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In this repo, we index and organize some of the best and most recent machine learning courses available on YouTube. 4 | 5 | **Machine Learning** 6 | 7 | - [Caltech CS156: Learning from Data](#caltech-cs156-learning-from-data) 8 | - [Stanford CS229: Machine Learning](#stanford-cs229-machine-learning) 9 | - [Making Friends with Machine Learning](#making-friends-with-machine-learning) 10 | - [Applied Machine Learning](#applied-machine-learning) 11 | - [Introduction to Machine Learning (Tübingen)](#introduction-to-machine-learning-Tübingen) 12 | - [Machine Learning Lecture (Stefan Harmeling)](#machine-learning-lecture-stefan-harmeling) 13 | - [Statistical Machine Learning (Tübingen)](#statistical-machine-learning-Tübingen) 14 | - [Probabilistic Machine Learning](#probabilistic-machine-learning) 15 | - [MIT 6.S897: Machine Learning for Healthcare (2019)](#mit-6s897-machine-learning-for-healthcare-2019) 16 | 17 | **Deep Learning** 18 | 19 | - [Neural Networks: Zero to Hero](#neural-networks-zero-to-hero-by-andrej-karpathy) 20 | - [MIT: Deep Learning for Art, Aesthetics, and Creativity](#mit-deep-learning-for-art-aesthetics-and-creativity) 21 | - [Stanford CS230: Deep Learning (2018)](#stanford-cs230-deep-learning-2018) 22 | - [Introduction to Deep Learning (MIT)](#introduction-to-deep-learning) 23 | - [CMU Introduction to Deep Learning (11-785)](#cmu-introduction-to-deep-learning-11-785) 24 | - [Deep Learning: CS 182](#deep-learning-cs-182) 25 | - [Deep Unsupervised Learning](#deep-unsupervised-learning) 26 | - [NYU Deep Learning SP21](#nyu-deep-learning-sp21) 27 | - [Foundation Models](#foundation-models) 28 | - [Deep Learning (Tübingen)](#deep-learning-Tübingen) 29 | 30 | **Scientific Machine Learning** 31 | 32 | - [Parallel Computing and Scientific Machine Learning](#parallel-computing-and-scientific-machine-learning) 33 | 34 | **Practical Machine Learning** 35 | 36 | - [LLMOps: Building Real-World Applications With Large Language Models](#llmops-building-real-world-applications-with-large-language-models) 37 | - [Evaluating and Debugging Generative AI](#evaluating-and-debugging-generative-ai) 38 | - [ChatGPT Prompt Engineering for Developers](#chatgpt-prompt-engineering-for-developers) 39 | - [LangChain for LLM Application Development](#langchain-for-llm-application-development) 40 | - [LangChain: Chat with Your Data](#langchain-chat-with-your-data) 41 | - [Building Systems with the ChatGPT API](#building-systems-with-the-chatgpt-api) 42 | - [LangChain & Vector Databases in Production](#langchain--vector-databases-in-production) 43 | - [Building LLM-Powered Apps](#building-llm-powered-apps) 44 | - [Full Stack LLM Bootcamp](#full-stack-llm-bootcamp) 45 | - [Full Stack Deep Learning](#full-stack-deep-learning) 46 | - [Practical Deep Learning for Coders](#practical-deep-learning-for-coders) 47 | - [Stanford MLSys Seminars](#stanford-mlsys-seminars) 48 | - [Machine Learning Engineering for Production (MLOps)](#machine-learning-engineering-for-production-mlops) 49 | - [MIT Introduction to Data-Centric AI](#mit-introduction-to-data-centric-ai) 50 | 51 | **Natural Language Processing** 52 | 53 | - [XCS224U: Natural Language Understanding (2023)](#xcs224u-natural-language-understanding-2023) 54 | - [Stanford CS25 - Transformers United](#stanford-cs25---transformers-united) 55 | - [NLP Course (Hugging Face)](#nlp-course-hugging-face) 56 | - [CS224N: Natural Language Processing with Deep Learning](#cs224n-natural-language-processing-with-deep-learning) 57 | - [CMU Neural Networks for NLP](#cmu-neural-networks-for-nlp) 58 | - [CS224U: Natural Language Understanding](#cs224u-natural-language-understanding) 59 | - [CMU Advanced NLP 2021/2022/2024](#cmu-advanced-nlp) 60 | - [Multilingual NLP](#multilingual-nlp) 61 | - [Advanced NLP](#advanced-nlp) 62 | 63 | **Computer Vision** 64 | 65 | - [CS231N: Convolutional Neural Networks for Visual Recognition](#cs231n-convolutional-neural-networks-for-visual-recognition) 66 | - [Deep Learning for Computer Vision](#deep-learning-for-computer-vision) 67 | - [Deep Learning for Computer Vision (DL4CV)](#deep-learning-for-computer-vision-dl4cv) 68 | - [Deep Learning for Computer Vision (neuralearn.ai)](#deep-learning-for-computer-vision-neuralearnai) 69 | 70 | **Reinforcement Learning** 71 | 72 | - [Deep Reinforcement Learning](#deep-reinforcement-learning) 73 | - [Reinforcement Learning Lecture Series (DeepMind)](#reinforcement-learning-lecture-series-deepmind) 74 | - [Reinforcement Learning (Polytechnique Montreal, Fall 2021)](#reinforcement-learning-polytechnique-montreal-fall-2021) 75 | - [Foundations of Deep RL](#foundations-of-deep-rl) 76 | - [Stanford CS234: Reinforcement Learning](#stanford-cs234-reinforcement-learning) 77 | 78 | **Graph Machine Learning** 79 | 80 | - [Machine Learning with Graphs (Stanford)](#machine-learning-with-graphs-stanford) 81 | - [AMMI Geometric Deep Learning Course](#ammi-geometric-deep-learning-course) 82 | 83 | **Multi-Task Learning** 84 | 85 | - [Multi-Task and Meta-Learning (Stanford)](#stanford-cs330-deep-multi-task-and-meta-learning) 86 | 87 | **Others** 88 | 89 | - [MIT Deep Learning in Life Sciences](#mit-deep-learning-in-life-sciences) 90 | - [Self-Driving Cars (Tübingen)](#self-driving-cars-Tübingen) 91 | - [Advanced Robotics (Berkeley)](#advanced-robotics-uc-berkeley) 92 | 93 | --- 94 | 95 | ## Caltech CS156: Learning from Data 96 | 97 | An introductory course in machine learning that covers the basic theory, algorithms, and applications. 98 | 99 | - Lecture 1: The Learning Problem 100 | - Lecture 2: Is Learning Feasible? 101 | - Lecture 3: The Linear Model I 102 | - Lecture 4: Error and Noise 103 | - Lecture 5: Training versus Testing 104 | - Lecture 6: Theory of Generalization 105 | - Lecture 7: The VC Dimension 106 | - Lecture 8: Bias-Variance Tradeoff 107 | - Lecture 9: The Linear Model II 108 | - Lecture 10: Neural Networks 109 | - Lecture 11: Overfitting 110 | - Lecture 12: Regularization 111 | - Lecture 13: Validation 112 | - Lecture 14: Support Vector Machines 113 | - Lecture 15: Kernel Methods 114 | - Lecture 16: Radial Basis Functions 115 | - Lecture 17: Three Learning Principles 116 | - Lecture 18: Epilogue 117 | 118 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLD63A284B7615313A) 119 | 120 | ## Stanford CS229: Machine Learning 121 | 122 | To learn some of the basics of ML: 123 | 124 | - Linear Regression and Gradient Descent 125 | - Logistic Regression 126 | - Naive Bayes 127 | - SVMs 128 | - Kernels 129 | - Decision Trees 130 | - Introduction to Neural Networks 131 | - Debugging ML Models 132 | ... 133 | 134 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) 135 | 136 | ## Making Friends with Machine Learning 137 | 138 | A series of mini lectures covering various introductory topics in ML: 139 | 140 | - Explainability in AI 141 | - Classification vs. Regression 142 | - Precession vs. Recall 143 | - Statistical Significance 144 | - Clustering and K-means 145 | - Ensemble models 146 | ... 147 | 148 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG) 149 | 150 | ## Neural Networks: Zero to Hero (by Andrej Karpathy) 151 | 152 | Course providing an in-depth overview of neural networks. 153 | 154 | - Backpropagation 155 | - Spelled-out intro to Language Modeling 156 | - Activation and Gradients 157 | - Becoming a Backprop Ninja 158 | 159 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) 160 | 161 | ## MIT: Deep Learning for Art, Aesthetics, and Creativity 162 | 163 | Covers the application of deep learning for art, aesthetics, and creativity. 164 | 165 | - Nostalgia -> Art -> Creativity -> Evolution as Data + Direction 166 | - Efficient GANs 167 | - Explorations in AI for Creativity 168 | - Neural Abstractions 169 | - Easy 3D Content Creation with Consistent Neural Fields 170 | ... 171 | 172 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLCpMvp7ftsnIbNwRnQJbDNRqO6qiN3EyH) 173 | 174 | ## Stanford CS230: Deep Learning (2018) 175 | 176 | Covers the foundations of deep learning, how to build different neural networks(CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects, and career advice for deep learning practitioners. 177 | 178 | - Deep Learning Intuition 179 | - Adversarial examples - GANs 180 | - Full-cycle of a Deep Learning Project 181 | - AI and Healthcare 182 | - Deep Learning Strategy 183 | - Interpretability of Neural Networks 184 | - Career Advice and Reading Research Papers 185 | - Deep Reinforcement Learning 186 | 187 | 🔗 [Link to Course](https://youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) 🔗 [Link to Materials](https://cs230.stanford.edu/syllabus/) 188 | 189 | ## Applied Machine Learning 190 | 191 | To learn some of the most widely used techniques in ML: 192 | 193 | - Optimization and Calculus 194 | - Overfitting and Underfitting 195 | - Regularization 196 | - Monte Carlo Estimation 197 | - Maximum Likelihood Learning 198 | - Nearest Neighbours 199 | - ... 200 | 201 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83) 202 | 203 | ## Introduction to Machine Learning (Tübingen) 204 | 205 | The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction. 206 | 207 | - Linear regression 208 | - Logistic regression 209 | - Regularization 210 | - Boosting 211 | - Neural networks 212 | - PCA 213 | - Clustering 214 | - ... 215 | 216 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) 217 | 218 | ## Machine Learning Lecture (Stefan Harmeling) 219 | 220 | Covers many fundamental ML concepts: 221 | 222 | - Bayes rule 223 | - From logic to probabilities 224 | - Distributions 225 | - Matrix Differential Calculus 226 | - PCA 227 | - K-means and EM 228 | - Causality 229 | - Gaussian Processes 230 | - ... 231 | 232 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLzrCXlf6ypbxS5OYOY3EN_0u2fDuIT6Gt) 233 | 234 | ## Statistical Machine Learning (Tübingen) 235 | 236 | The course covers the standard paradigms and algorithms in statistical machine learning. 237 | 238 | - KNN 239 | - Bayesian decision theory 240 | - Convex optimization 241 | - Linear and ridge regression 242 | - Logistic regression 243 | - SVM 244 | - Random Forests 245 | - Boosting 246 | - PCA 247 | - Clustering 248 | - ... 249 | 250 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) 251 | 252 | ## Practical Deep Learning for Coders 253 | 254 | This course covers topics such as how to: 255 | 256 | - Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems 257 | - Create random forests and regression models 258 | - Deploy models 259 | - Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face 260 | - Foundations and Deep Dive to Diffusion Models 261 | - ... 262 | 263 | 🔗 [Link to Course - Part 1](https://www.youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU) 264 | 265 | 🔗 [Link to Course - Part 2](https://www.youtube.com/watch?v=_7rMfsA24Ls&ab_channel=JeremyHoward) 266 | 267 | ## Stanford MLSys Seminars 268 | 269 | A seminar series on all sorts of topics related to building machine learning systems. 270 | 271 | 🔗 [Link to Lectures](https://www.youtube.com/playlist?list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq) 272 | 273 | ## Machine Learning Engineering for Production (MLOps) 274 | 275 | Specialization course on MLOPs by Andrew Ng. 276 | 277 | 🔗 [Link to Lectures](https://www.youtube.com/playlist?list=PLkDaE6sCZn6GMoA0wbpJLi3t34Gd8l0aK) 278 | 279 | ## MIT Introduction to Data-Centric AI 280 | 281 | Covers the emerging science of Data-Centric AI (DCAI) that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. Topics include: 282 | 283 | - Data-Centric AI vs. Model-Centric AI 284 | - Label Errors 285 | - Dataset Creation and Curation 286 | - Data-centric Evaluation of ML Models 287 | - Class Imbalance, Outliers, and Distribution Shift 288 | - ... 289 | 290 | 🔗 [Course Website](https://dcai.csail.mit.edu/) 291 | 292 | 🔗 [Lecture Videos](https://www.youtube.com/watch?v=ayzOzZGHZy4&list=PLnSYPjg2dHQKdig0vVbN-ZnEU0yNJ1mo5) 293 | 294 | 🔗 [Lab Assignments](https://github.com/dcai-course/dcai-lab) 295 | 296 | ## Machine Learning with Graphs (Stanford) 297 | 298 | To learn some of the latest graph techniques in machine learning: 299 | 300 | - PageRank 301 | - Matrix Factorizing 302 | - Node Embeddings 303 | - Graph Neural Networks 304 | - Knowledge Graphs 305 | - Deep Generative Models for Graphs 306 | - ... 307 | 308 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) 309 | 310 | ## Probabilistic Machine Learning 311 | 312 | To learn the probabilistic paradigm of ML: 313 | 314 | - Reasoning about uncertainty 315 | - Continuous Variables 316 | - Sampling 317 | - Markov Chain Monte Carlo 318 | - Gaussian Distributions 319 | - Graphical Models 320 | - Tuning Inference Algorithms 321 | - ... 322 | 323 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL05umP7R6ij2YE8rRJSb-olDNbntAQ_Bx) 324 | 325 | ## MIT 6.S897: Machine Learning for Healthcare (2019) 326 | 327 | This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. 328 | 329 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) 330 | 331 | ## Introduction to Deep Learning 332 | 333 | To learn some of the fundamentals of deep learning: 334 | 335 | - Introduction to Deep Learning 336 | 337 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) 338 | 339 | ## CMU Introduction to Deep Learning (11-785) 340 | 341 | The course starts off gradually from MLPs (Multi Layer Perceptrons) and then progresses into concepts like attention 342 | and sequence-to-sequence models. 343 | 344 | 🔗 [Link to Course](https://deeplearning.cs.cmu.edu/F22/index.html) \ 345 | 🔗 [Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPxRmjgjm0P1WT6H-gTqE8j9) \ 346 | 🔗 [Tutorials/Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPz8WXg8RqH0sEN6X2L65HUZ) 347 | 348 | ## Deep Learning: CS 182 349 | 350 | To learn some of the widely used techniques in deep learning: 351 | 352 | - Machine Learning Basics 353 | - Error Analysis 354 | - Optimization 355 | - Backpropagation 356 | - Initialization 357 | - Batch Normalization 358 | - Style transfer 359 | - Imitation Learning 360 | - ... 361 | 362 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) 363 | 364 | ## Deep Unsupervised Learning 365 | 366 | To learn the latest and most widely used techniques in deep unsupervised learning: 367 | 368 | - Autoregressive Models 369 | - Flow Models 370 | - Latent Variable Models 371 | - Self-supervised learning 372 | - Implicit Models 373 | - Compression 374 | - ... 375 | 376 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) 377 | 378 | ## NYU Deep Learning SP21 379 | 380 | To learn some of the advanced techniques in deep learning: 381 | 382 | - Neural Nets: rotation and squashing 383 | - Latent Variable Energy Based Models 384 | - Unsupervised Learning 385 | - Generative Adversarial Networks 386 | - Autoencoders 387 | - ... 388 | 389 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) 390 | 391 | ## Foundation Models 392 | 393 | To learn about foundation models like GPT-3, CLIP, Flamingo, Codex, and DINO. 394 | 395 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL9t0xVFP90GD8hox0KipBkJcLX_C3ja67) 396 | 397 | ## Deep Learning (Tübingen) 398 | 399 | This course introduces the practical and theoretical principles of deep neural networks. 400 | 401 | - Computation graphs 402 | - Activation functions and loss functions 403 | - Training, regularization and data augmentation 404 | - Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks 405 | - Deep generative models such as auto-encoders, variational auto-encoders and generative adversarial networks 406 | - ... 407 | 408 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) 409 | 410 | ## Parallel Computing and Scientific Machine Learning 411 | 412 | - The Basics of Scientific Simulators 413 | - Introduction to Parallel Computing 414 | - Continuous Dynamics 415 | - Inverse Problems and Differentiable Programming 416 | - Distributed Parallel Computing 417 | - Physics-Informed Neural Networks and Neural Differential Equations 418 | - Probabilistic Programming, AKA Bayesian Estimation on Programs 419 | - Globalizing the Understanding of Models 420 | 421 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLCAl7tjCwWyGjdzOOnlbGnVNZk0kB8VSa) 422 | 423 | ## XCS224U: Natural Language Understanding (2023) 424 | 425 | This course covers topics such as: 426 | 427 | - Contextual Word Representations 428 | - Information Retrieval 429 | - In-context learning 430 | - Behavioral Evaluation of NLU models 431 | - NLP Methods and Metrics 432 | - ... 433 | 434 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp) 435 | 436 | ## Stanford CS25 - Transformers United 437 | 438 | This course consists of lectures focused on Transformers, providing a deep dive and their applications 439 | 440 | - Introduction to Transformers 441 | - Transformers in Language: GPT-3, Codex 442 | - Applications in Vision 443 | - Transformers in RL & Universal 444 | Compute Engines 445 | - Scaling transformers 446 | - Interpretability with transformers 447 | - ... 448 | 449 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM) 450 | 451 | ## NLP Course (Hugging Face) 452 | 453 | Learn about different NLP concepts and how to apply language models and Transformers to NLP: 454 | 455 | - What is Transfer Learning? 456 | - BPE Tokenization 457 | - Batching inputs 458 | - Fine-tuning models 459 | - Text embeddings and semantic search 460 | - Model evaluation 461 | - ... 462 | 463 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o) 464 | 465 | ## CS224N: Natural Language Processing with Deep Learning 466 | 467 | To learn the latest approaches for deep learning based NLP: 468 | 469 | - Dependency parsing 470 | - Language models and RNNs 471 | - Question Answering 472 | - Transformers and pretraining 473 | - Natural Language Generation 474 | - T5 and Large Language Models 475 | - Future of NLP 476 | - ... 477 | 478 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) 479 | 480 | ## CMU Neural Networks for NLP 481 | 482 | To learn the latest neural network based techniques for NLP: 483 | 484 | - Language Modeling 485 | - Efficiency tricks 486 | - Conditioned Generation 487 | - Structured Prediction 488 | - Model Interpretation 489 | - Advanced Search Algorithms 490 | - ... 491 | 492 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) 493 | 494 | ## CS224U: Natural Language Understanding 495 | 496 | To learn the latest concepts in natural language understanding: 497 | 498 | - Grounded Language Understanding 499 | - Relation Extraction 500 | - Natural Language Inference (NLI) 501 | - NLU and Neural Information Extraction 502 | - Adversarial testing 503 | - ... 504 | 505 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rPt5D0zs3YhbWSZA8Q_DyiJ) 506 | 507 | ## CMU Advanced NLP 508 | 509 | To learn: 510 | 511 | - Basics of modern NLP techniques 512 | - Multi-task, Multi-domain, multi-lingual learning 513 | - Prompting + Sequence-to-sequence pre-training 514 | - Interpreting and Debugging NLP Models 515 | - Learning from Knowledge-bases 516 | - Adversarial learning 517 | - ... 518 | 519 | 🔗 [Link to 2021 Edition](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AYSXn_GKVgwXVluCT9chJ6) 520 | 521 | 🔗 [Link to 2022 Edition](https://www.youtube.com/playlist?list=PL8PYTP1V4I8D0UkqW2fEhgLrnlDW9QK7z) 522 | 523 | 🔗 [Link to 2024 Edition](https://www.youtube.com/playlist?list=PL8PYTP1V4I8DZprnWryM4nR8IZl1ZXDjg) 524 | 525 | ## Multilingual NLP 526 | 527 | To learn the latest concepts for doing multilingual NLP: 528 | 529 | - Typology 530 | - Words, Part of Speech, and Morphology 531 | - Advanced Text Classification 532 | - Machine Translation 533 | - Data Augmentation for MT 534 | - Low Resource ASR 535 | - Active Learning 536 | - ... 537 | 538 | 🔗 [Link to 2020 Course](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5) 539 | 540 | 🔗 [Link to 2022 Course](https://www.youtube.com/playlist?list=PL8PYTP1V4I8BhCpzfdKKdd1OnTfLcyZr7) 541 | 542 | ## Advanced NLP 543 | 544 | To learn advanced concepts in NLP: 545 | 546 | - Attention Mechanisms 547 | - Transformers 548 | - BERT 549 | - Question Answering 550 | - Model Distillation 551 | - Vision + Language 552 | - Ethics in NLP 553 | - Commonsense Reasoning 554 | - ... 555 | 556 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) 557 | 558 | ## CS231N: Convolutional Neural Networks for Visual Recognition 559 | 560 | Stanford's Famous CS231n course. The videos are only available for the Spring 2017 semester. The course is currently known as Deep Learning for Computer Vision, but the Spring 2017 version is titled Convolutional Neural Networks for Visual Recognition. 561 | 562 | - Image Classification 563 | - Loss Functions and Optimization 564 | - Introduction to Neural Networks 565 | - Convolutional Neural Networks 566 | - Training Neural Networks 567 | - Deep Learning Software 568 | - CNN Architectures 569 | - Recurrent Neural Networks 570 | - Detection and Segmentation 571 | - Visualizing and Understanding 572 | - Generative Models 573 | - Deep Reinforcement Learning 574 | 575 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) 🔗 [Link to Materials](http://cs231n.stanford.edu/2017/) 576 | 577 | ## Deep Learning for Computer Vision 578 | 579 | To learn some of the fundamental concepts in CV: 580 | 581 | - Introduction to deep learning for CV 582 | - Image Classification 583 | - Convolutional Networks 584 | - Attention Networks 585 | - Detection and Segmentation 586 | - Generative Models 587 | 588 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) 589 | 590 | ## Deep Learning for Computer Vision (DL4CV) 591 | 592 | To learn modern methods for computer vision: 593 | 594 | - CNNs 595 | - Advanced PyTorch 596 | - Understanding Neural Networks 597 | - RNN, Attention and ViTs 598 | - Generative Models 599 | - GPU Fundamentals 600 | - Self-Supervision 601 | - Neural Rendering 602 | - Efficient Architectures 603 | 604 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) 605 | 606 | ## Deep Learning for Computer Vision (neuralearn.ai) 607 | 608 | To learn modern methods for computer vision: 609 | 610 | - Self-Supervised Learning 611 | - Neural Rendering 612 | - Efficient Architectures 613 | - Machine Learning Operations (MLOps) 614 | - Modern Convolutional Neural Networks 615 | - Transformers in Vision 616 | - Model Deployment 617 | 618 | 🔗 [Link to Course](https://www.youtube.com/watch?v=IA3WxTTPXqQ) 619 | 620 | ## AMMI Geometric Deep Learning Course 621 | 622 | To learn about concepts in geometric deep learning: 623 | 624 | - Learning in High Dimensions 625 | - Geometric Priors 626 | - Grids 627 | - Manifolds and Meshes 628 | - Sequences and Time Warping 629 | - ... 630 | 631 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C) 632 | 633 | ## Deep Reinforcement Learning 634 | 635 | To learn the latest concepts in deep RL: 636 | 637 | - Intro to RL 638 | - RL algorithms 639 | - Real-world sequential decision making 640 | - Supervised learning of behaviors 641 | - Deep imitation learning 642 | - Cost functions and reward functions 643 | - ... 644 | 645 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) 646 | 647 | ## Reinforcement Learning Lecture Series (DeepMind) 648 | 649 | The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. 650 | 651 | - Introduction to RL 652 | - Dynamic Programming 653 | - Model-free algorithms 654 | - Deep reinforcement learning 655 | - ... 656 | 657 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) 658 | 659 | 660 | ## LLMOps: Building Real-World Applications With Large Language Models 661 | 662 | Learn to build modern software with LLMs using the newest tools and techniques in the field. 663 | 664 | 🔗 [Link to Course](https://www.comet.com/site/llm-course/) 665 | 666 | ## Evaluating and Debugging Generative AI 667 | 668 | You'll learn: 669 | 670 | - Instrument A Jupyter Notebook 671 | - Manage Hyperparameters Config 672 | - Log Run Metrics 673 | - Collect artifacts for dataset and model versioning 674 | - Log experiment results 675 | - Trace prompts and responses for LLMs 676 | - ... 677 | 678 | 🔗 [Link to Course](https://www.deeplearning.ai/short-courses/evaluating-debugging-generative-ai/) 679 | 680 | ## ChatGPT Prompt Engineering for Developers 681 | 682 | Learn how to use a large language model (LLM) to quickly build new and powerful applications. 683 | 684 | 🔗 [Link to Course](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) 685 | 686 | ## LangChain for LLM Application Development 687 | 688 | You'll learn: 689 | 690 | - Models, Prompt, and Parsers 691 | - Memories for LLMs 692 | - Chains 693 | - Question Answering over Documents 694 | - Agents 695 | 696 | 🔗 [Link to Course](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/) 697 | 698 | ## LangChain: Chat with Your Data 699 | 700 | You'll learn about: 701 | 702 | - Document Loading 703 | - Document Splitting 704 | - Vector Stores and Embeddings 705 | - Retrieval 706 | - Question Answering 707 | - Chat 708 | 709 | 🔗 [Link to Course](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) 710 | 711 | ## Building Systems with the ChatGPT API 712 | 713 | Learn how to automate complex workflows using chain calls to a large language model. 714 | 715 | 🔗 [Link to Course](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) 716 | 717 | ## LangChain & Vector Databases in Production 718 | 719 | Learn how to use LangChain and Vector DBs in Production: 720 | 721 | - LLMs and LangChain 722 | - Learning how to Prompt 723 | - Keeping Knowledge Organized with Indexes 724 | - Combining Components Together with Chains 725 | - ... 726 | 727 | 🔗 [Link to Course](https://learn.activeloop.ai/courses/langchain) 728 | 729 | ## Building LLM-Powered Apps 730 | 731 | Learn how to build LLM-powered applications using LLM APIs 732 | 733 | - Unpacking LLM APIs 734 | - Building a Baseline LLM Application 735 | - Enhancing and Optimizing LLM Applications 736 | - ... 737 | 738 | 🔗 [Link to Course](https://www.wandb.courses/courses/building-llm-powered-apps) 739 | 740 | ## Full Stack LLM Bootcamp 741 | 742 | To learn how to build and deploy LLM-powered applications: 743 | 744 | - Learn to Spell: Prompt Engineering 745 | - LLMOPs 746 | - UX for Language User Interfaces 747 | - Augmented Language Models 748 | - Launch an LLM App in One Hour 749 | - LLM Foundations 750 | - Project Walkthrough: askFSDL 751 | - ... 752 | 753 | 🔗 [Link to Course](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/) 754 | 755 | ## Full Stack Deep Learning 756 | 757 | To learn full-stack production deep learning: 758 | 759 | - ML Projects 760 | - Infrastructure and Tooling 761 | - Experiment Managing 762 | - Troubleshooting DNNs 763 | - Data Management 764 | - Data Labeling 765 | - Monitoring ML Models 766 | - Web deployment 767 | - ... 768 | 769 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) 770 | 771 | ## Introduction to Deep Learning and Deep Generative Models 772 | 773 | Covers the fundamental concepts of deep learning 774 | 775 | - Single-layer neural networks and gradient descent 776 | - Multi-layer neural networks and backpropagation 777 | - Convolutional neural networks for images 778 | - Recurrent neural networks for text 779 | - Autoencoders, variational autoencoders, and generative adversarial networks 780 | - Encoder-decoder recurrent neural networks and transformers 781 | - PyTorch code examples 782 | 783 | 🔗 [Link to Course](https://www.youtube.com/watch?v=1nqCZqDYPp0&list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) 🔗 [Link to Materials](https://sebastianraschka.com/blog/2021/dl-course.html) 784 | 785 | ## Self-Driving Cars (Tübingen) 786 | 787 | Covers the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques. 788 | 789 | - Camera, lidar and radar-based perception 790 | - Localization, navigation, path planning 791 | - Vehicle modeling/control 792 | - Deep Learning 793 | - Imitation learning 794 | - Reinforcement learning 795 | 796 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) 797 | 798 | ## Reinforcement Learning (Polytechnique Montreal, Fall 2021) 799 | 800 | Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems. 801 | 802 | - Introduction to RL 803 | - Multi-armed bandits 804 | - Policy Gradient Methods 805 | - Contextual Bandits 806 | - Finite Markov Decision Process 807 | - Dynamic Programming 808 | - Policy Iteration, Value Iteration 809 | - Monte Carlo Methods 810 | - ... 811 | 812 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) 🔗 [Link to Materials](https://chandar-lab.github.io/INF8953DE/) 813 | 814 | ## Foundations of Deep RL 815 | 816 | A mini 6-lecture series by Pieter Abbeel. 817 | 818 | - MDPs, Exact Solution Methods, Max-ent RL 819 | - Deep Q-Learning 820 | - Policy Gradients and Advantage Estimation 821 | - TRPO and PPO 822 | - DDPG and SAC 823 | - Model-based RL 824 | 825 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjNymuBM9RdmB3Z9N5-0IlY0) 826 | 827 | ## Stanford CS234: Reinforcement Learning 828 | 829 | Covers topics from basic concepts of Reinforcement Learning to more advanced ones: 830 | 831 | - Markov decision processes & planning 832 | - Model-free policy evaluation 833 | - Model-free control 834 | - Reinforcement learning with function approximation & Deep RL 835 | - Policy Search 836 | - Exploration 837 | - ... 838 | 839 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) 🔗 [Link to Materials](https://web.stanford.edu/class/cs234/) 840 | 841 | ## Stanford CS330: Deep Multi-Task and Meta Learning 842 | 843 | This is a graduate-level course covering different aspects of deep multi-task and meta learning. 844 | 845 | - Multi-task learning, transfer learning basics 846 | - Meta-learning algorithms 847 | - Advanced meta-learning topics 848 | - Multi-task RL, goal-conditioned RL 849 | - Meta-reinforcement learning 850 | - Hierarchical RL 851 | - Lifelong learning 852 | - Open problems 853 | 854 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) 🔗 [Link to Materials](https://cs330.stanford.edu/) 855 | 856 | ## MIT Deep Learning in Life Sciences 857 | 858 | A course introducing foundations of ML for applications in genomics and the life sciences more broadly. 859 | 860 | - Interpreting ML Models 861 | - DNA Accessibility, Promoters and Enhancers 862 | - Chromatin and gene regulation 863 | - Gene Expression, Splicing 864 | - RNA-seq, Splicing 865 | - Single cell RNA-sequencing 866 | - Dimensionality Reduction, Genetics, and Variation 867 | - Drug Discovery 868 | - Protein Structure Prediction 869 | - Protein Folding 870 | - Imaging and Cancer 871 | - Neuroscience 872 | 873 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB) 874 | 875 | 🔗 [Link to Materials](https://mit6874.github.io/) 876 | 877 | ## Advanced Robotics: UC Berkeley 878 | 879 | This is course is from Peter Abbeel and covers a review on reinforcement learning and continues to applications in robotics. 880 | 881 | - MDPs: Exact Methods 882 | - Discretization of Continuous State Space MDPs 883 | - Function Approximation / Feature-based Representations 884 | - LQR, iterative LQR / Differential Dynamic Programming 885 | - ... 886 | 887 | 🔗 [Link to Course](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjNBPJdt8WamRAt4XKc639wF) 🔗 [Link to Materials](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa19/) 888 | 889 | --- 890 | 891 | Reach out on [Twitter](https://twitter.com/omarsar0) if you have any questions. 892 | 893 | If you are interested to contribute, feel free to open a PR with a link to the course. It will take a bit of time, but I have plans to do many things with these individual lectures. We can summarize the lectures, include notes, provide additional reading material, include difficulty of content, etc. 894 | 895 | You can now find ML Course notes [here](https://github.com/dair-ai/ML-Course-Notes). 896 | --------------------------------------------------------------------------------