├── .idea ├── .gitignore ├── awesome-deep-learning-resources.iml ├── inspectionProfiles │ ├── Project_Default.xml │ └── profiles_settings.xml ├── misc.xml ├── modules.xml └── vcs.xml └── README.md /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/awesome-deep-learning-resources.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 17 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome resources related to Deep learning / Computer Vision / NLP / Multimodal 2 | 3 | Before starting, I would recommend watching these two awesome videos by Andrew Ng on general career advice. [[short](https://www.youtube.com/watch?v=uLL6RZhoj3o) | [long](https://www.youtube.com/watch?v=733m6qBH-jI)] 4 | 5 | One should start from probability, linear algebra, optimization, then to go deep into deep learning related topics. Here 6 | are the links: 7 | 8 | ## Fundamental Mathematical concepts 9 | 10 | | Mathematical topics | Link | Professor (Institution) | 11 | |-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------| 12 | | Linear Algebra | [Lectures](https://www.youtube.com/watch?v=QVKj3LADCnA&list=PL49CF3715CB9EF31D) | Gilbert Strang (MIT) | 13 | | Probability | [Lectures](https://www.youtube.com/watch?v=1uW3qMFA9Ho&list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6) | John Tsitsiklis (MIT) | 14 | | MIT RES.LL-005 Mathematics of Big Data and Machine Learning | [Lectures](https://www.youtube.com/watch?v=t4K6lney7Zw&list=PLUl4u3cNGP62uI_DWNdWoIMsgPcLGOx-V) | Jeremy Kepner, Vijay Gadepally (MIT) | 15 | | Probability for Computer Scientists | [Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOpr_A7B9SriE_iZmkanvUg) | Jerry Cain (Stanford University) | 16 | | Probability | [Lectures](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | Joe Blitzstein (Harvard) | 17 | | Optimization related to deep learning | [Lectures](https://www.youtube.com/watch?v=Cx5Z-OslNWE&list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | Gilbert Strang (MIT) | 18 | | Optimization (General) | [Lectures](https://www.youtube.com/watch?v=9hToSoZXM9s&list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | Geoff Gordon (CMU) | 19 | | Convex optimization | [Lectures](https://www.youtube.com/watch?v=XFKBNJ14UmY&list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | Ryan Tibshirani (CMU) | 20 | | Causal Inference | [Lectures](https://www.youtube.com/@BradyNealCausalInference) | Brady Neal (MILA) | 21 | | Probabilistic Graphic Models | [Lectures](https://www.youtube.com/watch?v=ideeHZ4luXw&list=PLMaUodmJlZajWSyD5CPoRFxVdq4MNyHYw) | Kayhan Batmaghelich (Now at Boston University, lectures recorded at CMU) | 22 | | Probablistic Graphical Models | [Lectures](https://www.youtube.com/watch?v=GqMzbbaN6T4&list=PLzERW_Obpmv-_TkPEmCyzaJUGHtl7S01i) | Daphne koller (Stanford) | 23 | | Engineering Math: Differential Equations and Dynamical Systems | [Lectures](https://www.youtube.com/watch?v=9fQkLQZe3u8&list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA) | Steve Brunton (University of Washington) | 24 | | Engineering Math: Vector Calculus and Partial Differential Equations | [Lectures](https://www.youtube.com/playlist?list=PLMrJAkhIeNNQromC4WswpU1krLOq5Ro6S) | Steve Brunton (University of Washington) | 25 | | Complex Analysis | [Lectures](https://www.youtube.com/watch?v=_mv0q7-WF4E&list=PLMrJAkhIeNNQBRslPb7I0yTnES981R8Cg) | Steve Brunton (University of Washington) | 26 | 27 | ## Deep learning coding in pytorch fundamentals 28 | 29 | | Course | 30 | |-------------------------------------------------------------------------------------------------------------------------------| 31 | | [Deep Lizard](https://www.youtube.com/watch?v=v5cngxo4mIg&list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG) | 32 | | [Aladdin Persson (pytorch)](https://www.youtube.com/watch?v=2S1dgHpqCdk&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz) | 33 | | [Aladdin Persson (pytorch lightning)](https://www.youtube.com/watch?v=XbIN9LaQycQ&list=PLhhyoLH6IjfyL740PTuXef4TstxAK6nGP) | 34 | | [freeCodeCamp](https://www.youtube.com/watch?v=V_xro1bcAuA&t=2598s) | 35 | | [Pytorch autograd](https://www.youtube.com/watch?v=MswxJw-8PvE) | 36 | | [Pytorch distributed training](https://www.youtube.com/watch?v=toUSzwR0EV8) | 37 | | [Generative Adversarial Networks (GANs)](https://www.youtube.com/watch?v=OXWvrRLzEaU&list=PLhhyoLH6IjfwIp8bZnzX8QR30TRcHO8Va) | 38 | | [Object detections](https://www.youtube.com/watch?v=t-phGBfPEZ4&list=PLhhyoLH6Ijfw0TpCTVTNk42NN08H6UvNq) | 39 | | [Umar Jamil (architectures e.g.,transformers, diffusion)](https://www.youtube.com/@umarjamilai) | 40 | | [Andrej Karpathy (building language model step by step)](https://www.youtube.com/watch?v=kCc8FmEb1nY&t=4725s) | 41 | | [PyTorch and Monai for AI Healthcare Imaging](https://www.youtube.com/watch?v=M3ZWfamWrBM) | 42 | 43 | ## Deep learning / Computer vision / Medical imaging / NLP courses 44 | 45 | | Professor (Institute) | Course name (Link) | Course id | 46 | |-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| 47 | | Bhiksha Raj (CMU) | [Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLp-0K3kfddPxRmjgjm0P1WT6H-gTqE8j9) | [11-785](https://deeplearning.cs.cmu.edu/S24/index.html) | 48 | | Soheil Feizi (UMD) | [Foundations of Deep Learning](https://www.youtube.com/watch?v=6Nctj1GCFVo&list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | [CMSC 828W](https://www.cs.umd.edu/class/fall2020/cmsc828W/) | 49 | | Yann LeCun, Alfredo Canziani (NYU) | [DEEP LEARNING](https://www.youtube.com/watch?v=mTtDfKgLm54&list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | [DS-GA 1008](https://atcold.github.io/NYU-DLSP21/) | 50 | | Andrew Ng (Stanford) | [Deep Learning](https://www.youtube.com/watch?v=PySo_6S4ZAg&list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | [CS 230](https://cs230.stanford.edu/) | 51 | | Alexander Amini (MIT) | [Introduction to Deep Learning](https://www.youtube.com/watch?v=QDX-1M5Nj7s&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) | [MIT 6.S191](http://introtodeeplearning.com/) | 52 | | Sergey Levine (UC Berkeley) | [Deep Learning](https://www.youtube.com/watch?v=rSY1pVGdZ4I&list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | [CS W182 / 282A](https://cs182sp21.github.io/) | 53 | | Pieter Abbeel (UC Berkeley) | [Deep Unsupervised Learning](https://www.youtube.com/watch?v=tFR6Likf4VI&list=PLwRJQ4m4UJjPIvv4kgBkvu_uygrV3ut_U) | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp24/home) | 54 | | Andreas Geiger (University of Tübingen) | [Computer vision](https://www.youtube.com/watch?v=YW1cIaOBkI8&list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | [Course link](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/) | 55 | | Louis-Philippe Morency (CMU) | [Multimodal Machine Learning](https://www.youtube.com/watch?v=6YsbpYSO_QM&list=PL-Fhd_vrvisNM7pbbevXKAbT_Xmub37fA) | [11-777](https://cmu-multicomp-lab.github.io/mmml-course/fall2022/) | 56 | | Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo (UC Berkeley) | [Modern Computer Vision](https://www.youtube.com/watch?v=Q3fqoJ41g6U&list=PLzWRmD0Vi2KVsrCqA4VnztE4t71KnTnP5) | [CS 198-126](https://ml-berkeley.notion.site/CS-198-126-Deep-Learning-for-Visual-Data-a57e2aca54c046edb7014439f81ba1d5) | 57 | | Yogesh S Rawat (UCF) | [Computer Vision](https://www.youtube.com/watch?v=upVlsNsam4M&list=PLd3hlSJsX_Im0zAkTX3ogoiDN9Y7G6tSx) | [CAP5415](https://www.crcv.ucf.edu/courses/cap5415-fall-2022/) | 58 | | Mubarak Shah (UCF) | [Advanced Computer Vision](https://www.youtube.com/watch?v=iSJDgVRST7c&list=PLd3hlSJsX_In7qup928HaHmilugBGctuF) | [CAP6412](https://www.crcv.ucf.edu/courses/cap6412-spring-2023/) | 59 | | Chen Chen (UCF) | [Medical Image Computing](https://www.youtube.com/watch?v=BWD9_EEacEg&list=PLd3hlSJsX_IkrRa_MTEsSVWc0zxHbjx9z) | [CAP 5516](https://www.crcv.ucf.edu/chenchen/cap5516-spring2022.html) | 60 | | Justin Johnson (University of Michigan) | [Deep Learning for Computer Vision](https://www.youtube.com/watch?v=dJYGatp4SvA&list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | [EECS 498.008 / 598.008](https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/) | 61 | | Christopher Manning (Standford) | [NLP with Deep Learning](https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4) | [CS224N](https://web.stanford.edu/class/cs224n/) | 62 | | Graham Neubig (CMU) | [Advanced NLP](https://www.youtube.com/watch?v=pGC-QiNbuwA&list=PL8PYTP1V4I8AYSXn_GKVgwXVluCT9chJ6) | [CMU CS 11-711](https://phontron.com/class/anlp2021/schedule/class-introduction.html) | 63 | | Sergey Levine (UC Berkeley) | [Deep Reinforcement Learning](https://www.youtube.com/watch?v=JHrlF10v2Og&list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH&index=1) | [CS285](https://rail.eecs.berkeley.edu/deeprlcourse/resources/) | 64 | | Emma Brunskill (Stanford) | [Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | CS234 | 65 | | Chelsea Finn (Stanford) | [Deep Multi-Task & Meta Learning](https://www.youtube.com/watch?v=bkVCAk9Nsss&list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI) | [CS330](https://cs330.stanford.edu/) | 66 | | DeepFindr - Talk series | [Understanding Graph Neural Networks](https://www.youtube.com/watch?v=fOctJB4kVlM&list=PLV8yxwGOxvvoNkzPfCx2i8an--Tkt7O8Z) | | 67 | | Nando de Freitas (University of Oxford) | [Deep Learning](https://www.youtube.com/watch?v=dV80NAlEins&list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | [Course Link](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) | 68 | | Ali Ghodsi (University of Waterloo) | [Deep Learning](https://www.youtube.com/watch?v=RLH2meHRHHc&list=PLehuLRPyt1HxuYpdlW4KevYJVOSDG3DEz) | STAT 940 | 69 | | Ali Ghodsi (University of Waterloo) | [Deep Learning](https://www.youtube.com/watch?v=-WDfNxid_8Q&list=PLehuLRPyt1Hwqk1BopyiREdPfJng2cuNQ) | STAT 946 | 70 | | Ali Ghodsi (University of Waterloo) | [Data Visualization](https://www.youtube.com/watch?v=L-pQtGm3VS8&list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | STAT 442/842 | 71 | | Tianqi Chen, Zico Kolter (CMU) | [Deep Learning Systems](https://www.youtube.com/watch?v=ftP5HeOvsI0&list=PLGzYMymX8amNyGPuJ35YWdq59eQ5jYCZ1) | [10-414/714](https://dlsyscourse.org/) | 72 | | Steve Brunton (University of Washington) | [Data-Driven Dynamical Systems](https://www.youtube.com/watch?v=Kap3TZwAsv0&list=PLMrJAkhIeNNR6DzT17-MM1GHLkuYVjhyt) | [Course Link](https://www.eigensteve.com/) | 73 | | Volodymyr Kuleshov (Cornell University) | [Deep Generative Models](https://www.youtube.com/watch?v=IZgvgLy1wyg&list=PL2UML_KCiC0UPzjW9BjO-IW6dqliu9O4B) | CS 6785 | 74 | 75 | ## Machine learning courses 76 | 77 | | Professor (Institute) | Course name (Link) | Course id | 78 | |------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------| 79 | | Yaser Abu-Mostafa (Caltech) | [Machine Learning](https://www.youtube.com/watch?v=mbyG85GZ0PI&list=PLD63A284B7615313A) | CS-156 | 80 | | Andrew Ng (Stanford) | [Machine Learning](https://www.youtube.com/watch?v=jGwO_UgTS7I&list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | [CS 229](https://cs229.stanford.edu/syllabus-autumn2018.html) | 81 | | Roni Rosenfel (CMU) | [Machine Learning](https://www.youtube.com/watch?v=GEopUIvfJmA&list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | [10601A / 10601C](https://www.cs.cmu.edu/~roni/10601-f17/) | 82 | | Anima Anadkumar (Caltech) | [Foundations of Machine Learning and Statistical Inference](https://www.youtube.com/watch?v=Hn6giCdZmMg&list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | CS 165 | 83 | | Von Luxburg and Hennig(University of Tübingen) | [Probabilistic and Statistical Machine Learning](https://www.youtube.com/watch?v=jFcYpBOeCOQ&list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | [Course](https://www.tml.cs.uni-tuebingen.de/teaching/index.php) | 84 | | Patrick Winston (MIT) | [Artificial Intelligence](https://www.youtube.com/watch?v=_PwhiWxHK8o&list=PLnvKubj2-I2LhIibS8TOGC42xsD3-liux) | [6.034](https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/) | 85 | | Himabindu (Hima) Lakkaraju (Harvard) | [Machine Learning Explainability Workshop](https://www.youtube.com/playlist?list=PLoROMvodv4rPh6wa6PGcHH6vMG9sEIPxL) | Exaplainable workshop at Stanford | 86 | | Cynthia Rudin (Duke University) | [Intuition for the Algorithms of Machine Learning](https://www.youtube.com/watch?v=7o9TMQAHgkQ&list=PLNeXFnYrCJneoY_rKtWJy833YiMrCRi5f) | | 87 | | Jure Leskovec (Stanford) | [Machine Learning with Graphs](https://www.youtube.com/watch?v=JAB_plj2rbA&list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | [CS 224W](https://web.stanford.edu/class/cs224w/) | 88 | | David Sontag (MIT) | [Machine Learning for Healthcare](https://www.youtube.com/watch?v=vof7x8r_ZUA&list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | [6.S897](https://ocw.mit.edu/courses/6-s897-machine-learning-for-healthcare-spring-2019/) | 89 | | Manolis Kellis, James Galagan (MIT) | [Machine Learning in Genomics](https://www.youtube.com/watch?v=CTPs5HELLpo&list=PLypiXJdtIca6dEYlNoZJwBaz__CdsaoKJ) | [6.047/6.878](https://compbio.mit.edu/6047/) | 90 | | Percy Liang, Dorsa Sadigh (Stanford) | [Artificial Intelligence](https://www.youtube.com/watch?v=J8Eh7RqggsU&list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | [CS 221](https://stanford-cs221.github.io/autumn2019/#schedule) | 91 | | Nando de Freitas (UBC) | [Machine Learning](https://www.youtube.com/watch?v=w2OtwL5T1ow&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6) | [CS 540](https://www.cs.ubc.ca/~nando/540-2013/lectures.html) | 92 | | Nando de Freitas (UBC) | [Machine Learning Undergrad](https://www.youtube.com/watch?v=pid0lUH467o&list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | [CS 340](https://www.cs.ubc.ca/~nando/340-2012/lectures.php) | 93 | | Mohamed Abdelfattah (Cornell University) | [Machine Learning Hardware & Systems](https://www.youtube.com/watch?v=66HCPAcimGY&list=PL0mFAhrXqy9CuopJhAB8GVu_Oy7J0ery6) | [ECE 5545 (CS 5775)](https://abdelfattah-class.github.io/ece5545/) | 94 | | Kilian Weinberger (Cornell University) | [Machine Learning for Intelligent Systems](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | [CS 4780/CS 5780](https://www.cs.cornell.edu/courses/cs4780/2018fa/) | 95 | | Steve Brunton (University of Washington) | [Physics Informed Machine Learning](https://www.youtube.com/watch?v=JoFW2uSd3Uo&list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa) | | 96 | | Andreas Mueller (Columbia University) | [Applied Machine Learning](https://www.youtube.com/watch?v=d79mzijMAw0&list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | [COMS W4995](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) | 97 | | Volodymyr Kuleshov (Cornell University) | [Applied Machine Learning](https://www.youtube.com/watch?v=vcE9WGbi4QY&list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83) | [Cornell CS 5787](https://github.com/kuleshov/cornell-cs5785-2020-applied-ml) | 98 | | Chip Huyen (Stanford University) | [Machine Learning Systems Design]() | [CS 329S](https://stanford-cs329s.github.io) | 99 | 100 | ## Foundation models courses 101 | 102 | | Professor (Institute) | Course name (Link) | Course id | 103 | |-------------------------------------|------------------------------------------------------------------------------------------|------------------------------------------------------------------| 104 | | Percy Liang (Stanford) | [Large Language Models](https://stanford-cs324.github.io/winter2022/) | CS324 | 105 | | Div Garg (Stanford) | [Transformers](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM) | [CS25](https://web.stanford.edu/class/cs25/) | 106 | | Lei Li (CMU) | Large Language Model Systems | [11868](https://llmsystem.github.io/llmsystem2024spring/) | 107 | | Chenyan Xiong (CMU) | Large Language Models Methods and Applications | [11667](https://cmu-llms.org/calendar/) | 108 | | Dawn Song (UC Berkeley) | Understanding Large Language Models: Foundations and Safety | [CS194/294-267](https://rdi.berkeley.edu/understanding_llms/s24) | 109 | | Wenhu Chen (University of Waterloo) | CS 886: Recent Advances on Foundation Models | [CS 886](https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/) | 110 | 111 | 112 | 113 | ### Contributing 114 | Have anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/shantanu-ai/deep-learning-resources/pulls). --------------------------------------------------------------------------------