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1. 1▶Graph Neural Networks - Lecture 15 - Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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2. 2▶Systems Genetics - Lecture 14 - Deep Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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3. 3▶Disease Circuitry Dissection GWAS - Lecture 12 - Deep Learning in Life Science (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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4. 4▶GWAS mechanism - Lecture 13 - Deep Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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5. 5▶Dimensionality Reduction - Lecture 11 - Deep Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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6. 6▶Single Cell Genomics - Lecture 10 - Deep Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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7. 7▶Gene Expression Prediction - Lecture 09 - Deep Learning in Life Sciences (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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8. 8▶Regulatory Genomics - Deep Learning in Life Sciences - Lecture 07 (Spring 2021)MIT Deep Learning in Life Sciences - Spring 2021Notes
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9. 9▶Deep Learning for Regulatory Genomics - Regulator binding, Transcription Factors TFsMIT Deep Learning in Life Sciences - Spring 2021Notes
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10. 10▶Generative Models, Adversarial Networks GANs, Variational Autoencoders VAEs, Representation LearningMIT Deep Learning in Life Sciences - Spring 2021Notes
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11. 11▶Lecture 15 | Efficient Methods and Hardware for Deep LearningStanford University CS231n, Spring 2017Notes
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12. 12▶Lecture 15 | Efficient Methods and Hardware for Deep LearningLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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13. 13▶Lecture 14 | Deep Reinforcement LearningStanford University CS231n, Spring 2017Notes
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14. 14▶Lecture 14 | Deep Reinforcement LearningLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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15. 15▶Lecture 13 | Generative ModelsStanford University CS231n, Spring 2017Notes
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16. 16▶Lecture 13 | Generative ModelsLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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17. 17▶Lecture 12 | Visualizing and UnderstandingStanford University CS231n, Spring 2017Notes
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18. 18▶Lecture 12 | Visualizing and UnderstandingLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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19. 19▶Lecture 11 | Detection and SegmentationStanford University CS231n, Spring 2017Notes
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20. 20▶Lecture 11 | Detection and SegmentationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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21. 21▶Lecture 9 | CNN ArchitecturesStanford University CS231n, Spring 2017Notes
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22. 22▶Lecture 9 | CNN ArchitecturesLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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23. 23▶Lecture 10 | Recurrent Neural NetworksStanford University CS231n, Spring 2017Notes
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24. 24▶Lecture 10 | Recurrent Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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25. 25▶Lecture 8 | Deep Learning SoftwareStanford University CS231n, Spring 2017Notes
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26. 26▶Lecture 8 | Deep Learning SoftwareLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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27. 27▶Lecture 7 | Training Neural Networks IIStanford University CS231n, Spring 2017Notes
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28. 28▶Lecture 7 | Training Neural Networks IILecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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29. 29▶Lecture 6 | Training Neural Networks IStanford University CS231n, Spring 2017Notes
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30. 30▶Lecture 6 | Training Neural Networks ILecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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31. 31▶Lecture 5 | Convolutional Neural NetworksStanford University CS231n, Spring 2017Notes
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32. 32▶Lecture 5 | Convolutional Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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33. 33▶Lecture 4 | Introduction to Neural NetworksStanford University CS231n, Spring 2017Notes
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34. 34▶Lecture 4 | Introduction to Neural NetworksLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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35. 35▶Lecture 3 | Loss Functions and OptimizationStanford University CS231n, Spring 2017Notes
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36. 36▶Lecture 3 | Loss Functions and OptimizationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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37. 37▶Lecture 2 | Image ClassificationStanford University CS231n, Spring 2017Notes
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38. 38▶Lecture 2 | Image ClassificationLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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39. 39▶Lecture 1 | Introduction to Convolutional Neural Networks for Visual RecognitionStanford University CS231n, Spring 2017Notes
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40. 40▶Lecture 1 | Introduction to Convolutional Neural Networks for Visual RecognitionLecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)Notes
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41. 41▶MIT 6.S191 Lecture 3: Convolutional Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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42. 42▶MIT 6.S191 Lecture 4: Deep Generative ModelsMIT 6.S191: Introduction to Deep LearningNotes
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43. 43▶MIT 6.S191 Lecture 6: Deep Reinforcement LearningMIT 6.S191: Introduction to Deep LearningNotes
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44. 44▶MIT 6.S191 Lecture 5 Multimodal Deep LearningMIT 6.S191: Introduction to Deep LearningNotes
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45. 45▶MIT 6.S191 Lecture 2: Sequence Modeling with Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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46. 46▶MIT 6.S191 Lecture 1: Intro to Deep LearningMIT 6.S191: Introduction to Deep LearningNotes
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