Free artificial intelligence and machine learning video tutorial resource: March 2022

Thursday, March 31, 2022

Y10 Computer Science 30th March P19 AI, Robotics and Machine Learning

This is for students who are isolating or working from home.

Wednesday, March 30, 2022

This AI Creates Beautiful Light Simulations! 🔆

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/hcKP9gY 📝 The paper "Neural Radiosity" is available here: https://ift.tt/FESf9Ze 🔆 The free light transport course is available here. You'll love it! https://ift.tt/wZhU0Qj 📝 The neural rendering paper "Gaussian Material Synthesis" is available here: https://ift.tt/yo2tGwn ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/tSfPeOd - 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, Jonathan, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Paul F, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, 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/tSfPeOd Thumbnail background image credit: https://ift.tt/hgam1Ro Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu The thumbnail is used as illustration. Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/JnTlNqX Chapters: 00:00 Intro 00:12 Chapter 1 - Radiosity 01:55 Chapter 2 - Neural Rendering 03:26 Chapter 3 - Neural Radiosity? Can that really be? Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/XwOx6q2 Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/V278WLO

Building a listwise ranking model with TF Recommenders and TF Ranking

Developer Advocate Wei Wei shows how to leverage TensorFlow Ranking, a deep learning library, to improve the ranking stage for TF Recommenders. Follow along as he shares how to build a listwise ranking model. Resources: TensorFlow Ranking → https://goo.gle/3N2REoe Listwise ranking using TF Recommenders and TF ranking → https://goo.gle/3IfXTRL Position-aware ListMLE: A Sequential Learning Process → https://goo.gle/3JnzdYT Normalized Discounted Cumulative Gain (NDCG) → https://goo.gle/3N9HxxU Deep Learning Recommendation Model (DLRM)→ https://goo.gle/3tgwESX Chapters: 0:00 - Introduction 2:12 - High level overview of TF Ranking 2:57 - Ways to rank a candidate 3:26 - Building a ranking model 7:22 - Deep Learning Recommendation Model 8:14 - Recap Watch more Building recommendation systems with TensorFlow → https://goo.gle/3Bi8NUS Subscribe to TensorFlow → https://goo.gle/TensorFlow product: TensorFlow - TensorFlow Recommenders; fullname: Wei Wei;

Tuesday, March 29, 2022

IUI 2022 - Better Together? An Evaluation of AI-Supported Code Translation

Abstract Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone. Paper https://arxiv.org/abs/2202.07682 Citation Justin D. Weisz, Michael Muller, Steven I. Ross, Fernando Martinez, Stephanie Houde, Mayank Agarwal, Kartik Talamadupula, and John T. Richards. 2022. Better Together? An Evaluation of AI-Supported Code Translation. In 27th International Conference on Intelligent User Interfaces (IUI ’22), March 22– 25, 2022, Helsinki, Finland. ACM, New York, NY, USA, 25 pages. https: //doi.org/10.1145/3490099.3511157 Demo Video https://youtu.be/8bi_LquqfSI

Author Interview - Memory-assisted prompt editing to improve GPT-3 after deployment

#nlp #gpt3 #prompt This is an interview with the authors of this work, Aman Madaan and Niket Tandon. Large language models such as GPT-3 have enabled many breakthroughs and new applications recently, but they come with an important downside: Training them is very expensive, and even fine-tuning is often difficult. This paper presents an adaptive method to improve performance of such models after deployment, without ever changing the model itself. This is done by maintaining a memory of interactions and then dynamically adapting new prompts by augmenting them with memory content. This has many applications, from non-intrusive fine-tuning to personalization. OUTLINE: 0:00 - Intro 0:45 - Paper Overview 2:00 - What was your original motivation? 4:20 - There is an updated version of the paper! 9:00 - Have you studied this on real-world users? 12:10 - How does model size play into providing feedback? 14:10 - Can this be used for personalization? 16:30 - Discussing experimental results 17:45 - Can this be paired with recommender systems? 20:00 - What are obvious next steps to make the system more powerful? 23:15 - Clarifying the baseline methods 26:30 - Exploring cross-lingual customization 31:00 - Where did the idea for the clarification prompt come from? 33:05 - What did not work out during this project? 34:45 - What did you learn about interacting with large models? 37:30 - Final thoughts Paper: https://ift.tt/ep2s9Q5 Code & Data: https://ift.tt/gXYpQtT Abstract: Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homonym, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. All the code and data is available at this https URL. Authors: Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang Links: Merch: https://ift.tt/N9QPExX 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/5RmwMaJ BitChute: https://ift.tt/qwfZdN9 LinkedIn: https://ift.tt/mR02LyN BiliBili: https://ift.tt/n87DjqC 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/QjEWafN Patreon: https://ift.tt/BtYlbZ5 Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Sunday, March 27, 2022

NVIDIA's New AI: Enhance! 🔍

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/Ae2xrli 📝 The paper "EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks" is available here: https://ift.tt/xaWfLPg 📝 The latent space material synthesis paper "Gaussian Material Synthesis" is available here: https://ift.tt/N28YaX7 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/J6hVozv - 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, Jonathan, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Paul F, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, 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/J6hVozv Thumbnail background image credit: https://ift.tt/R5MepkA 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/7rRudcC Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/7U2qgOR Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/5v8DVqH #NVIDIA

Saturday, March 26, 2022

Global AI 2022 - Introduction to Azure Machine Learning

On this session we will get to know Azure Machine Learning, what is its purpose and how does it fit on the Azure ecosystem. Also we will go hands on a high level introduction of its features to follow by jumping into all of the different ways of creating a Machine Learning Model in the different ways that this data science platform provides. At the end of the session you will know and understand Azure Machine Learning, its purpose, functionalities and will be able to use it. Notes and comments Recuerda que tienes las presentaciones y demos de las sesiones del Global AI Bootcamp de Madrid en nuestro repositorio en Github: https://github.com/GlobalAISpain/GlobalAIBootcamp2022 También puedes encontrar a la ponente de esta sesion en Twitter: https://twitter.com/joslat

Friday, March 25, 2022

Global AI 2022 - Introduction to Azure Machine Learning

Thursday, March 24, 2022

Author Interview - BLIP: Bootstrapping Language-Image Pre-training

#blip #interview #salesforce Paper Review Video: https://youtu.be/X2k7n4FuI7c Sponsor: Assembly AI https://ift.tt/EYRx4rH Cross-modal pre-training has been all the rage lately in deep learning, especially training vision and language models together. However, there are a number of issues, such as low quality datasets that limit the performance of any model trained on it, and also the fact that pure contrastive pre-training cannot be easily fine-tuned for most downstream tasks. BLIP unifies different tasks and objectives in a single pre-training run and achieves a much more versatile model, which the paper immediately uses to create, filter, clean and thus bootstrap its own dataset to improve performance even more! OUTLINE: 0:00 - Intro 0:40 - Sponsor: Assembly AI 1:30 - Start of Interview 2:30 - What's the pitch? 4:40 - How did data bootstrapping come into the project? 7:10 - How big of a problem is data quality? 11:10 - Are the captioning & filtering models biased towards COCO data? 14:40 - Could the data bootstrapping be done multiple times? 16:20 - What was the evolution of the BLIP architecture? 21:15 - Are there additional benefits to adding language modelling? 23:50 - Can we imagine a modular future for pre-training? 29:45 - Diving into the experimental results 42:40 - What did and did not work out during the research? 45:00 - How is research life at Salesforce? 46:45 - Where do we go from here? Paper: https://ift.tt/97AJcdB Code: https://ift.tt/pRsJDf5 Demo: https://ift.tt/yqSRFp8 Abstract: Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at this https URL. Authors: Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi 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/mxMkZLo BitChute: https://ift.tt/9nQgZPT LinkedIn: https://ift.tt/nDzjfRE BiliBili: https://ift.tt/RhamAe2 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/Pf24Uhw Patreon: https://ift.tt/6Prc3Y4 Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Wednesday, March 23, 2022

Tutorial 4: HBM System and Architecture for AI applications

Tutorial 4: HBM System and Architecture for AI applications Speakers: Manish Jain and Nikhil Raghavendra Rao (Rambus) Tutorial Abstract: Artificial intelligence/machine learning (AI/ML) is impacting every industry and touching the lives of everyone. AI/ML’s evolution is proceeding at a lightning pace. Training capabilities are growing at a rate of 10X per year driving rapid improvements in every aspect of computing hardware and software. Memory bandwidth is one such critical area of focus enabling the continued growth of AI/ML. HBM memory is the ideal solution for the high bandwidth requirements of AI/ML training. The benefits of HBM make it the superior choice. The performance is outstanding, and higher implementation and manufacturing costs can be traded off against savings of board space and power. In data center environments, its lower power translates to reduced heat loads for an environment where cooling is often one of the top operating costs. This tutorial starts with the discussion on AI/ML driving high memory bandwidth requirements and creating need for innovative memory solutions (2.5D). It then describes HBM system components and its role. HBM performance evolution and how data rates have increased with each generation is discussed. It talks about HBM memory features, HBM PHY design and architecture key points, with emphasis on HBM3 designs. It covers system design, signal integrity and power integrity considerations for a HBM memory subsystem. About Speakers: Manish Jain is Senior Director of Engineering at Rambus, Bangalore. He is responsible for Serdes and Memory PHY development. He has spent 26 years in Semiconductor Industry, with over 17 years spanning at Rambus. He has designed Non-Volatile Memories, Analog Circuits and High-speed Analog Mixed Signal Circuits. His research interests include high-speed mixed-signal CMOS circuit design, transmitter and receiver design, equalization, PLL/DLL design and signal integrity analysis. He has received his B.E Degree in Electronics and Communication Engineering from Maulana Azad National Institute of Technology, Bhopal in 1995 Nikhil Raghavendra Rao is a Principal Engineer, Architecture at Rambus Bangalore. He is responsible for Memory PHY Architecture. He has 17 years of experience in the semiconductor industry and has worked primarily on Digital Design and Verification, SoC Design, FPGA prototyping, Graphics Memory (GDDR) PHY and HBM PHY Architecture. His research interests include Memory subsystem architecture, CPU architectures and Computer networking. He holds a Bachelors degree in Electrical Engineering from NIT Surat and a Master’s degree from Manipal University.

Tutorial 4: AI in Security – A Potential to Make and Break a Secure Connected World

Tutorial 4: AI in Security – A Potential to Make and Break a Secure Connected World Speaker: Prof. Debdeep Mukhopadhyay (IIT KGP) Tutorial Abstract: In this part of the talk, we provide a detailed overview on both the boon and bane of AI on Security. To be more specific we start with describing how Machine Learning (ML)/Deep Learning(DL) can be leveraged to perform advanced side channel attacks on cryptographic implementations. Subsequently, we present deep learning based methodologies for leakage assessment due to fault attacks on crypto-devices. We further present a state-of-the-art overview on the threats of machine learning in modeling Physically Unclonable Functions (PUFs), a promising hardware security primitive. Subsequently, we look at the opportunities from DL based methods in developing effective diagnostic tools for powerful malwares. We present case-studies on using Performance Counter based approaches in detecting menacing threats like ransomware and rowhammer attacks. About Speakers: Prof. Debdeep Mukhopadhyay is currently a Professor at the Department of Computer Science and Engineering, IIT Kharagpur, India. At IIT Kharagpur he initiated the Secured Embedded Architecture Laboratory (SEAL), with a focus on Hardware Security. He had worked as, visiting scientist at NTU Singapore, visiting Associate Professor of NYU Shanghai, Assistant Professor at IIT Madras, and Visiting Researcher at NYU Tandon School of Engineering, USA. He holds a Ph.D., an M.S., and a B.Tech from IIT Kharagpur. His books include Fault Tolerant Architectures for Cryptography and Hardware Security (Springer), Cryptography and Network Security (Mc GrawHills), Hardware Security: Design, Threats, and Safeguards (CRC Press), and Timing Channels in Cryptography (Springer). He has written more than 250 papers in peer-reviewed conferences and journals and collaborated with several Indian/Foreign Organizations. He has been on the program committee and editorial boards of several top international conferences and journals. Prof. Mukhopadhyay is the recipient of the prestigious Shanti Swarup Bhatnagar Award 2021 for Science & Technology, and is a Fellow of the Indian National Academy of Engineers. He was awarded the DST Swarnajayanti Fellowship 2015-16, Data Security Council of India Award for Cyber Security Education, ASEM-DUO Fellowship, INSA Young Scientist award, INAE Young Engineer award, Associateship for the Indian Academy of Sciences. He was awarded the Outstanding Young Faculty fellowship from IIT Kharagpur, and the Techno-Inventor Best PhD award from the Indian Semiconductor Association. He has recently incubated a start-up on Hardware Security, ESP Pvt. Ltd. at IIT Kharagpur, and is a senior member of IEEE and ACM. Manaar Alam is currently a Postdoctoral Associate at the Modern Microprocessors Architecture Laboratory, Center for Cyber Security – Research Institute, NYU Abu Dhabi, UAE. Manaar holds a PhD degree in Computer Science and Engineering from IIT Kharagpur, India. He received the prestigious IBM PhD Fellowship award during his PhD. He also holds an MTech degree from IIT Dhanbad, India, and a BTech degree from IEM Kolkata, India. He worked as a Visiting Research Assistant at NTU Singapore.

Tuesday, March 22, 2022

Tutorial 4: HBM System and Architecture for AI applications

What are Artificial Intelligence, Machine Learning, Deep Learning & Computer Vision? #AI #ML #DL #CV

This video explains what are Artificial Intelligence, Machine Learning, Neural Network, Deep Learning & Computer Vision for beginners. This is the first video of our tutorial series about Artificial Intellligence for beginners that gives a basic introduction to the field of AI and Computer Vision. Time Stamps: 0:00-0:34 : Introduction 0:34-0:46 : What is Artificial Intelligence 0:46-1:11 : Early AI techniques 1:11-1:28 : What is Machine Learning 1:28-2:31 : What is Neural Network 2:31-2:38 : What is Deep Learning 2:38-3:49 : What is Computer Vision 3:49-4:46 : Summary The first use of the phrase “Artificial Intelligence”: https://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802 Artificial Intelligence, or AI, is the science and engineering of making machines learn, think, and act like humans. Machine Learning is a sub-field of AI where machines learn directly from data instead of hand-coded rules. Deep Learning is a sub-field of Machine Learning where machines learn using Deep Neural Networks. And finally, Computer Vision is the science and engineering of interpreting visual data. Many Computer Vision problems are solved using AI, but many others are not. LearnOpenCV is a companion youtube channel for https://learnopencv.com/ where we share tutorials and code ( C++ / Python ) for Computer Vision, Machine Learning, Deep Learning and Artificial Intelligence. Computer Vision and AI Courses https://opencv.org/courses

Tutorial 4: HBM System and Architecture for AI applications

Monday, March 21, 2022

Tutorial 4: HBM System and Architecture for AI applications

Wednesday, March 16, 2022

Python Pandas Tutorials | Handling Missing Data | AI Simplified

Python Pandas Tutorials | Handling Missing Data | AI Simplified Numpy Tutorial Playlist : https://youtube.com/playlist?list=PLteMi10eDWx8acB7mmHtQkSNJSEafVbPE Pandas Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx9EQfWLORFquJmfUfkeWUG2 Matplotlib Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx8APfujX32iqLdMRTcdEoxy 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://t.co/Xqou2r5mBd Follow us on our Social Media Pages: Instagram : https://www.instagram.com/letslearnwise/ Facebook : https://www.facebook.com/Letslearnwise LinkedIn : https://linkedin.com/company/letslear... Website : https://www.letslearnwise.com ⚡ Please leave a LIKE and SUBSCRIBE for more content! ⚡ #python #pythonforbeginners #machinelearning #numpy #pandas #datavisualisation #pythontutorial #matpolitlib #artificialintelligence #datascience #plotting ---------------------------------------------------------------------------------------------------------------------- Want to Enrol in our Live-Instructor Led Online courses?? How it Works? 1. This is a 5 Week live Instructor-led Online Course with industry grade real time examples 2. Every Session will be recorded and accessible during and after the course for life time. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Certificate 4 Mock Interviews will be conducted at the end of the course to prepare for the interviews. Courses Lets LearnWise offers: 1. Python Beginner Course 2. Python Advanced Course 3. Python for Machine Learning 4. Machine Learning 5. Data Science 6. Deep Learning 7. Reinforcement Learning 8. Cloud Platforms like AWS, Azure, GCP 9. Robotics -------------------------------------------------------------------------------------------------------------------- Tags: - Python for beginners - Python Tutorials - Python Pandas Tutorials - Pandas Interview Questions - Python Plots - Data Visualisation - Line chart - ML Projects for Beginners - Machine Learning for Beginners - Beginner Machine Learning Projects

Tuesday, March 15, 2022

Python Pandas Tutorials | Handling Missing Data | AI Simplified

Sunday, March 13, 2022

Practical Application of AI in Petroleum Production | Joy Ugoyah

In this talk, Joy presents a case study on predicting scale deposition in petroleum production with machine learning. She provides the key details including her development methods, evaluation techniques, and final results. About the speaker Joy Ugoyah is a Petroleum Engineer, Machine Learning Engineer, Researcher, and Business Development Strategist for startups. Her interest lies in finding ways to apply AI technology to improve petroleum exploration and production operations. She believes that one person alone cannot build the success of an industry so she loves to share the knowledge she has. She is currently open to opportunities to apply my knowledge of ML/AI practically in the energy industry. Speaker's LinkedIn: https://www.linkedin.com/in/joy-ugoyah/ ---------------------------------------------------------------------------------------------------------------------- 🤔 About AI School Africa🧠 AI School (formerly "Port Harcourt School of AI") is a remote-first, non-profit organization with the cause of connecting interested individuals to quality data science and AI education and opportunities while making the knowledge and use of the technology accessible to underrepresented groups across Africa. We hold monthly meet-ups for our community. Opt-in to receive emails from us on new AI and data science-related meetups decided by our community members every month. 🌐 Website: www.phcschoolofai.org 👆🏽 Join our community: https://bit.ly/aischool_signup 🔜 Events page: https://www.eventbrite.com/o/ai-school-20148283582 🎧 Podcast page: https://anchor.fm/african-data-scientist/ 🐦 Twitter: https://twitter.com/aischool_africa 🔗 LinkedIn page: https://www.linkedin.com/company/ai-school-nigeria/ ---------------------------------------------------------------------------------------------------------------------- SMASH THE LIKE (👍) BUTTON FOR THE AI AND HIT SUBSCRIBE (🔔) TO HELP US KEEP BRINGING YOU AMAZING WORKSHOPS IN AI AND DATA SCIENCE. #machinelearning #artificialintelligence #petroleumengineering #aischool #datascience #scaledeposition #petroleumproduction #casestudy

Smart ML Experiment tracking and model registry with Neptune.ai Platform

Neptune.ai is a Machine Learning observability platform for ML experiment tracking and model registry for the enterprise teams. With this SaaS platform, any enterprise user can log, store, query, display, organize, and compare all your model metadata in a single place. Highlights: - Feel in control of your model building and experimentation - Be more productive at ML engineering and research - Focus on ML, leave metadata bookkeeping to platform - Use computational resources more efficiently - Build reproducible, compliant, and traceable models - Any one can get started in just 5 minutes Content Timeline: ----------------- - (00:00) Video Start - (00:08) Introduction - (02:00) Why and Why? - (04:05) 3 Key Features - (05:05) Platform Service Status - (06:00) MLOps Tutorials - (08:31) Get Platform Access - (09:15) Jupyter Notebook (colab & GitHub) - (10:30) Heart Disease Detection ML Experiment - (12:12) ML Experiment in Keras/TensorFlow - (20:46) Export colab notebook to GitHub - (22:16) Adding Neptune Tracking code - (23:08) Create Tracking Project - (26:17) Initialize Neptune runtime objects - (28:28) ML Experiment with Neptune Objects - (30:01) Neptune Callback with ML Experiment - (32:05) Neptune ML Tracking Dashboard - (38:30) Adding another Experiment - (42:05) Comparing Experiments - (44:32) Custom content (image) Tracking - (45:56) More Experiments tracking - (48:38) Adding Jupyter notebook as resource - (49:57) Pushing Colab notebook to GitHub - (50:19) Recap - (52:25) Credits Neptune.ai: https://neptune.ai/ Source Code used in this example: https://github.com/prodramp/publiccode/tree/master/machine_learning/neptune_ai Please visit: ------------------ Prodramp LLC | https://prodramp.com | @prodramp https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) https://www.linkedin.com/in/avkashchauhan Tags: #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #bias #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai

Scikit Learn Tutorial | Machine Learning with Python | Python Tutorial

Welcome to this video course on Scikit-Learn. This course will explain how to use scikit-learn to do machine learning. The aim of this course is to provide the good understanding of scikit-learn Contents: 1 - Prerequisites to Start Learning Machine Learning 2 - Loading datasets Using Scikit-Learn 3 - Building SVC Model using scikit-learn 4 - Building a pipeline 5 - Hyperparameter Tuning | How to tune Hyperparameters with scikit-learn Methods to load Toy Datasets and exploring their feature names, number of instances and other details have been shown. Also, which library to use when a data is in a particular format is also discussed. Lastly, fetching dataset from openml.org has been shown with an example. Some important links for more info: - https://www.youtube.com/playlist?list... - https://www.youtube.com/playlist?list... - https://www.youtube.com/playlist?list... - https://scikit-learn.org/stable/docum... - https://www.scipy.org/docs.html - https://pandas.pydata.org/pandas-docs... Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. Linear regression, Logistic regression, Cluster analysis. Use SciKit-Learn for Machine Learning Tasks #Python #PythonForDataScience #PythonTutorial #ProgrammingKnowledge #ScikitLearn #ml #ai #pythontutorial #pythonprogrammingforbeginners #pythontraining #pythontutorialforbeginners #numpypythontutorial #pythonsimplilearn #Sklearn #Python #PythonForDataScience #PythonTutorial #PythonForBeginners #PythonOnlineTraining ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ http://bit.ly/2vsuMaS ⚫️ http://bit.ly/2GOaeQB Java Programming Course ➡️ http://bit.ly/2GEfQMf ⚫️ http://bit.ly/2Vvjy4a Bash Shell Scripting Course ➡️ http://bit.ly/2DBVF0C ⚫️ http://bit.ly/2UM06vF Linux Command Line Tutorials ➡️ http://bit.ly/2IXuil0 ⚫️ http://bit.ly/2IXukt8 C Programming Course ➡️ http://bit.ly/2GQCiD1 ⚫️ http://bit.ly/2ZGN6ej C++ Programming Course ➡️ http://bit.ly/2V4oEVJ ⚫️ ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - https://skillshare.eqcm.net/r1KEj Data Science - http://bit.ly/2lD9h5L | http://bit.ly/2lI8wIl Machine Learning - http://bit.ly/2WGGQpb | http://bit.ly/2GghLXX Artificial Intelligence - http://bit.ly/2lYqaYx | http://bit.ly/2NmaPya MERN Stack E-Degree Program - http://bit.ly/2kx2NFe | http://bit.ly/2lWj4no DevOps E-degree - http://bit.ly/2k1PwUQ | http://bit.ly/2k8Ypfy Data Analytics with R - http://bit.ly/2lBKqz8 | http://bit.ly/2lAjos3 AWS Certification Training - http://bit.ly/2kmLtTu | http://bit.ly/2lAkQL1 Projects in Java - http://bit.ly/2kzn25d | http://bit.ly/2lBMffs Machine Learning With TensorFlow - http://bit.ly/2m1z3AF | http://bit.ly/2lBMhnA Angular 8 - Complete Essential Guide - http://bit.ly/2lYvYRP Kotlin Android Development Masterclass - http://bit.ly/2GcblsI Learn iOS Programming Building Advance Projects - http://bit.ly/2kyX7ue DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

Friday, March 11, 2022

Numpy Tutorials | Accessing and Iterating values in Arrays | AI Simplified

Numpy Tutorials | Accessing and Iterating values in Arrays | AI Simplified Numpy Tutorial Playlist : https://youtube.com/playlist?list=PLteMi10eDWx8acB7mmHtQkSNJSEafVbPE Pandas Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx9EQfWLORFquJmfUfkeWUG2 Matplotlibe Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx8APfujX32iqLdMRTcdEoxy 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://t.co/Xqou2r5mBd Follow us on our Social Media Pages: Instagram : https://www.instagram.com/letslearnwise/ Facebook : https://www.facebook.com/Letslearnwise LinkedIn : https://linkedin.com/company/letslear... Website : https://www.letslearnwise.com ⚡ Please leave a LIKE and SUBSCRIBE for more content! ⚡ #python #pythonforbeginners #machinelearning #numpy #pandas #datavisualisation #pythontutorial #matpolitlib #artificialintelligence #datascience #plotting ---------------------------------------------------------------------------------------------------------------------- Want to Enrol in our Live-Instructor Led Online courses?? How it Works? 1. This is a 5 Week live Instructor-led Online Course with industry grade real time examples 2. Every Session will be recorded and accessible during and after the course for life time. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Certificate 4 Mock Interviews will be conducted at the end of the course to prepare for the interviews. Courses Lets LearnWise offers: 1. Python Beginner Course 2. Python Advanced Course 3. Python for Machine Learning 4. Machine Learning 5. Data Science 6. Deep Learning 7. Reinforcement Learning 8. Cloud Platforms like AWS, Azure, GCP 9. Robotics -------------------------------------------------------------------------------------------------------------------- Tags: - Python for beginners - Python Tutorials - Matplotlib Tutorials - Matplotlib Interview Questions - Python Plots - Data Visualisation - Line chart - ML Projects for Beginners - Machine Learning for Beginners - Beginner Machine Learning Projects

Full Scale Machine Learning with DataRobot AI Cloud Platform

End to end machine learning with DataRobot AI Cloud Platform DataRobot AI Cloud is a new approach built for the demands, challenges and opportunities of AI today. It's a single system of record, accelerating the delivery of AI to production for every organization.All users collaborate in a unified environment built for continuous optimization across the entire AI lifecycle. It is designed for the collaboration for all users in the enterprise: - Data Science & Analytics Experts - IT & DevOps Teams - Executives & Information Workers The AI Platform has 3 main functionalities: 1. Data Preparation (Make your data ready for machine learning) 2. Machine Learning (AutoML, VisualML) 3. MLOps (Deploy your model per your need) In this tutorial we are focussing on the second part of the AI platform "Machine Learning". We are going to cover in-depth details about the model building process and explain most of the functionalities related to AI cloud model training, evaluation, performance, re-training, validationm and various other steps. Video Content with Timeline: ---------------------------------------------- - (00:00) Video Start - (00:07) Video Content Intro - (02:20) AI cloud platform access - (02:30) Data Preparation Tutorial Intro - (02:59) ML Development Project - (05:21) Importing Dataset for ML - (06:02) ML Focussed EDA with source data - (07:09) Supervised ML with AI Platform - (09:44) Advance Options with ML Training - (12:19) ML Training Start - (12:48) Data Quality Exploration - (15:21) Features List in Source Data - (22:41) Features Association - (27:16) Data Quality Assessment - (29:50) AI Models in ML Project - (33:02) AI Models Repo - (37:34) Bias and Fairness - (40:49) Feature Impact and Feature Effect - (43:05) Prediction Explanations - (44:22) Explore Model Details - (44:51) Model Evaluations - (47:30) Advanced Model Tuning - (48:22) Model comparisons - (49:54) Model Speed vs Model Accuracy - (50:51) Model Insight - (51:17) Improving Model Accuracy - (53:03) Ensembling or Model Blending - (01:00:52) Deploy Model from ML Pipeline - (01:02:30) AI Report Generation - (01:03:44) AI Platform Documentation - (01:04:35) Thanks - (01:04:49) Credits Please visit: ------------------ Prodramp LLC https://prodramp.com | @prodramp https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) https://www.linkedin.com/in/avkashchauhan Tags: #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #bias #modelbias

Thursday, March 10, 2022

Numpy Tutorials | Accessing and Iterating values in Arrays | AI Simplified

Wednesday, March 9, 2022

Matplotlib Tutorials | Introduction to Plotting - Histograms | AI Simplified

Matplotlib Tutorials | Introduction to Plotting - Histograms | AI Simplified Numpy Tutorial Playlist : https://youtube.com/playlist?list=PLteMi10eDWx8acB7mmHtQkSNJSEafVbPE Pandas Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx9EQfWLORFquJmfUfkeWUG2 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://t.co/Xqou2r5mBd Follow us on our Social Media Pages: Instagram : https://www.instagram.com/letslearnwise/ Facebook : https://www.facebook.com/Letslearnwise LinkedIn : https://linkedin.com/company/letslear... Website : https://www.letslearnwise.com ⚡ Please leave a LIKE and SUBSCRIBE for more content! ⚡ #python #pythonforbeginners #machinelearning #numpy #pandas #datavisualisation #pythontutorial #matpolitlib #artificialintelligence #datascience #plotting ---------------------------------------------------------------------------------------------------------------------- Want to Enrol in our Live-Instructor Led Online courses?? How it Works? 1. This is a 5 Week live Instructor-led Online Course with industry grade real time examples 2. Every Session will be recorded and accessible during and after the course for life time. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Certificate 4 Mock Interviews will be conducted at the end of the course to prepare for the interviews. Courses Lets LearnWise offers: 1. Python Beginner Course 2. Python Advanced Course 3. Python for Machine Learning 4. Machine Learning 5. Data Science 6. Deep Learning 7. Reinforcement Learning 8. Cloud Platforms like AWS, Azure, GCP 9. Robotics -------------------------------------------------------------------------------------------------------------------- Tags: - Python for beginners - Python Tutorials - Matplotlib Tutorials - Matplotlib Interview Questions - Python Plots - Data Visualisation - Line chart - ML Projects for Beginners - Machine Learning for Beginners - Beginner Machine Learning Projects

Tuesday, March 8, 2022

Data centric Explainable ML Tutorial 6: Lab 2 -Explainable ML using the DALEX Package

This lab explores the basics of the DALEX package to explain the random forest (RF) model for land cover mapping. We will calculate global feature (predictor variable) importance, compute and plot accumulated dependence profile (local effects), calculate Shapley values, and plot break down profiles. Additional resources Data-centric Explainable Machine Learning for Land Cover Classification: A Practical Guide in R https://aigeolabs.com/buy-ebook/ Explainable Machine Learning for Land Cover Classification: An Introductory Guide https://aigeolabs.com/ebooks/ Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python. https://ema.drwhy.ai/ R Script for Lab 2 https://aigeolabs.com/wp-content/uploads/2022/03/Lab-2-Explainable-ML-_DALEX.zip Lab 2 Data Set (Download Links) https://aigeolabs.com/wp-content/uploads/2021/09/Gweru_Data_Blog.zip

SYVX - Ai Driven Railway Maintenance

Incorporating patented Ai technology, SYVX allows organisations to centralise disparate data sources, and optimise the maintenance of rail assets based on advanced Ai algorithms, Automated Machine Learning, and Deep Natural Language processing.

Data centric Explainable ML Tutorial 7: Lab 3

This lab will look at how far the oversampling technique can minimize the impact of data imbalance for land cover classification. Additional resources Data-centric Explainable Machine Learning for Land Cover Classification: A Practical Guide in R https://aigeolabs.com/buy-ebook/ Explainable Machine Learning for Land Cover Classification: An Introductory Guide https://aigeolabs.com/ebooks/ Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python. https://ema.drwhy.ai/ R Script for Lab 3 https://aigeolabs.com/wp-content/uploads/2022/03/Lab-3-Lab-3-Data-balancing-Strategies.zip Lab 3 Data Set (Download Links) https://aigeolabs.com/wp-content/uploads/2021/09/Gweru_Data_Blog.zip

Data centric Explainable ML Tutorial 10: Lab 4 Step 3

In this step, we will train and evaluate the random forest (RF) model, and perform land cover classification. Additional resources Data-centric Explainable Machine Learning for Land Cover Classification: A Practical Guide in R https://aigeolabs.com/buy-ebook/ Explainable Machine Learning for Land Cover Classification: An Introductory Guide https://aigeolabs.com/ebooks/ Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python. https://ema.drwhy.ai/ R Script for Lab 4 https://aigeolabs.com/wp-content/uploads/2022/03/Lab-4-Data-centric-Explainable-ML.zip Lab 4 Data Set (Download Links) https://aigeolabs.com/wp-content/uploads/2022/03/EO_TA_2020_Polygon_Dec_Updated.zip

Data Science with Artificial Intelligence 7

Artificial Intelligence and Data Science is a primary requirement of the Industry and Research. Python libraries are powerful tool to implement Data Science concepts and do Data Analysis. This course starts with the Python language and explores the Python libraries and their applications. Case studies with real life data are discussed in this course. The course also discusses Machine Learning concepts. Finally the participants will work on a Live Project where all the concepts learned will be applied.

Data Science with Artificial Intelligence 3

Data Science with Artificial Intelligence 12

Data Science with Artificial Intelligence 9

Tutorial on AI Planning: Theory and Practice

The tutorial provides a theoretical background on AI Planning and introduces some of the existing tools, as well as existing applications that were tackled with these tools. We show how to use the tools on an example application. The participants will get the knowledge and hands-on experience necessary to start using AI Planning tools in their applications. https://aiplanning-tutorial.github.io/

Monday, March 7, 2022

Tutorial on AI Planning: Theory and Practice

Sunday, March 6, 2022

Altair Knowledge Studio Advanced Machine Learning and Artificial Intelligence

Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and Explainable AI without restricting how models are configured and tuned, giving you control over model building. Watch this short demonstration video to learn more.

Saturday, March 5, 2022

Python Pandas Tutorials | CSV and DataFrames | AI Simplified

Python Pandas Tutorials | CSV and DataFrames | AI Simplified Numpy Tutorial Playlist : https://youtube.com/playlist?list=PLteMi10eDWx8acB7mmHtQkSNJSEafVbPE Pandas Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx9EQfWLORFquJmfUfkeWUG2 Matplotlib Tutorial Playlist: https://youtube.com/playlist?list=PLteMi10eDWx8APfujX32iqLdMRTcdEoxy 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://t.co/Xqou2r5mBd Follow us on our Social Media Pages: Instagram : https://www.instagram.com/letslearnwise/ Facebook : https://www.facebook.com/Letslearnwise LinkedIn : https://linkedin.com/company/letslear... Website : https://www.letslearnwise.com ⚡ Please leave a LIKE and SUBSCRIBE for more content! ⚡ #python #pythonforbeginners #machinelearning #numpy #pandas #datavisualisation #pythontutorial #matpolitlib #artificialintelligence #datascience #plotting ---------------------------------------------------------------------------------------------------------------------- Want to Enrol in our Live-Instructor Led Online courses?? How it Works? 1. This is a 5 Week live Instructor-led Online Course with industry grade real time examples 2. Every Session will be recorded and accessible during and after the course for life time. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Certificate 4 Mock Interviews will be conducted at the end of the course to prepare for the interviews. Courses Lets LearnWise offers: 1. Python Beginner Course 2. Python Advanced Course 3. Python for Machine Learning 4. Machine Learning 5. Data Science 6. Deep Learning 7. Reinforcement Learning 8. Cloud Platforms like AWS, Azure, GCP 9. Robotics -------------------------------------------------------------------------------------------------------------------- Tags: - Python for beginners - Python Tutorials - Python Pandas Tutorials - Pandas Interview Questions - Python Plots - Data Visualisation - Line chart - ML Projects for Beginners - Machine Learning for Beginners - Beginner Machine Learning Projects

Friday, March 4, 2022

Orginal & Unique, Application of Machine Learning 🤖 part 1 #shorts #robotics #machinelearning

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Orginal & Unique, Application of Machine learning 🤖 part 20 #shorts #robotics #machinelearning

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Orginal & Unique, Designing Machine Learning project 🤖 part 21 #shorts #robotics #machinelearning

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