Free artificial intelligence and machine learning video tutorial resource: May 2021

Monday, May 31, 2021

In this video, Senior Developer Advocate Priyanka Vergadia will show us how to scale machine learning training resources using TensorFlow Cloud. Stay tuned to learn how you can instantly scale to train with more data and more GPUs. Learn more → https://ift.tt/3uhTRBi What is TensorFlow Cloud → https://youtu.be/d7lAO_cLxDQ Subscribe to TensorFlow → https://goo.gle/TensorFlow

Ensemble Learning Part 5 | Hard Voting | Soft Voting | Machine Learning Tutorial

Voting is an ensemble machine learning algorithm. For regression, a voting ensemble involves making a prediction that is the average of multiple other regression models. In classification, a hard voting ensemble involves summing the votes for crisp class labels from other models and predicting the class with the most votes. A soft voting ensemble involves summing the predicted probabilities for class labels and predicting the class label with the largest sum probability. In this video, you will discover how to create voting ensembles for machine learning algorithms in Python. It is the fifth part of the Ensemble Learning Playlist. All 14 videos combined teaches Ensemble Learning in an In-Depth Manner. ✅Subscribe to our Channel to learn more about AI, ML and Data Science. InsideAIML’s Artificial Intelligence Masters Program provides training in the skills required for a career in AI. You will master Data Science, Deep Learning, TensorFlow, Machine Learning and other AI concepts. The course is designed by IITians and includes projects on advanced algorithms and artificial neural networks. Learn more at: https://insideaiml.com/courses For more updates on courses and tips follow us on: - Telegram: https://t.me/insideaiml - Instagram: https://www.instagram.com/inside_aiml/ - Twitter: https://twitter.com/insideaiml - LinkedIn: https://www.linkedin.com/company/insideaiml - Facebook: https://www.facebook.com/insideaimledu - Youtube: https://www.youtube.com/channel/UCz5qPOuMdz3oXv-gPO3h9Iw #MachineLearning #DataScience #DeepLearning #Python #AI #ArtificialIntelligence #Ensemble Learning #Voting #HardVoting #SoftVoting

Reward Is Enough (Machine Learning Research Paper Explained)

#reinforcementlearning #deepmind #agi What's the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion. OUTLINE: 0:00 - Intro & Outline 4:10 - Reward Maximization 10:10 - The Reward-is-Enough Hypothesis 13:15 - Abilities associated with intelligence 16:40 - My Criticism 26:15 - Reward Maximization through Reinforcement Learning 31:30 - Discussion, Conclusion & My Comments Paper: https://ift.tt/3fZew8a Abstract: In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence. Authors: David Silver, Satinder Singh, Doina Precup, Richard S. Sutton 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

Sunday, May 30, 2021

Ensemble Learning Part 3 | Bias and Variance | Machine Learning Tutorial

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. This video talks about Bias and Variance and it is the third part of the Ensemble Learning Playlist. All 14 videos combined teaches Ensemble Learning in an In-Depth Manner. ✅Subscribe to our Channel to learn more about AI, ML and Data Science. InsideAIML’s Artificial Intelligence Masters Program provides training in the skills required for a career in AI. You will master Data Science, Deep Learning, TensorFlow, Machine Learning and other AI concepts. The course is designed by IITians and includes projects on advanced algorithms and artificial neural networks. Learn more at: https://insideaiml.com/courses For more updates on courses and tips follow us on: - Telegram: https://t.me/insideaiml - Instagram: https://www.instagram.com/inside_aiml/ - Twitter: https://twitter.com/insideaiml - LinkedIn: https://www.linkedin.com/company/insideaiml - Facebook: https://www.facebook.com/insideaimledu - Youtube: https://www.youtube.com/channel/UCz5qPOuMdz3oXv-gPO3h9Iw #MachineLearning #DataScience #DeepLearning #Python #AI #ArtificialIntelligence #Ensemble Learning #Bias #Variance

Machine Learning | Dr Jain Classes | L11 Revision and Self Study Notes | Handwritten notes

Link for the PDF - https://drive.google.com/file/d/1eWx5vavryK77FOn-WfBsr8qRxKLlHLoN/view?usp=sharing Welcome to Dr Jain Classes for CSE. This is the L11. (no audio) Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

[Rant] Can AI read your emotions? (No, but ...)

#facerecognition #emotiondetection #mindreading Face recognition has a bad rep in the ML community. While the technology continuously advances, so does the resistance against its applications, with good reasons: AI emotion analysis hints at a dystopian future where our lives are completely governed by algorithms. However, we must be realistic about what is and isn't possible with AI, and while current systems are not the most accurate, denying the link between your facial expression and your emotions is not productive either. https://twitter.com/jblefevre60/status/1395617615964475392 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

PYTHON AND MACHINE LEARNING : Day -5

7 Days Free Bootcamp on PYTHON AND MACHINE LEARNING in collaboration with Microsoft Learn Student Ambassador Program and AWS Students Club. Link to the notebook: https://github.com/ShapeAI/Python-and-Machine-Learning/blob/main/Data_Types_Operators.ipynb Student Influencer Program Application: https://forms.gle/52RNVYRtM94x4v9B7 📍Website: https://www.shapeai.tech/ 📍LinkedIn : https://www.linkedin.com/in/shape-ai-... 📍Instagram: https://www.instagram.com/shape.ai/?h... 📍 YouTube: https://www.youtube.com/channel/UCTUv... 📍 Telegram: https://t.me/shapeAI

Machine Learning | Dr Jain Classes | L12 Final Lecture | Handwritten notes

Link for the PDF - https://drive.google.com/file/d/1OSQRv0xn5jKDt0_06HBaGGCmLKIgDB9l/view?usp=sharing Welcome to Dr Jain Classes for CSE. This is the L12. (no audio) Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Saturday, May 29, 2021

Fast and Slow Learning of Recurrent Independent Mechanisms (Machine Learning Paper Explained)

#metarim #deeprl #catastrophicforgetting Reinforcement Learning is very tricky in environments where the objective shifts over time. This paper explores agents in multi-task environments that are usually subject to catastrophic forgetting. Building on the concept of Recurrent Independent Mechanisms (RIM), the authors propose to separate the learning procedures for the mechanism parameters (fast) and the attention parameters (slow) and achieve superior results and more stability, and even better zero-shot transfer performance. OUTLINE: 0:00 - Intro & Overview 3:30 - Recombining pieces of knowledge 11:30 - Controllers as recurrent neural networks 14:20 - Recurrent Independent Mechanisms 21:20 - Learning at different time scales 28:40 - Experimental Results & My Criticism 44:20 - Conclusion & Comments Paper: https://ift.tt/3vhBEVU RIM Paper: https://ift.tt/2mFJXMG Abstract: Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules. Authors: Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio 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

Ensemble Learning Part 2 | Super Learning & Stacked Regression | Machine Learning Tutorial

The super learner is an ensemble machine learning algorithm that combines all of the models and model configurations that you might investigate for a predictive modelling problem and uses them to make a prediction as good as or better than any single model that you may have investigated. This video talks about super learning, stacked regression or stacking and it is the second part of the Ensemble Learning Playlist. All 14 videos combined teaches Ensemble Learning in an In-Depth Manner. ✅Subscribe to our Channel to learn more about AI, ML and Data Science. InsideAIML’s Artificial Intelligence Masters Program provides training in the skills required for a career in AI. You will master Data Science, Deep Learning, TensorFlow, Machine Learning and other AI concepts. The course is designed by IITians and includes projects on advanced algorithms and artificial neural networks. Learn more at: https://insideaiml.com/courses For more updates on courses and tips follow us on: - Telegram: https://t.me/insideaiml - Instagram: https://www.instagram.com/inside_aiml/ - Twitter: https://twitter.com/insideaiml - LinkedIn: https://www.linkedin.com/company/insideaiml - Facebook: https://www.facebook.com/insideaimledu - Youtube: https://www.youtube.com/channel/UCz5qPOuMdz3oXv-gPO3h9Iw #MachineLearning #DataScience #DeepLearning #Python #AI #ArtificialIntelligence #Ensemble Learning #Bagging #Boosting

Machine Learning | Dr Jain Classes | L10 Unsupervised Learning paradigm | Handwritten notes

Welcome to Dr Jain Classes for CSE. This is the L10. Covered topics: 1. Nearest Neighbour Classifier 2. K Nearest Neighbour 3. When K is even and odd in KNN 4. Why K should be small? Implications of small and large K 5. Regression using KNN 6. Why KNN works well in close points or dense points? 7. we can say that we should have training samples well spread out in the input space or region. 8. For error to stay within a reasonable limit, your NNC will require an extraordinarily large number of training examples. 9. Curse of Dimensionality 10. VC Dimension for a regression problem 11. Decision Trees 12. Error in Decision Trees is wrt decision label 13. you assign the majority label to a set of examples so wrt to the majority label you will have an error in that node. 14. Error in Decision Trees 15. Worst case in Decision Trees 16. Idea of Decision Trees 17. Selecting an appropriate splitting criteria 18. ID3 algorithm 19. Greedy Algorithm and Decision Trees 20. Classifying test examples in decision trees 21. Will it be useful to use decision trees in real life? 22. Guard Against overfitting 23. Overfitting in Decision trees 24. How to avoid overfitting 25. Performance of SVM (Revision) 26. Regularization 27. Ensemble of Decision trees 28. Drawbacks of ID3 29. Random sampling 30. Random forest classifier 31. Neural Network / Deep Learning introduction 32. Perceptron Algorithm 33. Non-Linear Decision Boundary 34. Cross Entropy Loss 35. 0-1 loss 36. Softmax function 37. Feed Forward Neural Network 38. Training Algorithm 39. Backpropagation 40. Gradient Descent Derivation 41. Equation for Backward pass 42. GD vs SGD 43. Summary for MNIST 44. Data Normalization for Neural Network 45. Adjusting the learning rate (eta) 46. Wandering in space 47. Batch Size 48. Neurons Nonlinear computation units 49. Clustering 50. Similarity Measure 51. Distance function 52. Outcome of Clustering 53. Overlapping Clusters 54. Dendrograms 55. Hierarchical Clustering 56. Single Linkage CLustering 57. Max Linkage CLustering 58. Average Linkage Clustering 59. Neural Network as a universal approximation (IMP) 60. Boolean Algebra using Neural Network 61. Distortion measure for clustering 62. Formulation of Unsupervised clustering using graphs 63. Find partitions that minimize the cut value 64. the optimization problem in clustering 65. problem is to search for the optimal partitions of the graph into k sets of nodes. 66. Dimensionality reduction (no audio) Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/1e4SJ-PBPP2gDfTrRbB7EiGfX3vB2loXq/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Ensemble Learning Part 3 | Bias and Variance | Machine Learning Tutorial

Beautiful Fluid Simulations...In Just 40 Seconds! 🤯

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned post is available here: https://ift.tt/2K6NoaV 📝 The paper "Wave Curves: Simulating Lagrangian water waves on dynamically deforming surfaces" is available here: https://ift.tt/3urDO48 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, 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

Machine Learning | Dr Jain Classes | L11 Revision and Self Study Notes | Handwritten notes

Friday, May 28, 2021

Machine Learning | Dr Jain Classes | L3 Linear Predictors | Handwritten notes tutorial | B/M TECH

Welcome to Dr Jain Classes for CSE. This is L3 Linear Predictors for the Machine Learning Series. Covered topics: 1. Linear Predictors 2. Notation for ML predictors 3. Terminologies 4. Hypothesis class 5. Scaling 6. Binary Classification 7. Separable and Non Separable cases 8. Top view of Linear and Logistic regression Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/1frexkfhKt6i2r4jSRh5rLVfFmhORZTfy/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Machine Learning | Dr Jain Classes | L4 Linear Regression | Handwritten notes tutorial | B/M TECH

Welcome to Dr Jain Classes for CSE. This is L4 Linear Regression for the Machine Learning Series. Covered topics: 1. Iterative Algorithm Rosen Blat (1958) 2. Perceptron Intuition 3. Linear Regression 4. Difference between loss and error in ML 5. Empirical Risk Minimization (ERM) 6. Logistic Regression 7. Halfspace classifier, Linear Regression, Logistic Regression 8. VC Dimension properties. Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/16kD8e9l9vBuYUd99J0MT_mDlDFZANtXF/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Machine Learning | Dr Jain Classes | L5 Proofs | Handwritten notes tutorial | B/M TECH

Welcome to Dr Jain Classes for CSE. This is L5 Linear Regression and Proofs for the Machine Learning Series. Covered topics: Linear Regression and related VC Dimension proofs. Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/1moEhIjx4UR65R8Y5M2zDCQ5b6X6SqBES/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Machine Learning | Dr Jain Classes | L6 Mathematics Revise | Handwritten notes tutorial | B/M TECH

Welcome to Dr Jain Classes for CSE. This is the L6 Revision of Mathematical concepts required for the Machine Learning Series. Covered topics: Please like, share, and subscribe to download the pdf file. ML Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L BTech CSE Notes, Syllabus and GATE Notes Playlist - https://www.youtube.com/playlist?list=PLyn-p9dKO9gLh4EqBkM-86-Aout3llxoz pdf file for this lecture: https://drive.google.com/file/d/1ALrKILVm1rvJb2LI4wdTUOOOwDJFvH84/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Ensemble Learning Part 2 | Super Learning & Stacked Regression | Machine Learning Tutorial

Thursday, May 27, 2021

Machine Learning | Dr Jain Classes | L3 Linear Predictors | Handwritten notes tutorial | B/M TECH

Machine Learning | Dr Jain Classes | L4 Linear Regression | Handwritten notes tutorial | B/M TECH

Machine Learning | Dr Jain Classes | L5 Proofs | Handwritten notes tutorial | B/M TECH

Machine Learning | Dr Jain Classes | L6 Mathematics Revise | Handwritten notes tutorial | B/M TECH

Wednesday, May 26, 2021

[ML News] DeepMind fails to get independence from Google

#deepmind #google #mlnews DeepMind has reportedly failed to negotiate for greater independence from Google/Alphabet. While DeepMind wanted to set up a non-profit-like structure, Google seems to go for the opposite approach and seek tight integration. How is AI best served? Original Article: https://ift.tt/3ysdj1I 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

Train TensorFlow models at cloud scale with TensorFlow Cloud | Demo

What if you could instantly scale your TensorFlow model training from your local environment to take advantage of cloud-scale compute and train a bigger/faster model? Or you could do concurrent hyper-parameter training to more quickly optimize your model for your next Kaggle competition? With tensorflow_cloud, you can! Learn how to get set up and started. Resources: Tensorflow.org/cloud → https://goo.gle/3dY4ILQ Github repo → https://goo.gle/3gM3PrN Speaker: Sina Chavoshi Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Demos → https://goo.gle/io21-alldemos All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #Cloud; ML/AI; Open Source product: TensorFlow - General; event: Google I/O 2021; fullname: Sina Chavoshi;

Beyond evaluation: Improving fairness with Model Remediation | Demo

Fairness evaluation is a crucial step in avoiding bias in order to determine model performance for a variety of users. When we identify that our model underperforms on slices of our data, we need a strategy to mitigate this to avoid creating or reinforcing unfair bias. This Demo demonstrates how the Model Remediation Library can be used to achieve this goal with an emphasis on best practices. MinDiff is the first in what will ultimately be a larger Model Remediation Library of techniques. Resources: Model Remediation Case Study → https://goo.gle/32WZr0T Model Remediation Documentation → https://goo.gle/3eBnSqc Model Remediation GitHub → https://goo.gle/3gM471R Speaker: Sean O'Keefe Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Demos → https://goo.gle/io21-alldemos All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #ML/AI product: TensorFlow - General; event: Google I/O 2021; fullname: Sean O'Keefe;

Easily deploy TF Lite models to the web | Demo

Learn to bridge the gap between mobile and web machine learning (ML) development by deploying a compatible task library TensorFlow Lite model to the web with WebAssembly. We show how it can be done with a couple of lines of code. This supports image classification, image segmentation, object detection, and text classification use cases. Resources: Demo 1 → https://goo.gle/3vxb62N Demo 2 → https://goo.gle/3gLY79b Speaker: Jing Jin Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Demos → https://goo.gle/io21-alldemos All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #ML/AI, Open Source, Web product: TensorFlow - TensorFlow Lite; event: Google I/O 2021; fullname: Jing Jin;

Tuesday, May 25, 2021

Meet Your Virtual AI Stuntman! 💪🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills" is available here: https://ift.tt/2qr44gb ❤️ 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, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, 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 tree image credit: https://ift.tt/3vlGD86 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Machine Learning project tutorial with Scratch 4 AI Extensions!

Scratch for AI has its own AI extensions that enable students of ages 7 to 13 to create their own fun, playful, hands-on and experiential learning experiences with Artificial Intelligence features! This video is a tutorial of how you can explore the concept of Machine Learning by creating and training your own program using the Scratch for AI: Machine Learning extension! Visit our website to learn more: https://aiworldschool.com/ #homeschooling #AIforkids #remotelearning #learnathome #scratch #scratchathome #hourofcode #edtech #Selflearning #csedweek #elearning #scratchforkids #AI LET’S CONNECT Facebook: https://www.facebook.com/aiworldschool/ Twitter: https://twitter.com/Aiworldschool1 LinkedIn: https://www.linkedin.com/company/ai-world-school Instagram: https://www.instagram.com/ai_worldschool/

Monday, May 24, 2021

TensorFlow.js: Make a smart webcam in JS with a pre-trained ML model | Workshop

Learn how to detect over 80 common objects in real time by using a TensorFlow.js pre-trained model in your web browser to give your next web application superpowers. We walk through an end-to-end creation of a smart camera in this Workshop. Resources: What's new in TensorFlow.js? Machine learning for next gen web apps → https://goo.gle/3v6cwBg Join us for the TensorFlow.js Ask Me Anything! → https://goo.gle/3h4JSfR Codelab: Learn how to convert Python ML model to TensorFlow.js → https://goo.gle/2QEsrYK Speaker: Jason Mayes Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #IoT #OpenSource #Web #Cloud product: TensorFlow - TensorFlow JS, TensorFlow - TensorFlow Lite; event: Google I/O 2021; fullname: Jason Mayes; re_ty: Livestream;

Google's AI Map | Q&A

Google has lots of AI offerings, and if you don't know where to start, this is the Ask Me Anything Session for you, where you can ask questions about Cloud, TensorFlow, Mobile, Web, and more. Speaker(s): Laurence Moroney, Dale Markowitz, Karl Weinmeister Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Q&As → https://goo.gle/io21-allQAs All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #Cloud #Opensource product: TensorFlow - General; event: Google I/O 2021; fullname: Laurence Moroney, Dale Markowitz, Karl Weinmeister; re_ty: Livestream;

Building trusted AI products | Workshop

AI unlocks exciting new product opportunities. As a predictive technology,it also brings new challenges for building trusted experiences. In this Workshop, you get insights from the People + AI Research team for building trustworthy, user-centered AI products, walk through a series of exercises on opportunities in the AI development process for improving and calibrating user trust, and get to know a broader toolkit of resources available for further exploration. Resources: Building Trusted AI Products → https://goo.gle/3eJ2f7r People + AI Guidebook → https://goo.gle/2PCCQnm Classify Text with BERT → https://goo.gle/3gNYxMq Speaker: Maysam Moussalem Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #Design #AI/ML product: TensorFlow - General; event: Google I/O 2021; fullname: Maysam Moussalem; re_ty: Livestream;

Make your app a product with the TFX team | Q&A

If you're doing amazing things with machine learning (ML) and are ready to build a product, this Ask Me Anything session is for you. Training your model is just the beginning; you can go from zero to hero with Production ML by using TensorFlow Extended (TFX) to make your app ready for the world. Here, the TFX team discusses how to use TFX to scale your ML application, and answers all of your questions. Speakers: Robert Crowe, Anusha Ramesh, Zhitao Li, Kaz Sato Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Q&As → https://goo.gle/io21-allQAs All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #Opensource product: TensorFlow - TensorFlow Extended; event: Google I/O 2021; fullname: Robert Crowe, Anusha Ramesh, Zhitao Li, Kaz Sato; re_ty: Livestream;

Build and deploy custom object detection model with TensorFlow Lite | Workshop

A common application of machine learning is object detection, where the model is able to determine bounding boxes around instances of that item in the image. Imagine a model that can count the number of birds in your garden and show you where they are. In this Workshop, you learn how to train a custom object detection model and deploy it to an Android app in just a few lines of code. All you need are Android Studio and a web browser. No machine learning knowledge is required. Resources: Codelab → https://goo.gle/3vsSi4w Speaker: Khanh LeViet Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #Android #GooglePlay product: TensorFlow - General, Android - Android Studio; event: Google I/O 2021; fullname: Khanh LeViet; re_ty: Livestream;

Building with TensorFlow Lite for microcontrollers | Workshop

Today, people use TensorFlow to develop large scale machine learning models. But did you know that TensorFlow can now run on microcontrollers? In this Workshop, the speaker discusses the potential of building with TensorFlow Lite for microcontrollers. He debuts demos, shows you how to train a model, and explains where TensorFlow fits in the TinyML ecosystem. Participants should have an Arduino Sense 33 BLE and install the Arduino IDE, but it’s not required. Resources: Tiny Machine Learning Kit → https://goo.gle/3xiJFeO Arduino NANO 33 BLE Sense → https://goo.gle/3v7Ym2p Speaker: Pete Warden Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #OpenSource #IoT product: TensorFlow - TensorFlow Lite; event: Google I/O 2021; fullname: Pete Warden; re_ty: Livestream;

Machine Learning on the web with TensorFlow.js | Q&A

Curious about machine learning in the browser and beyond with JavaScript? In this Ask Me Anything (AMA) Session, key TensorFlow.js members answer questions, hear your suggestions and are here to chat about anything else that's on your mind. This event is open and encourages for people of all levels - even if you're new to ML! Resources: Make a smart webcam in JS with a pre-trained ML model with TensorFlow.js → https://goo.gle/3ur8Qd9 What's new in TensorFlow.js? Machine learning for next gen web apps → https://goo.gle/3v6cwBg TensorFlow.js website - check out our easy to use premade models → https://goo.gle/32UGEDm Easily deploy TF Lite models to the web → https://goo.gle/3tOYNxi\ Speakers: Jason Mayes, Sandeep Gupta, Ping Yu Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Q&As → https://goo.gle/io21-allQAs All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI/ML #Cloud #IoT #Opensource #Web product: TensorFlow - TensorFlow JS; event: Google I/O 2021; fullname: Jason Mayes, Sandeep Gupta, Ping Yu; re_ty: Livestream;

What is TensorFlow Cloud?

Today, we are talking about scaling machine learning training resources right from Colab Notebooks or Kaggle Kernels using TensorFlow Cloud. Senior Developer Advocate Priyanka Vergadia will give an overview of TensorFlow Cloud, provide tips, and more! Learn more → https://ift.tt/3uhTRBi Subscribe to TensorFlow → https://goo.gle/TensorFlow Suggested video → https://goo.gle/3fL9ITV

AI ML DL

Pervasive AI for IoT Applications (Tutorial lecture)

Traditional cloud-based IoT architectures suffer from many issues, including scalability, communication and computational efficiency, in addition to privacy. This motivated the need for new emerging trends such as Edge, Fog, and Pervasive Computing, where we merge hierarchical computing with efficient communication, and leveraging learning-based distributed optimization, in order to resolve many of the issues highlighted above. In this talk, I will highlight the motivation behind pervasive AI models for Internet of Things (IoT), and cyber-physical systems (CPS), in light of traditional cloud-based architectures. Then, I will discuss some contributions we have recently published regarding distributed inference/classifications in IoT, and multi-drone systems, taking into considerations privacy and mobility of network users. I will also cover recent contributions regarding distributed learning scenarios using multi-agents and federated learning architectures that address heterogeneous user data to improve the learning performance, and outcomes in distributed networks.

Machine Learning project tutorial with Scratch 4 AI Extensions!

Expire-Span: Not All Memories are Created Equal: Learning to Forget by Expiring (Paper Explained)

#expirespan #nlp #facebookai Facebook AI (FAIR) researchers present Expire-Span, a variant of Transformer XL that dynamically assigns expiration dates to previously encountered signals. Because of this, Expire-Span can handle sequences of many thousand tokens, while keeping the memory and compute requirements at a manageable level. It severely matches or outperforms baseline systems, while consuming much less resources. We discuss its architecture, advantages, and shortcomings. OUTLINE: 0:00 - Intro & Overview 2:30 - Remembering the past in sequence models 5:45 - Learning to expire past memories 8:30 - Difference to local attention 10:00 - Architecture overview 13:45 - Comparison to Transformer XL 18:50 - Predicting expiration masks 32:30 - Experimental Results 40:00 - Conclusion & Comments Paper: https://ift.tt/3yzjFML Code: https://ift.tt/3oFDzRR ADDENDUM: I mention several times that the gradient signal of the e quantity only occurs inside the R ramp. By that, I mean the gradient stemming from the model loss. The regularization loss acts also outside the R ramp. Abstract: Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous timesteps efficiently, as not all states from previous timesteps are preserved. We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality. Next, we show that Expire-Span can scale to memories that are tens of thousands in size, setting a new state of the art on incredibly long context tasks such as character-level language modeling and a frame-by-frame moving objects task. Finally, we analyze the efficiency of Expire-Span compared to existing approaches and demonstrate that it trains faster and uses less memory. Authors: Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan 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

Sunday, May 23, 2021

Machine Learning | Dr Jain Classes | L1 Course Introduction & Syllabus | Handwritten notes tutorial

Welcome to Dr Jain Classes for CSE. This is L1 Course Introduction & Syllabus for the Machine Learning Series. In this video, I have discussed what topics I will be covering in the complete series of the machine learning series of lectures. Covered topics: 1. Course Content 2. What is learning? 3. Is learning always successful? 4. Examples of successful and unsuccessful learning 5. Email classifier 6. Piegon rat example. 7. Hypothesis 8. Why do we need machine learning? 9. Training/Test/Val split 10. Training Dataset, Testing Dataset, Validation Dataset 11. Definition of the best hypothesis 12. Generalization in Machine Learning Please like, share, and subscribe to download the pdf file. Full Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L pdf file: https://drive.google.com/file/d/1dAoDg3bATS1mM8fafnC06AnTkrQ-vJsg/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Machine Learning | Dr Jain Classes | L2 Supervised and Unsupervised | Handwritten notes tutorial

Welcome to Dr Jain Classes for CSE. This is L2 Supervised, Unsupervised, reinforcement learning, Active Learning, Teacher, for the Machine Learning Series. In this video, I have discussed very early topics of Machine Learning theory. Covered topics: 1. Unsupervised 2. Supervised 3. Reinforcement learning and example. 4. Active learning 5. Teacher-student theory 6. Supervisor's role 7. Helpful, Adversarial and Statistical / Stochastic Supervisors 8. Difference between statistics and machine learning Please like, share, and subscribe to download the pdf file. Full Playlist: https://www.youtube.com/playlist?list=PLyn-p9dKO9gIQf9TkwP99yHGIk7YeAc0L pdf file: https://drive.google.com/file/d/1kjh7zBjCW10fUKLNEFXaSHzGGk2WImQU/view?usp=sharing Channel Description: Hi Students, here I will share my lecture videos in advanced concepts in Computer Science and Engineering. Please subscribe to my channel, share my videos, and like them. You can ask me to my email ID if you want any kind of support related to concepts, tutorials, learning in CSE. I help students with: 1. Providing handwritten notes for CSE Subjects (AI, ML, DL, Graph Theory Applications, Discrete Maths, Cryptography, Operating Systems, Data Structures using C, Algorithms, DCN, Theory of Computation, Digital Logic, and Databases ) 2. Providing Counseling for GATE CSE. 3. IIT Ph.D. and MTech Interview preparation 4. SOP and SOR preparation. 5. Abroad Collabs for universities. 6. Handwritten notes, Slides and PPTs, course design, Academic consultancy 7. Btech & Mtech projects in machine learning, computer vision, and Deep Learning using google colab and many other related problems in academia. Feel free to email me. Tags: Machine learning, Artificial Intelligence, ML notes, AI notes, handwritten notes, last-minute exam guide, Machine Learning tutorial, beginner to advanced machine learning, gate cse, dr jain classes, linear regression, logistic regression, gradient descent, SGD, risk minimization, SVM, support vector machines, kernel trick, kernels, decision trees, random forest, neural networks, perception algorithm, backpropagation, model selection and validation, k fold cross-validation, training, testing, validation dataset, Unsupervised machine learning, supervised machine learning, nearest neighbor, KNN, k means clustering, clustering, PCA, dimensionality reduction, Bayes, Generative models, MLE, LDA, Maximum likelihood estimator, linear discriminant analysis, DNN, CNN, RNN, No free lunch theorem in machine learning, VC dimensions, PAC learning, APAC learning, bias-variance tradeoff

Saturday, May 22, 2021

One AI Explainer

One AI is One Models automated machine learning and data science engine purpose built for creating predictive models and surfacing insight from workforce and business data.

Machine Learning | Dr Jain Classes | L1 Course Introduction & Syllabus | Handwritten notes tutorial

Beautiful Glitter Simulation…Faster Than Real Time! ✨

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "Procedural Physically based BRDF for Real-Time Rendering of Glints" is available here: https://ift.tt/3wqNvRD 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, 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

Friday, May 21, 2021

Hands on with Cloud AI | Workshop

Get hands on and learn how Cloud AI can help Developers and Data Scientists with end-to-end machine learning (ML) workflows. We walk through how to experiment, train, deploy, and manage ML models at scale in this hands-on Workshop. Resources: Build and deploy a model with Vertex AI → https://goo.gle/3xNJA2Q Train TensorFlow models at cloud scale with TensorFlow Cloud → https://goo.gle/3ojYSbu Speaker: Sara Robinson Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #Cloud #AI/ML product: Cloud - AI and Machine Learning - AI Platform, Cloud - General; event: Google I/O 2021; fullname: Sara Robinson; re_ty: Livestream;

Anomaly detection with TensorFlow | Workshop

Learn how to go from basic Keras Sequential models to more complex models using the subclassing API, and see how to build an autoencoder and use it for anomaly detection with an electrocardiogram dataset to find abnormal heart rhythms. Resources: Intro to Autoencoders → https://goo.gle/3eheOXi Speaker(s): Laurence Moroney Watch more: TensorFlow at Google I/O 2021 Playlist → https://goo.gle/io21-TensorFlow-1 All Google I/O 2021 Workshops → https://goo.gle/io21-workshops All Google I/O 2021 Sessions → https://goo.gle/io21-allsessions Subscribe to TensorFlow → https://goo.gle/TensorFlow #GoogleIO #AI #ML product: TensorFlow - General; event: Google I/O 2021; fullname: Laurence Moroney; re_ty: Livestream; "