Learn · Foundations of Deep Learning: Concepts and Applications

Lecture 1 of 65Week 1 Introduction to Artificial Neural Networks

Overview of Machine Learning and Deep Learning

Prof. Sriram Ganapathy Prof. Ashwini Kodipalli Prof. Baishali Garai · IISc Bangalore RV University

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Foundations of Deep Learning: Concepts and Applications, IISc Bangalore RV University (at NPTEL)
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CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IISc Bangalore.
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All the lectures

  1. Overview of Machine Learning and Deep Learning Video
  2. Single Layer Perceptron Video
  3. Hands-on on Single Layer Perceptron Video
  4. Multi-Layer Perceptron Video
  5. Activation and Loss Functions Video
  6. Gradient descent algorithm Video
  7. Backpropagation in Neural Network Video
  8. Varients of Gradient descent and Momentum based techniques Video
  9. Regularization techniques in Neural Networks Video
  10. Hands-on on MLP for classification problem Video
  11. Fundamentals of Image representation and Image preprocessing and Data augmentation Video
  12. Introduction to Convolutional Neural Networks, Inspiration behind CNN, Key Components of CNN Video
  13. Convolution for RGB, Pooling Layer and Flatten Layer Video
  14. Batch Normalization Video
  15. Hands-on session on building simple CNN model Video
  16. Architecture and Implementation of AlexNet Video
  17. VGGNet (VGG16 and VGG19) and GoogleNet Video
  18. Introduction to ResNet (ResNet 34 and ResNet50) Video
  19. Introduction to Transfer Learning Video
  20. Hands-on on standard CNN architectures and transfer learning Video
  21. Hands-on on building and deep CNN ensemble model. Video
  22. Need for XAI, PCI, Occlusion method Video
  23. SHAP and LIME Video
  24. Guided backpropagation Video
  25. Grad-CAM and Class Activation Maps (CAMs) Video
  26. Hands-on implemenation of XAI methods Video
  27. Image segmentation Video
  28. U-Net with Attention and Evaluation Metric for Segmentation Video
  29. Object detection Video
  30. YOLO v1 for object detection Video
  31. Hands-on implemenation of Unet Video
  32. Creating .yaml file for the custom dataset Video
  33. Hands-on implemenation of YOLO Video
  34. Introduction to Recurrent Neural Networks Video
  35. Numerical Example for RNN Video
  36. Vanishing and exploding gradients in NN Video
  37. Backpropagation through time (BTT) Video
  38. Hands-on implementation of RNN model and its variants. Video
  39. Introduction to LSTM Video
  40. Gated Recurrent Unit Video
  41. LSTM-Encoder Decoder Structure Video
  42. LSTM with attention Video
  43. Hands-on implementation of LSTM Video
  44. Introduction to NLP Video
  45. Text Preprocessing and Text Representations Video
  46. Discrete and Distributed Word Representation Video
  47. Word Embeddings in NLP Video
  48. Hands-on implementation of RNN in NLP application Video
  49. Introduction to Autoencoders Video
  50. Types of Autoencoders Video
  51. VAE Architecture - Encoder part Video
  52. VAE Architecture - Decoder part Video
  53. Reparameterization Trick Video
  54. Hands-on implemenation of Autoencoders Video
  55. Foundations of Language Modeling and Transformers Video
  56. Transformer Architectures and Attention Mechanisms Video
  57. In-context learning and Self-Supervised Learning in LLMs Video
  58. Large Language Models: Tokenization, Generation, and Sampling Video
  59. Pre-training and Instruction Tuning of Large Language Models Video
  60. Reinforcement Learning for Aligning Large Language Models Video
  61. Reasoning, Retrieval, and Efficiency in Post-trained LLMs Video
  62. Evaluation, Benchmarking, and Impact of Foundation Models Video
  63. LLMs demo -TA Video
  64. Diffusion Models for Generative Modelling Video
  65. GANs and Diffusion models demo -TA Video