSaLSyU1JXgm85BEPuZ Deep learning Tensorflow Playlist - https://www.youtube.com/playlist?list=PL3Sk77w7CQs_VrXLP_Kmj3ufmhehMrfHp Deep Learning with Pytorch - https://youtube.com/playlist?list=PL3Sk77w7CQs_GYy2Ruf62WJBdKfRv9J2N Linear Algebra Playlist - https://www.youtube.com/playlist?list=PL3Sk77w7CQs9c6GhauRPhKd4HPAM7E5Vi Tips & Tricks for Data Science - Machine Learning - Deep learning - https://www.youtube.com/playlist?list=PL3Sk77w7CQs8ftC0Cyyu20LFGA-nnzDmm Subscribe for more updates

[ML News] OpenAI removes GPT-3 waitlist | GauGAN2 is amazing | NYC regulates AI hiring tools

#mlnews #gaugan #gpt-3 Your weekly dose of ML News! More GauGAN images here: https://ift.tt/3rq3MHo OUTLINE: 0:00 - Intro 0:20 - Sponsor: Weights & Biases 2:20 - OpenAI's removes GPT-3 Waitlist 4:55 - NVIDIA releases GauGAN2 Webapp 9:45 - Everyday Robots tackles real-life tasks 12:15 - MetNet-2: 12-hour Rain Forecasting 14:45 - TinyML Dog Bark Stopper 15:55 - AI learns to drive Mario Kart 64 on real hardware 17:40 - NYC regulates bias in AI hiring tools 21:05 - Beverage companies big into AI 21:50 - How does AlphaZero play Chess? 23:35 - Helpful Things 28:00 - ArXiv founder awarded Einstein Foundation Award References: OpenAI's removes GPT-3 Waitlist https://ift.tt/30xVe5N https://ift.tt/31oebsf NVIDIA releases GauGAN2 Webapp https://ift.tt/3rzxLwu https://ift.tt/3DMCQ7Q https://ift.tt/2ZfJ0hT https://ift.tt/2FksFMe https://ift.tt/2UIVsPD Everyday Robots tackles real-life tasks https://ift.tt/3cqNUf4 https://ift.tt/3ctKsAd https://ift.tt/3de4v6b MetNet-2: 12-hour Rain Forecasting https://ift.tt/3Fj9ZZf TinyML Dog Bark Stopper https://ift.tt/3rwbqA4 AI learns to drive Mario Kart 64 on real hardwware https://www.youtube.com/watch?v=z9E38sN5nRQ NYC regulates bias in AI hiring tools https://ift.tt/3oIHBZW Beverage companies big into AI https://ift.tt/3FzdwCK How does AlphaZero play Chess? https://ift.tt/30RMM20 https://ift.tt/3lu1zXJ Helpful Things https://ift.tt/3G02c30 https://ift.tt/3lu1Bij https://ift.tt/31kUHoe https://ift.tt/3FtxZc9 https://ift.tt/3rtgg0X https://ift.tt/2M5qQZE https://ift.tt/3k4Jihy https://deepgenx.com/ https://ift.tt/3oUqODs https://ift.tt/31sceuw https://ift.tt/3xLDJM1 https://ift.tt/31oCTIO ArXiv founder awarded Einstein Foundation Award https://ift.tt/3Iffyu8 Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

X ray Image Processing (AI-Batch2)

The project is to test the AI automated Chest X-ray image processing algorithm which the initial model allows in differentiating Chest X-rays of Normal lungs and with a possible diagnosis of pneumonia.

Object Detection Deep Learning Full Tutorial - Part 1 - Intro to Object Detection

Welcome to our Full Tutorial Video Series of - Object Detection with Deep Learning Pre-Requisites: Core Python : https://www.youtube.com/watch?v=3UzIGstC7E0&list=PL3Sk77w7CQs_aV7aaGgXBdbwPA8lAXI9- Numpy : https://www.youtube.com/watch?v=PCax_7Efg9Q&list=PL3Sk77w7CQs-F8mibBu1S9B6qTuuhDcol Pandas : https://www.youtube.com/watch?v=G3DeEQKp4zg&list=PL3Sk77w7CQs-VXMjV4MNpnRoXVXrIaXBK Matplotlib: https://www.youtube.com/watch?v=z1D-kaOe9tQ&list=PL3Sk77w7CQs8zbA-EuEREWIrKpYcAsh3Y Machine Learning Tutorial: https://www.youtube.com/watch?v=yf5STEdkiQs&list=PL3Sk77w7CQs-f5TSaLSyU1JXgm85BEPuZ Deep Learning with Tensorflow: https://www.youtube.com/watch?v=l8HaRZWHmfg&list=PL3Sk77w7CQs_VrXLP_Kmj3ufmhehMrfHp All the best

Sparse is Enough in Scaling Transformers (aka Terraformer) | ML Research Paper Explained

#scalingtransformers #terraformer #sparsity Transformers keep pushing the state of the art in language and other domains, mainly due to their ability to scale to ever more parameters. However, this scaling has made it prohibitively expensive to run a lot of inference requests against a Transformer, both in terms of compute and memory requirements. Scaling Transformers are a new kind of architecture that leverage sparsity in the Transformer blocks to massively speed up inference, and by including additional ideas from other architectures, they create the Terraformer, which is both fast, accurate, and consumes very little memory. OUTLINE: 0:00 - Intro & Overview 4:10 - Recap: Transformer stack 6:55 - Sparse Feedforward layer 19:20 - Sparse QKV Layer 43:55 - Terraformer architecture 55:05 - Experimental Results & Conclusion Paper: https://ift.tt/3E3Mmnq Code: https://ift.tt/3daqnPP Abstract: Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reach. We address this problem by leveraging sparsity. We study sparse variants for all layers in the Transformer and propose Scaling Transformers, a family of next generation Transformer models that use sparse layers to scale efficiently and perform unbatched decoding much faster than the standard Transformer as we scale up the model size. Surprisingly, the sparse layers are enough to obtain the same perplexity as the standard Transformer with the same number of parameters. We also integrate with prior sparsity approaches to attention and enable fast inference on long sequences even with limited memory. This results in performance competitive to the state-of-the-art on long text summarization. Authors: Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin, Łukasz Kaiser, Wojciech Gajewski, Henryk Michalewski, Jonni Kanerva Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Simulating A Virtual World…For A Thousand Years! 🤯

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Synthetic Silviculture: Multi-scale Modeling of Plant Ecosystems" is available here: https://ift.tt/3Ddkyvt 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Kelompok 6 | Resume Video BISA AI "Tutorial Kalkulus Machine Learning"

Anggota Kelompok 6: 1. Vidya Adelia Anya 2. Debi Manda 3. Athalla Aqsha Suryoatmodjo

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (Paper Explained)

#ext5 #transferlearning #exmix The T5 model has been a staple for NLP research for the last years. Both its size and its approach to formulate all NLP tasks as prompt-based language modeling make it a convenient choice to tackle new challenges and provides a strong baseline for most current datasets. ExT5 pushes T5 to its limits by pre-training not only on self-supervised mask filling, but also at the same time on 107 different supervised NLP tasks, which is their new ExMix dataset. The resulting model compares very favorably to T5 when fine-tuned to downstream tasks. OUTLINE: 0:00 - Intro & Overview 2:15 - Recap: The T5 model 3:55 - The ExT5 model and task formulations 8:10 - ExMix dataset 9:35 - Do different tasks help each other? 16:50 - Which tasks should we include? 20:30 - Pre-Training vs Pre-Finetuning 23:00 - A few hypotheses about what's going on 27:20 - How much self-supervised data to use? 34:15 - More experimental results 38:40 - Conclusion & Summary Paper: https://ift.tt/3FYbs7B Abstract: Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces ExMix (Extreme Mixture): a massive collection of 107 supervised NLP tasks across diverse domains and task-families. Using ExMix, we study the effect of multi-task pre-training at the largest scale to date, and analyze co-training transfer amongst common families of tasks. Through this analysis, we show that manually curating an ideal set of tasks for multi-task pre-training is not straightforward, and that multi-task scaling can vastly improve models on its own. Finally, we propose ExT5: a model pre-trained using a multi-task objective of self-supervised span denoising and supervised ExMix. Via extensive experiments, we show that ExT5 outperforms strong T5 baselines on SuperGLUE, GEM, Rainbow, Closed-Book QA tasks, and several tasks outside of ExMix. ExT5 also significantly improves sample efficiency while pre-training. Authors: Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Gradient Descent Algorithm (GD Algorithm) | Deep Learning Tutorials | Society of AI

Deep Learning Tutorials In this video we discussed about GD Algorithm: Gradient Descent is the most common optimization algorithm in machine learning and deep learning. It is a first-order optimization algorithm. In above video we talk about GD Algorithm and Vectorized GD Algorithm: 1. Modified SN class. 2. Overall setup - What is the data, model, task. 3. Plotting functions - 3D, contour. 4. Individual algorithms and how they perform. 5. Exercise. Do write your views in the comment section. Subscribe for more videos. Follow us on: Website: https://www.societyofai.in/ LinkedIn: https://www.linkedin.com/company/soci... Facebook: https://www.facebook.com/societyofai/ Twitter: https://twitter.com/societyofai

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions (Paper Explained)

#imle #backpropagation #discrete Backpropagation is the workhorse of deep learning, but unfortunately, it only works for continuous functions that are amenable to the chain rule of differentiation. Since discrete algorithms have no continuous derivative, deep networks with such algorithms as part of them cannot be effectively trained using backpropagation. This paper presents a method to incorporate a large class of algorithms, formulated as discrete exponential family distributions, into deep networks and derives gradient estimates that can easily be used in end-to-end backpropagation. This enables things like combinatorial optimizers to be part of a network's forward propagation natively. OUTLINE: 0:00 - Intro & Overview 4:25 - Sponsor: Weights & Biases 6:15 - Problem Setup & Contributions 8:50 - Recap: Straight-Through Estimator 13:25 - Encoding the discrete problem as an inner product 19:45 - From algorithm to distribution 23:15 - Substituting the gradient 26:50 - Defining a target distribution 38:30 - Approximating marginals via perturb-and-MAP 45:10 - Entire algorithm recap 56:45 - Github Page & Example Paper: https://ift.tt/3HZ19lG Code (TF): https://ift.tt/3p9iQq7 Code (Torch): https://ift.tt/3w85ai5 Our Discord: https://ift.tt/3dJpBrR Sponsor: Weights & Biases https://wandb.com Abstract: Combining discrete probability distributions and combinatorial optimization problems with neural network components has numerous applications but poses several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE), a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components. I-MLE is widely applicable as it only requires the ability to compute the most probable states and does not rely on smooth relaxations. The framework encompasses several approaches such as perturbation-based implicit differentiation and recent methods to differentiate through black-box combinatorial solvers. We introduce a novel class of noise distributions for approximating marginals via perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood estimation when used in some recently studied learning settings that involve combinatorial solvers. Experiments on several datasets suggest that I-MLE is competitive with and often outperforms existing approaches which rely on problem-specific relaxations. Authors: Mathias Niepert, Pasquale Minervini, Luca Franceschi Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Gradient Descent Algorithm (GD Algorithm) | Deep Learning Tutorials | Society of AI

Deep Learning Tutorials In this video we discussed about GD Algorithm: Gradient Descent is the most common optimization algorithm in machine learning and deep learning. It is a first-order optimization algorithm. In above video we talk about GD Algorithm and Vectorized GD Algorithm: 1. Modified SN class. 2. Overall setup - What is the data, model, task. 3. Plotting functions - 3D, contour. 4. Individual algorithms and how they perform. 5. Exercise. Do write your views in the comment section. Subscribe for more videos. Follow us on: Website: https://www.societyofai.in/ LinkedIn: https://www.linkedin.com/company/soci... Facebook: https://www.facebook.com/societyofai/ Twitter: https://twitter.com/societyofai

Man VS Machine: Who Plays Table Tennis Better? 🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis" is available here: https://ift.tt/3G8KM4r https://www.youtube.com/watch?v=SNnqtGLmX4Y ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Deep Learning for NLP - 17 (KAIST AI605 Fall 2021)

Course website: https://seominjoon.github.io/kaist-ai605/

Mitigating Overfitting and Underfitting with Dropout, Regularization - Full Stack Deep Learning

Overfitting and Underfitting are very common problems faced when training Deep Learning Models. In this tutorial, we shall see how to solve these problems via Data augmentation, Data collection, dropout, regularization, early stopping, less complex models, hyperparameter tuning, normalization. Previous (Introductory): https://youtu.be/GW3VadqOUnU Previous (Tensors and Variables): https://youtu.be/Kg2OgVHSH2o1 Previous (Linear Regression for Car Price Prediction): https://youtu.be/Y7DsfKyBF7g Previous (Convolutional Neural Networks for Malaria Diagnosis): https://youtu.be/MthqOrx_1Gk Previous (Loading and Saving TensorFlow Model to Gdrive): https://youtu.be/0U7IimAAC5Y Previous (Functional API, Model Subclassing and Custom Layers): https://youtu.be/wQz36Cmhe40 Previous (Performance Measurement): https://youtu.be/rqCzLNKJEvY Previous (Callbacks with TensorFlow 2): https://youtu.be/E_Ipd2LzTBw Colab Notebook: https://colab.research.google.com/drive/1uUH-asz3CFxlvld8uGx-m3crr4Iepp3u#scrollTo=ATnj3IWceW69 Check out our Deep Learning with TensorFlow 2 course (https://www.neuralearn.ai/course_page/3/). Check out our Deep Learning for Computer Vision with TensorFlow 2 course (https://www.neuralearn.ai/course_page/5/). Check out our Complete Linear Algebra Course https://www.neuralearn.ai/course_page/1/ Feel free to ask any questions. Always stay updated https://www.neuralearn.ai/subscribe/ Connect with us here: Twitter: https://twitter.com/neulearndotai Facebook: linkhttps://www.facebook.com/Neuralearnai-107372484374170/ LinkedIn https://www.linkedin.com/company/neuralearn

Peer Review is still BROKEN! The NeurIPS 2021 Review Experiment (results are in)

#neurips #peerreview #machinelearning A look at the results of the 2021 NeurIPS peer review experiment. https://ift.tt/3EFUTgS https://ift.tt/3xa2dhz Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Can We Simulate A Virtual Sponge? 🧽

❤️ Check out Weights & Biases and say hi in their community forum here: https://ift.tt/3DRsoMs 📝 The paper "Unified particle system for multiple-fluid flow and porous material" is available here: https://ift.tt/3HVlFDw https://ift.tt/3CPy0FB Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Parameter Prediction for Unseen Deep Architectures (w/ First Author Boris Knyazev)

#deeplearning #neuralarchitecturesearch #metalearning Deep Neural Networks are usually trained from a given parameter initialization using SGD until convergence at a local optimum. This paper goes a different route: Given a novel network architecture for a known dataset, can we predict the final network parameters without ever training them? The authors build a Graph-Hypernetwork and train on a novel dataset of various DNN-architectures to predict high-performing weights. The results show that not only can the GHN predict weights with non-trivial performance, but it can also generalize beyond the distribution of training architectures to predict weights for networks that are much larger, deeper, or wider than ever seen in training. OUTLINE: 0:00 - Intro & Overview 6:20 - DeepNets-1M Dataset 13:25 - How to train the Hypernetwork 17:30 - Recap on Graph Neural Networks 23:40 - Message Passing mirrors forward and backward propagation 25:20 - How to deal with different output shapes 28:45 - Differentiable Normalization 30:20 - Virtual Residual Edges 34:40 - Meta-Batching 37:00 - Experimental Results 42:00 - Fine-Tuning experiments 45:25 - Public reception of the paper ERRATA: - Boris' name is obviously Boris, not Bori - At 36:05, Boris mentions that they train the first variant, yet on closer examination, we decided it's more like the second Paper: https://ift.tt/3mhBKuz Code: https://ift.tt/3BjLTv6 Abstract: Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis. Authors: Boris Knyazev, Michal Drozdzal, Graham W. Taylor, Adriana Romero-Soriano Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Help Protect the Great Barrier Reef with Machine Learning

We are excited to announce a TensorFlow-sponsored Kaggle challenge to locate and identify harmful crown-of-thorns starfish (COTS), as part of a broader partnership between the Commonwealth Scientific and Industrial Research Organization (CSIRO) and Google, to help protect coral reefs everywhere. Join the challenge today at https://goo.gle/reef Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

PDF(Probability Density Function) | Probability and Statistics for Machine Learning

PDF(Probability Density Function) | Probability and Statistics for Machine Learning t is a series of videos about Probability and statistics with code. machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

Code walkthrough Percentile,Percentilerank,CDF, Quantiles,IQR,median|Statistics For Machine Learning

percentile, percentile rank, CDF, quantiles,IQR, median Probability and Statistics For Machine Learning It is a series of videos about Probability and statistics with code. machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

PDF(Probability Density Function) | Probability and Statistics for Machine Learning

PDF(Probability Density Function) | Probability and Statistics for Machine Learning t is a series of videos about Probability and statistics with code. machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

Learning Rate Grafting: Transferability of Optimizer Tuning (Machine Learning Research Paper Reivew)

#grafting #adam #sgd The last years in deep learning research have given rise to a plethora of different optimization algorithms, such as SGD, AdaGrad, Adam, LARS, LAMB, etc. which all claim to have their special peculiarities and advantages. In general, all algorithms modify two major things: The (implicit) learning rate schedule, and a correction to the gradient direction. This paper introduces grafting, which allows to transfer the induced learning rate schedule of one optimizer to another one. In that, the paper shows that much of the benefits of adaptive methods (e.g. Adam) are actually due to this schedule, and not necessarily to the gradient direction correction. Grafting allows for more fundamental research into differences and commonalities between optimizers, and a derived version of it makes it possible to computes static learning rate corrections for SGD, which potentially allows for large savings of GPU memory. OUTLINE 0:00 - Rant about Reviewer #2 6:25 - Intro & Overview 12:25 - Adaptive Optimization Methods 20:15 - Grafting Algorithm 26:45 - Experimental Results 31:35 - Static Transfer of Learning Rate Ratios 35:25 - Conclusion & Discussion Paper (OpenReview): https://ift.tt/30IA84C Old Paper (Arxiv): https://ift.tt/3HFueT2 Our Discord: https://ift.tt/3dJpBrR Abstract: In the empirical science of training large neural networks, the learning rate schedule is a notoriously challenging-to-tune hyperparameter, which can depend on all other properties (architecture, optimizer, batch size, dataset, regularization, ...) of the problem. In this work, we probe the entanglements between the optimizer and the learning rate schedule. We propose the technique of optimizer grafting, which allows for the transfer of the overall implicit step size schedule from a tuned optimizer to a new optimizer, preserving empirical performance. This provides a robust plug-and-play baseline for optimizer comparisons, leading to reductions to the computational cost of optimizer hyperparameter search. Using grafting, we discover a non-adaptive learning rate correction to SGD which allows it to train a BERT model to state-of-the-art performance. Besides providing a resource-saving tool for practitioners, the invariances discovered via grafting shed light on the successes and failure modes of optimizers in deep learning. Authors: Anonymous (Under Review) Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Sculpting Liquids in Virtual Reality! 🍷

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb 📝 The paper "Interactive Liquid Splash Modeling by User Sketches" is available here: https://ift.tt/3oMajZV https://ift.tt/3iOpn7u 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #vr

Intro to AI

Median,IQR(InterQuartile Range),Quantile | Probability and Statistics For Machine Learning

Probability and Statistics For Machine Learning It is a series of videos about Probability and statistics with code. machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

Code walkthrough Percentile,Percentilerank,CDF, Quantiles,IQR,median|Statistics For Machine Learning

percentile, percentile rank, CDF, quantiles,IQR, median Probability and Statistics For Machine Learning It is a series of videos about Probability and statistics with code. machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

Hello Ai Introduction to Machine Learning 2-1

This is the day two of the Hello Ai webinar with Edward pie

Outliers | Probability and Statistics For Machine Learning

Probability and Statistics For Machine Learning machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python

CDF(Cumulative Distribution Function) | Probability and Statistics For Machine Learning

Probability and Statistics For Machine Learning machinelearningprojectforbeginners mlproject fastapi serving machine learning model using fasapi how to save machine learning model end to end machine learning project how to do binary classification how to do multiclass classification how to save machine learning model how to deploy machine learning model how to use count vectorizer how to code naive Bayes naive Bayes logistic regression Bert transformer data science case study data science project data science case study for beginners data science project beginners #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognition python mnist data set mnist TensorFlow tutorial mnist data set neural network mnist dataset python mnist digit recognation python cnn mnist dataset tensorflow mnist classification using cnn mnist project mnist digit recognation tensorflow mnist dataset for handwrittem digits mnist datasetcolab mnist dataset in colab mnist dataset neural network mnist dataset explained Handwritten digit recognation on MNIST data tensorflow tutorial tensorflow object detection tensorflow js tensorflow tutorial for beginners tensorflow tutorial in hindi tensorflow python tensorflow projects tensorflow installation tensorflow lite android tutorial tensorflow lite tensorflow object detection api tutorial tensorflow in hindi tensorflow js tutorial tensorflow certification keras tutorial keras tutorial for beginners keras vs tensorflow keras tuner tensorflow keras tutorial tensorflow keras image classification tensorflow keras install on windows tensorflow keras regression tensorflow keras object detection tutorial tensorflow keras example objectdetection tensorflow keras cnn tensorflow keras gpu tensorflow keras tutorials for beginners tensorflow keras example tensorflow keras install anaconda tensorflow keras rnn recurrent neural network in hindi recurrent neural network tutorial recurrent neural network python data science for beginners data science course data science full course data science interview question data science tutorial data science project data science in hindi data science interview data science python data science projects for beginners data science roadmap data science for beginners in hindi machine learning tutorial machine learning projects machine learning full course machine learning interview question machine learning in hindi machine learning roadmap machine learning projects in python machine learning tutorial in hindi deep learning tutorial deep learning ai deep learning python deep learning projects deep learning in hindi deep learning full course deep learning tutorial in hindi natural language processing in artifitial intelligence natural language processing python natural language processing tutorial natural language processing full course natural language processing projects natural language processing tutorial in hindi natural language processing course natural language processing in artifitial intelligence in hindi natural language processing in python NLP technique NLP training videos NLP techniques in hindi NLP in artifitial intelligence NLP projects in python NLP tutorial NLP course artificial intelligence tutorial artificial intelligence course artificial intelligence in hindi artificial intelligence robot and machine learning artificial intelligence tutorials in hindi artificial intelligence full course data scintist career data scintist course data scintist salary in india data scintist interview data scintist tutorial data science job salary in india data scintist job opportunites in india data scintist job role data scintist job opportunity data scintist job profile data scintist job interview data scintist job interview questions data scintist job for fresher data scintist job guarentee data scintist job description data analytic exel data analytic hindi data analytic lifecycle data analytic interview question data analytic project data analytic vs data science data analytic with python nptel assignment data analytic with python