Artificial Intelligence Full Course | Artificial Intelligence Tutorial For Beginners | BookEx |

Introduction To Machine Learning https://youtu.be/yOOWWPeEKvs What is Artificial Intelligence? https://youtu.be/KmSq57W-Kdw Subscribe To Our YT Channel: https://www.youtube.com/channel/UC1JTW0VNu_5S_5V0ArsKgwg Learn Complete Artificial Intelligence Course: https://youtu.be/KmSq57W-Kdw Basic Structure Of C Programming Language: https://youtu.be/hXzaKOUpRKo Introduction to Data Structures and Algorithms in Hindi In 10 Min. https://youtu.be/0B4Uv60K8QA Basics of Object Oriented Programming Language (OOP's) in 10 min in Hindi: https://youtu.be/aYkGEiPKKhY Learn Data Base Management System : https://youtu.be/Jc6uq4zvCZc Learn "How To Make an Login Form" : https://youtu.be/BvOVX4iGHVA Relationship Between Artificial Intelligence, Machine Learning & Deep Learning. #ArtificialIntelligenceCourse. #AICourse #AIIntroduction #AIVsMLVsDL, #AI, #ML, #DL Complete Artificial Intelligence Course Introduction | A.I Course | Artificial Intelligence Vs Machine Learning Vs Deep Learning l AI vs ML vs DL. Difference Between A.I. and M. L. & D. L. Deep Learning Vs Machine Learning | AI Vs Machine Learning Vs Deep Learning AI VS ML VS DL VS Data Science In This Tutorial You Will Learn Complete Artificial Intelligence Course, Subscribe BookEx YT Channel To Learn More About Trending Technology

OpenAI Outperforms Some Humans In Article Summarization! 📜

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3foLj89 📝 The paper "Learning to Summarize with Human Feedback" is available here: https://ift.tt/2QTPoUr Reddit links to the showcased posts: 1. https://ift.tt/39uoHiB 2. https://ift.tt/3m2fSSk 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Thumbnail background image credit: https://ift.tt/3rvywTK Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Machine Learning PhD Survival Guide 2021 | Advice on Topic Selection, Papers, Conferences & more!

#machinelearning #phd #howto This video is advice for new PhD students in the field of Machine Learning in 2021 and after. The field has shifted dramatically in the last few years and navigating grad school can be very hard, especially when you're as clueless as I was when I started. The video is a personal recount of my mistakes and what I've learned from them. If you already have several published papers and know what to do, this video is not for you. However, if you are not even sure where to start, how to select a topic, or what goes in a paper, you might benefit from this video, because that's exactly how I felt. Main Takeaways: - Select niche topics rather than hype topics - Write papers that can't be rejected - Don't be discouraged by bad reviews - Take reviewing & teaching seriously - Keep up your focus - Conferences are for networking - Internships are great opportunities - Team up with complementary skills - Don't work too hard OUTLINE: 0:00 - Intro & Overview 1:25 - Thesis Topic Selection 4:25 - How To Publish Papers 5:35 - Dealing With Reviewers 6:30 - How To Be A Reviewer 7:40 - Take Teaching Seriously 8:30 - Maintain Focus 10:20 - Navigating Conferences 12:40 - Internships 13:40 - Collaborations 14:55 - Don't Forget To Enjoy Transcript: https://ift.tt/3u4nqGY Credits to Lanz for editing 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

Tweet Success Predictor (Part 1)

Michelle has created a Tweet Success predictor. This program helps to predict probability of success of a tweet. Well done Michelle! #m2mtechbytes #m2msteamchallengeseason2 #coding #designthinking #100DaysOfCode #GirlsWhoCode #python #deeplearning #ai #machinelearning

DeepMind’s AI Watches YouTube and Learns To Play! ▶️🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3ruAL9V 📝 The paper "Playing hard exploration games by watching YouTube" is available here: Paper: https://ift.tt/2rnrsMo Gameplay videos: https://www.youtube.com/playlist?list=PLZuOGGtntKlaOoq_8wk5aKgE_u_Qcpqhu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

NLP and Knowledge Mining Workloads - Azure AI Fundamentals tutorial

In this video from LearnKey's Azure AI Fundamentals (AI-900) course, expert Jason Manibog demonstrates machine learning scenarios.

Deep Learning|Understanding Sigmoid and tanh Activation Function part1 | #DeepLearning#Sigmoid#tanh

Hello In this video , i will explain Sigmoid and tanh function perceptron : https://youtu.be/wXntfwJzk34 Deep learning series : https://www.youtube.com/watch?v=uqSXG15IwF0&list=PLaZbQfJNDlPDbhHO5JaA-3TvvipMZEKVu refer : https://en.wikipedia.org/wiki/Sigmoid_function #datasciencetutorial #machinelearning #datascience #deeplearnig #activationfunction #sigmoid #tanh #differentiationofsigmoid #differentiationoftanh thank you #tensorflow #ailearning #datasciencetutorial #machinelearningtutorial #neuralnetwork #datascince​ #datascincetutorial​ #machinelearning​ #machinelearningtutorial​ #deeplearning​ #multilayerperceptron​ #attentionlayer​ #transformer​ #bert​ #tensorflow​ #keras​ #deeplearninginhindi Neural Network Deep learning Machine learning mnistdigit recognation 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

TechnicalTopicTuesday-39 #ai #ml #artificialintelligence #Machinelearning #kids #education

@Passion, People & Purpose #ai #ml #artificialintelligence #Machinelearning #kids #education Until we meet, happy leading and let's lead together. Stay safe. Bye for now. Find me on - YoutTube - @Passion, People & Purpose Twitter - https://twitter.com/vaishalilambe LinkedIn - https://www.linkedin.com/in/vaishali-lambe/ Instagram - @PassionPeoplePurpose Website - https://www.vaishalilambe.com/soleadsaturday Facebook - https://www.facebook.com/vaishalilambe17 Apple Podcasts - https://podcasts.apple.com/us/podcast/soleadsaturday/id1496626534?uo=4 Google Podcasts - https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8xMzFiYTA0MC9wb2RjYXN0L3Jzcw== Spotify - https://open.spotify.com/show/0bFOIm9EGFalhPG8YPBhVp

Is Google Translate Sexist? Gender Stereotypes in Statistical Machine Translation

#genderbias #algorithmicfairness #debiasing A brief look into gender stereotypes in Google Translate. The origin is a Tweet containing a Hungarian text. Hungarian is a gender-neutral language, so translating gender pronouns is ambiguous. Turns out that Google Translate assigns very stereotypical pronouns. In this video, we'll have a look at the origins and possible solutions to this problem. OUTLINE: 0:00 - Intro 1:10 - Digging Deeper 2:30 - How does Machine Translation work? 3:50 - Training Data Problems 4:40 - Learning Algorithm Problems 5:45 - Argmax Output Problems 6:45 - Pragmatics 7:50 - More on Google Translate 9:40 - Social Engineering 11:15 - Conclusion Songs: Like That - Anno Domini Beats Submarine - Dyalla Dude - Patrick Patrikios 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

An AI Made This Dog Photo - But How? 🐶

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3ceWoqh 📝 The paper "Training Generative Adversarial Networks with Limited Data" is available here: Paper: https://ift.tt/3e8wmU8 Pytorch implementation: https://ift.tt/3oKpHEk 📝 My thesis with the quote is available here: https://ift.tt/3ff8Xnc Unofficial StyleGAN2-ADA trained on corgis (+ colab notebook): https://ift.tt/3f5IZ5N 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb 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

Perceiver: General Perception with Iterative Attention (Google DeepMind Research Paper Explained)

#perceiver #deepmind #transformer Inspired by the fact that biological creatures attend to multiple modalities at the same time, DeepMind releases its new Perceiver model. Based on the Transformer architecture, the Perceiver makes no assumptions on the modality of the input data and also solves the long-standing quadratic bottleneck problem. This is achieved by having a latent low-dimensional Transformer, where the input data is fed multiple times via cross-attention. The Perceiver's weights can also be shared across layers, making it very similar to an RNN. Perceivers achieve competitive performance on ImageNet and state-of-the-art on other modalities, all while making no architectural adjustments to input data. OUTLINE: 0:00 - Intro & Overview 2:20 - Built-In assumptions of Computer Vision Models 5:10 - The Quadratic Bottleneck of Transformers 8:00 - Cross-Attention in Transformers 10:45 - The Perceiver Model Architecture & Learned Queries 20:05 - Positional Encodings via Fourier Features 23:25 - Experimental Results & Attention Maps 29:05 - Comments & Conclusion Paper: https://ift.tt/3sQm8yO My Video on Transformers (Attention is All You Need): https://youtu.be/iDulhoQ2pro Abstract: Biological systems understand the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models. These priors introduce helpful inductive biases, but also lock models to individual modalities. In this paper we introduce the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets. The model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. We show that this architecture performs competitively or beyond strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video and video+audio. The Perceiver obtains performance comparable to ResNet-50 on ImageNet without convolutions and by directly attending to 50,000 pixels. It also surpasses state-of-the-art results for all modalities in AudioSet. Authors: Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, Joao Carreira 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

National Webinar on "Research Trends in AI"

Training in AI/ML for Atmosphere Ocean Applications for Beginners 16-02-2021 Afternoon Day-2

Training in AI/ML for Atmosphere Ocean Applications for Beginners 16-02-2021 Afternoon Day-2 Hands on Session: Dr. Deepesh Jain

Deep Learning project - Learn Deep Learning part 1 tutorial | introduction of deep learning

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3.4. Seaborn Tutorial in Python | Machine Learning Course

This video is about detailed tutorial on Seaborn Library in Python. Hands-on Data Science Course: https://skl.sh/37HCLFj Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Let's build a Community of Machine Learning experts! Kindly Subscribe here👉 https://tinyurl.com/md0gjbis I am making a "Hands-on Machine Learning Course with Python" in YouTube. I'll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening). Make donations💵 to support the channel. Any contribution is appreciated! Thank you. Indian UPI id: siddhardhselvam2317@oksbi Not from India? Paypal id: siddhardhselvam2317@gmail.com Colab File Link: https://colab.research.google.com/drive/1_unR_OcDLzO38kQBjoFJdviMSO8x-gQU?usp=sharing Download the Course Curriculum File from here: https://drive.google.com/file/d/17i0c6SmncNuwSgr9W1MRRk3YYdEOP9Gd/view?usp=sharing LinkedIn: https://www.linkedin.com/in/siddhardhan-s-741652207 Telegram Group: https://t.me/siddhardhan Facebook group: https://www.facebook.com/groups/490857825649006/?ref=share

NVIDIA’s AI Puts Video Calls On Steroids! 💪

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3lt8Dm8 📝 The paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing" is available here: https://ift.tt/3fRvzbY 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/3lEgBsM Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Deep Learning project - Learn Deep Learning part 1 tutorial | introduction of deep learning

#deeplearning deep learning tutorial, deep learning ai, deep learning nptel, deep learning projects, deep learning krish naik, deep learning in hindi, deep learning in tamil, deep learning python, deep learning andrew ng, deep learning algorithms, deep learning and machine learning, deep learning applications, deep learning assignment, deep learning architecture, deep learning and neural networks, deep learning biology, deep learning basics, deep learning book, deep learning by andrew ng, deep learning by krish naik, deep learning book review, deep learning basics with python, deep learning by edureka, deep learning course, deep learning coursera, deep learning channel, deep learning cnn, deep learning crash course, deep learning code with harry, deep learning computer vision, deep learning coursera quiz answers, c deep learning framework, c language deep learning, c programming deep learning, deep learning c'est quoi, deep learning definition, deep learning deeplizard, deep learning documentary, deep learning deployment, deep learning django, deep learning drone, deep learning desktop, deep learning demystified, deep learning edureka, deep learning explained, deep learning end to end project, deep learning engineer, deep learning example, deep learning english, deep learning engineer salary, deep learning elon musk, machine learning e deep learning, deep learning full course, deep learning for computer vision, deep learning freecodecamp, deep learning for computer vision nptel, deep learning for beginners, deep learning full course in hindi, deep learning fundamentals, deep learning from scratch, deep learning great learning, deep learning gpu, deep learning game, deep learning google, deep learning geoffrey hinton, deep learning gan, deep learning gpu setup, deep learning goodfellow, deep learning hindi, deep learning harvard, deep learning hardware, deep learning history, deep learning hinton, deep learning hands on, deep learning heroes, deep learning healthcare, deep learning interview questions, deep learning in artificial intelligence, deep learning iit madras, deep learning in telugu, deep learning in python, deep learning introduction, deep learning jobs, deep learning java, deep learning javascript, deep learning jupyter notebook, deep learning js, deep learning java tutorial, deep learning jetson nano, deep learning jeff hinton, deep learning kya hai, deep learning keras, deep learning keras tutorial, deep learning kaggle, deep learning kelly howell, deep learning keywords, deep learning khan academy, deep learning p k biswas, deep learning laptop, deep learning lectures, deep learning laptop 2020, deep learning lex fridman, deep learning layers, deep learning latest, deep learning lecture series, deep learning layers explained, deep learning mit, deep learning music, deep learning machine, deep learning mathematics, deep learning model, deep learning malayalam, deep learning matlab, deep learning meaning, deep learning nptel assignment, deep learning nptel iit ropar, deep learning for beginners, deep learning neural networks, deep learning nptel assignment 3, deep learning nlp, deep learning nptel answers, deep learning nptel assignment 4, deep learning object detection, deep learning opencv, deep learning on macbook pro m1, deep learning opencv python, deep learning overview, deep learning on cloud, deep learning on mac, deep learning oxford, o que é deep learning, machine learning o deep learning, deep learning playlist, deep learning projects in python, deep learning pc, deep learning pc build, deep learning projects for beginners, deep learning point, deep learning questions, deep learning quantization, deep learning que es, deep learning quiz, deep learning quora, deep learning question paper, deep learning quiz questions and answers, deep learning questions for interview, deep q learning tutorial, deep q learning pytorch, deep q learning python, deep q learning in hindi, double deep q learning, deep q learning explained, deep q learning keras, experience replay deep q learning, deep learning roadmap, deep learning research, deep learning research papers, deep learning raspberry pi, deep learning robot, deep learning research ideas, deep learning real world applications, deep learning raspberry pi 4, r deep learning packages, r deep learning cookbook, r deep learning projects, r deep learning essentials, r deep learning tutorial, r deep learning example, deep learning using r, r cnn deep learning, deep learning stanford, deep learning simplilearn, deep learning specialization coursera, deep learning seeken, deep learning sentdex, deep learning setup, deep learning statquest, deep learning stanford university, deep learning tutorial in hindi, deep learning tamil, deep learning tutorial for beginners, Deep Learning Full Course - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Edureka

3.4. Seaborn Tutorial in Python | Machine Learning Course

This video is about detailed tutorial on Seaborn Library in Python. Hands-on Data Science Course: https://skl.sh/37HCLFj Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Let's build a Community of Machine Learning experts! Kindly Subscribe here👉 https://tinyurl.com/md0gjbis I am making a "Hands-on Machine Learning Course with Python" in YouTube. I'll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening). Colab File Link: https://colab.research.google.com/drive/1_unR_OcDLzO38kQBjoFJdviMSO8x-gQU?usp=sharing Download the Course Curriculum File from here: https://drive.google.com/file/d/17i0c6SmncNuwSgr9W1MRRk3YYdEOP9Gd/view?usp=sharing LinkedIn: https://www.linkedin.com/in/siddhardhan-s-741652207 Telegram Group: https://t.me/siddhardhan Facebook group: https://www.facebook.com/groups/490857825649006/?ref=share

Pretrained Transformers as Universal Computation Engines (Machine Learning Research Paper Explained)

#universalcomputation #pretrainedtransformers #finetuning Large-scale pre-training and subsequent fine-tuning is a common recipe for success with transformer models in machine learning. However, most such transfer learning is done when a model is pre-trained on the same or a very similar modality to the final task to be solved. This paper demonstrates that transformers can be fine-tuned to completely different modalities, such as from language to vision. Moreover, they demonstrate that this can be done by freezing all attention layers, tuning less than .1% of all parameters. The paper further claims that language modeling is a superior pre-training task for such cross-domain transfer. The paper goes through various ablation studies to make its point. OUTLINE: 0:00 - Intro & Overview 2:00 - Frozen Pretrained Transformers 4:50 - Evaluated Tasks 10:05 - The Importance of Training LayerNorm 17:10 - Modality Transfer 25:10 - Network Architecture Ablation 26:10 - Evaluation of the Attention Mask 27:20 - Are FPTs Overfitting or Underfitting? 28:20 - Model Size Ablation 28:50 - Is Initialization All You Need? 31:40 - Full Model Training Overfits 32:15 - Again the Importance of Training LayerNorm 33:10 - Conclusions & Comments Paper: https://ift.tt/3ciOjj3 Code: https://ift.tt/3qUlKxy Abstract: We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction. In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language improves performance and compute efficiency on non-language downstream tasks. In particular, we find that such pretraining enables FPT to generalize in zero-shot to these modalities, matching the performance of a transformer fully trained on these tasks. Authors: Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

All Hail The Adaptive Staggered Grid! 🌐🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "An adaptive staggered-tilted grid for incompressible flow simulation" is available here: https://ift.tt/3vtrySm https://ift.tt/38Lf4Mj ❤️ 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, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

3 New Things An AI Can Do With Your Photos!

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3rJDUDL 📝 The paper "GANSpace: Discovering Interpretable GAN Controls" is available here: https://ift.tt/3vkNqz8 📝 Our material synthesis paper is available here: https://ift.tt/2HhNzx5 📝 The font manifold paper is available here: https://ift.tt/1qlPMYt 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Thumbnail background image credit: https://ift.tt/3rJ3Fnu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

HYBRID AI tutorials: Deep Learning frameworks

This tutorial explores the available platforms and frameworks for Deep Learning and Machine learning in Medical imaging. Along with the introduction to DL frameworks, we provide an example project which makes use of the recommended tools and includes many good practice tips. Presented by: Zacharias Chalampalakis and Laura Dal Toso This video is part of a tutorial series on AI in medical imaging, provided by the MSCA ITN HYBRID. For more information about our project, visit https://www.hybrid2020.eu/home.html. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764458. The result only reflects the author‘s view and the EC/REA is not responsible for any use that may be made of the information it contains.

Yann LeCun - Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)

#selfsupervisedlearning #yannlecun #facebookai Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer knowledge faster than current systems. They suggest as a promising direction to build non-contrastive latent-variable predictive models, like VAEs, but ones that also provide high-quality latent representations for downstream tasks. OUTLINE: 0:00 - Intro & Overview 1:15 - Supervised Learning, Self-Supervised Learning, and Common Sense 7:35 - Predicting Hidden Parts from Observed Parts 17:50 - Self-Supervised Learning for Language vs Vision 26:50 - Energy-Based Models 30:15 - Joint-Embedding Models 35:45 - Contrastive Methods 43:45 - Latent-Variable Predictive Models and GANs 55:00 - Summary & Conclusion Paper (Blog Post): https://ift.tt/3uUfWYl My Video on BYOL: https://www.youtube.com/watch?v=YPfUiOMYOEE Video approved by Antonio. Abstract: We believe that self-supervised learning (SSL) is one of the most promising ways to build such background knowledge and approximate a form of common sense in AI systems. Authors: Yann LeCun, Ishan Misra 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

Microsoft Azure AI Fundamentals AI 900 Tutorial

#Azure #ArtificialIntelligence Free Practice Tests on Microsoft Azure AI Fundamentals Exam: https://www.testpreptraining.com/microsoft-azure-ai-fundamentals-ai-900-free-practice-test Complete Tutorial & Study Guide on Microsoft Azure AI Fundamentals Exam: https://www.testpreptraining.com/tutorial/exam-ai-900-microsoft-azure-ai-fundamentals/

Python Reinforcement Learning Tutorial for Beginners in 25 Minutes

Want to break into Reinforcement Learning with Python? Just not too sure where or how to start? Well in this video you’ll learn the basics of creating an OpenAI gym environment in Python and training a reinforcement learning algorithm to solve the Lunar Lander problem. You’ll be able to leverage the stable_baselines algorithms to quickly and effectively train a deep reinforcement learning model in Python, the same pattern can be used over and again to train and solve multiple reinforcement learning problems. In this video, you'll learn : 1. Installing Stable Baselines for Reinforcement Learning with Python 2. Training a Reinforcement Learning model using the ACER Algorithm 3. Running and Evaluating a Stable Baselines RL Model on LunarLander-v2 Get the code: https://github.com/nicknochnack/StableBaselinesRL Reinforcement Learning Crash Course: https://youtu.be/cO5g5qLrLSo Chapters: 0:00 - Start 1:55 - Reinforcement Learning Flow 4:09 - Installing Python Dependencies 6:10 - Importing RL Dependencies including stable_baselines 9:11 - Testing the LunarLander-v2 Environment 11:47 - Training an ACER Reinforcement Learning Model 17:52 - Evaluating the Model 20:30 - Saving and Reloading RL Model Weights Oh, and don't forget to connect with me! LinkedIn: https://www.linkedin.com/in/nicholasrenotte Facebook: https://www.facebook.com/nickrenotte/ GitHub: https://github.com/nicknochnack Patreon: https://www.patreon.com/nicholasrenotte Join the Discussion on Discord: https://discord.gg/mtTTwYkB29 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!

HYBRID AI tutorials: Deep Learning frameworks

This tutorial explores the available platforms and frameworks for Deep Learning and Machine learning in Medical imaging. Along with the introduction to DL frameworks, we provide an example project which makes use of the recommended tools and includes many good practice tips. Presented by: Zacharias Chalampalakis and Laura Dal Toso This video is part of a tutorial series on AI in medical imaging, provided by the MSCA ITN HYBRID. For more information about our project, visit https://www.hybrid2020.eu/home.html. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764458. The result only reflects the author‘s view and the EC/REA is not responsible for any use that may be made of the information it contains.

Microsoft Azure AI Fundamentals AI 900 Tutorial

#Azure #ArtificialIntelligence Free Practice Tests on Microsoft Azure AI Fundamentals Exam: https://www.testpreptraining.com/microsoft-azure-ai-fundamentals-ai-900-free-practice-test Complete Tutorial & Study Guide on Microsoft Azure AI Fundamentals Exam: https://www.testpreptraining.com/tutorial/exam-ai-900-microsoft-azure-ai-fundamentals/

Python Reinforcement Learning Tutorial for Beginners in 25 Minutes

Want to break into Reinforcement Learning with Python? Just not too sure where or how to start? Well in this video you’ll learn the basics of creating an OpenAI gym environment in Python and training a reinforcement learning algorithm to solve the Lunar Lander problem. You’ll be able to leverage the stable_baselines algorithms to quickly and effectively train a deep reinforcement learning model in Python, the same pattern can be used over and again to train and solve multiple reinforcement learning problems. In this video, you'll learn : 1. Installing Stable Baselines for Reinforcement Learning with Python 2. Training a Reinforcement Learning model using the ACER Algorithm 3. Running and Evaluating a Stable Baselines RL Model on LunarLander-v2 Get the code: https://github.com/nicknochnack/StableBaselinesRL Reinforcement Learning Crash Course: https://youtu.be/cO5g5qLrLSo Chapters: 0:00 - Start 1:55 - Reinforcement Learning Flow 4:09 - Installing Python Dependencies 6:10 - Importing RL Dependencies including stable_baselines 9:11 - Testing the LunarLander-v2 Environment 11:47 - Training an ACER Reinforcement Learning Model 17:52 - Evaluating the Model 20:30 - Saving and Reloading RL Model Weights Oh, and don't forget to connect with me! LinkedIn: https://www.linkedin.com/in/nicholasrenotte Facebook: https://www.facebook.com/nickrenotte/ GitHub: https://github.com/nicknochnack Patreon: https://www.patreon.com/nicholasrenotte Join the Discussion on Discord: https://discord.gg/mtTTwYkB29 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!

5 Crazy Simulations That Were Previously Impossible! ⛓

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3l0yG3Q 📝 The paper "Incremental Potential Contact: Intersection- and Inversion-free Large Deformation Dynamics" is available here: https://ift.tt/3l0nmoh 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Apple or iPod??? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model! #Shorts

#Shorts #shorts #openai In the paper Multimodal Neurons in Artificial Neural Networks OpenAI suggests that CLIP can be attacked adversarially by putting textual labels onto pictures. They demonstrated this with an apple labeled as an iPod. I reproduce that experiment and suggest a simple, but effective fix. Yes, this is a joke ;) Original Video: https://youtu.be/Z_kWZpgEZ7w OpenAI does a huge investigation into the inner workings of their recent CLIP model via faceted feature visualization and finds amazing things: Some neurons in the last layer respond to distinct concepts across multiple modalities, meaning they fire for photographs, drawings, and signs depicting the same concept, even when the images are vastly distinct. Through manual examination, they identify and investigate neurons corresponding to persons, geographical regions, religions, emotions, and much more. In this video, I go through the publication and then I present my own findings from digging around in the OpenAI Microscope. Paper: https://ift.tt/3sTizHR My Findings: https://ift.tt/3sVJ5jR My Video on CLIP: https://youtu.be/T9XSU0pKX2E My Video on Feature Visualizations & The OpenAI Microscope: https://youtu.be/Ok44otx90D4 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

This Magnetic Simulation Took Nearly A Month! 🧲

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "A Level-Set Method for Magnetic Substance Simulation" is available here: https://ift.tt/3qoPanf https://starryuniv.cn/ https://ift.tt/3qjXvca https://ift.tt/3kNIYUG Some links may be down, trying to add several of them to make sure you find one that works! ❤️ 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, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Multimodal Neurons in Artificial Neural Networks (w/ OpenAI Microscope, Research Paper Explained)

#openai #clip #microscope OpenAI does a huge investigation into the inner workings of their recent CLIP model via faceted feature visualization and finds amazing things: Some neurons in the last layer respond to distinct concepts across multiple modalities, meaning they fire for photographs, drawings, and signs depicting the same concept, even when the images are vastly distinct. Through manual examination, they identify and investigate neurons corresponding to persons, geographical regions, religions, emotions, and much more. In this video, I go through the publication and then I present my own findings from digging around in the OpenAI Microscope. OUTLINE: 0:00 - Intro & Overview 3:35 - OpenAI Microscope 7:10 - Categories of found neurons 11:10 - Person Neurons 13:00 - Donald Trump Neuron 17:15 - Emotion Neurons 22:45 - Region Neurons 26:40 - Sparse Mixture of Emotions 28:05 - Emotion Atlas 29:45 - Adversarial Typographic Attacks 31:55 - Stroop Test 33:10 - My Findings in OpenAI Microscope 33:30 - Superman Neuron 33:50 - Resting B*tchface Neuron 34:10 - Trash Bag Neuron 35:25 - God Weightlifting Neuron 36:40 - Organ Neuron 38:35 - Film Spool Neuron 39:05 - Feather Neuron 39:20 - Spartan Neuron 40:25 - Letter E Neuron 40:35 - Cleanin Neuron 40:45 - Frown Neuron 40:55 - Lion Neuron 41:05 - Fashion Model Neuron 41:20 - Baseball Neuron 41:50 - Bride Neuron 42:00 - Navy Neuron 42:30 - Hemp Neuron 43:25 - Staircase Neuron 43:45 - Disney Neuron 44:15 - Hillary Clinton Neuron 44:50 - God Neuron 45:15 - Blurry Neuron 45:35 - Arrow Neuron 45:55 - Trophy Presentation Neuron 46:10 - Receding Hairline Neuron 46:30 - Traffic Neuron 46:40 - Raised Hand Neuron 46:50 - Google Maps Neuron 47:15 - Nervous Smile Neuron 47:30 - Elvis Neuron 47:55 - The Flash Neuron 48:05 - Beard Neuron 48:15 - Kilt Neuron 48:25 - Rainy Neuron 48:35 - Electricity Neuron 48:50 - Droplets Neuron 49:00 - Escape Neuron 49:25 - King Neuron 49:35 - Country Neuron 49:45 - Overweight Men Neuron 49:55 - Wedding 50:05 - Australia Neuron 50:15 - Yawn Neuron 50:30 - Bees & Simpsons Neuron 50:40 - Mussles Neuron 50:50 - Spice Neuron 51:00 - Conclusion Paper: https://ift.tt/3sTizHR My Findings: https://ift.tt/3sVJ5jR My Video on CLIP: https://youtu.be/T9XSU0pKX2E My Video on Feature Visualizations & The OpenAI Microscope: https://youtu.be/Ok44otx90D4 Abstract: In 2005, a letter published in Nature described human neurons responding to specific people, such as Jennifer Aniston or Halle Berry. The exciting thing wasn’t just that they selected for particular people, but that they did so regardless of whether they were shown photographs, drawings, or even images of the person’s name. The neurons were multimodal. As the lead author would put it: "You are looking at the far end of the transformation from metric, visual shapes to conceptual... information." We report the existence of similar multimodal neurons in artificial neural networks. This includes neurons selecting for prominent public figures or fictional characters, such as Lady Gaga or Spiderman. Like the biological multimodal neurons, these artificial neurons respond to the same subject in photographs, drawings, and images of their name. Authors: Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, Chris Olah 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 Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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

Intro to Deep Learning (ML Intensive at X)

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series at X we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone! Chapters: 0:00 - Intro and outline 1:42 - TensorFlow.js demos + discussion 3:58 - AI vs ML vs DL 7:55 - What’s representation learning? 8:40 - A cartoon neural network (more on this later) 9:20 - What features does a network see? 10:47 - The “deep” in “deep learning” 12:48 - Why tree-based models are still important 13:38 - How your workflow changes with DL 14:02 - A couple illustrative code examples 17:59 - What’s a hyperparameter? 19:44 - The skills that are important in ML 20:48 - An example of applied work in healthcare 21:58 - Families of neural networks + applications 28:55 - Encoder-decoders + more on representation learning 32:45 - Families of neural networks continued 35:50 - Are neural networks opaque? 38:29 - Building up from a neuron to a neural network 49:11 - A demo of representation learning in TF Playground 53:24 - Importance of activation functions 54:36 - What’s a neural network library? 58:43 - Overfitting and underfitting 1:02:38 - Autoencoders (and anomaly detection) screencast and demo 1:12:13 - Book recommendations Here are three helpful classes you can check out to learn more: Intro to Deep Learning from MIT → http://goo.gle/3sPj8To MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54 Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I And here are all the links to demos and code from the video, in the order they appeared: Face and hand tracking demos → http://goo.gle/2WTCwSc Teachable machine demo → https://goo.gle/3bSCzCi What features does a network see? → http://goo.gle/3e2zpA5 DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5 Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7 Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb Audio recognition tutorial → http://goo.gle/3kFpl1j Transfer learning tutorial → http://goo.gle/3bV7D60 RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7 RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9 Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme Image captioning tutorial → http://goo.gle/3sKFNQz Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq TensorFlow Playground demos → http://goo.gle/2Px6rhB Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo Basic image classification tutorial → http://goo.gle/3c2AF3o Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv Keras early stopping callback → http://goo.gle/308XQUj Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4 Deep Learning book → http://goo.gle/3c2VQmd Find Josh on Twitter → https://goo.gle/308Ve8P Subscribe to TensorFlow → https://goo.gle/TensorFlow

L : 8 Type's Of Machine Learning | Machine Learning Tutorial | Artificial Intelligence |

Type's Of Machine Learning | Machine Learning Tutorial | Artificial Intelligence #Type_Of_Machine_Learning, #ML #AI Lecture 1 : What is Artificial Intelligence? https://youtu.be/KmSq57W-Kdw Subscribe To Our Channel: https://www.youtube.com/channel/UC1JT... Learn Artificial Intelligence: https://youtu.be/KmSq57W-Kdw Basic Structure Of C Program: https://youtu.be/hXzaKOUpRKo introduction to data structures and algorithms in Hindi In 10 Min. https://youtu.be/0B4Uv60K8QA Basics of object oriented programming language (oop's) in 10 min in Hindi: https://youtu.be/aYkGEiPKKhY Learn Data Base Management System : https://youtu.be/Jc6uq4zvCZc Learn "How To Make an Login Form" : https://youtu.be/BvOVX4iGHVA Different Type's Of Machine Learning In This Tutorial You Will Learn Different Type's Of Machine Learning, Subscribe BookEx YT Channel To Learn More About Trending Technology

L : 8 Type's Of Machine Learning | Machine Learning Tutorial | Artificial Intelligence |

Type's Of Machine Learning | Machine Learning Tutorial | Artificial Intelligence #Type_Of_Machine_Learning, #ML #AI Lecture 1 : What is Artificial Intelligence? https://youtu.be/KmSq57W-Kdw Subscribe To Our Channel: https://www.youtube.com/channel/UC1JT... Learn Artificial Intelligence: https://youtu.be/KmSq57W-Kdw Basic Structure Of C Program: https://youtu.be/hXzaKOUpRKo introduction to data structures and algorithms in Hindi In 10 Min. https://youtu.be/0B4Uv60K8QA Basics of object oriented programming language (oop's) in 10 min in Hindi: https://youtu.be/aYkGEiPKKhY Learn Data Base Management System : https://youtu.be/Jc6uq4zvCZc Learn "How To Make an Login Form" : https://youtu.be/BvOVX4iGHVA Different Type's Of Machine Learning In This Tutorial You Will Learn Different Type's Of Machine Learning, Subscribe BookEx YT Channel To Learn More About Trending Technology

Differentiable Material Synthesis Is Amazing! ☀️

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "MATch: Differentiable Material Graphs for Procedural Material Capture" is available here: https://ift.tt/3kBBy6W 📝 Our Photorealistic Material Editing paper is available here: https://ift.tt/2EytbF6 ☀️ The free course on writing light simulations is available here: https://ift.tt/2rdtvDu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image: https://ift.tt/307bQOu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m