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Udemy - Hands-on Deep Learning with PyTorch From Math to Modern AI 2026-10

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File Name
Size
1 - Course Introduction/1. Introduction.mp4
30 MB
2 - What is Deep Learning & Why it Matters Now/2. Section Introduction.mp4
22 MB
2 - What is Deep Learning & Why it Matters Now/3. AI → ML → Deep Learning — The family tree.mp4
54 MB
2 - What is Deep Learning & Why it Matters Now/4. Why Deep Learning exploded (data + compute + algorithms).mp4
52 MB
2 - What is Deep Learning & Why it Matters Now/5. Real-world wins image recognition, ChatGPT, AlphaFold.mp4
56 MB
2 - What is Deep Learning & Why it Matters Now/6. Demonstration - Setting up Google Colab & Installing Torch.mp4
74 MB
2 - What is Deep Learning & Why it Matters Now/7. Tensor Shape and Dimensions.mp4
36 MB
2 - What is Deep Learning & Why it Matters Now/8. Demonstration - Creating tensors-Finding its Shapes and Dimensions.mp4
14 MB
2 - What is Deep Learning & Why it Matters Now/9. Demonstration - Working with tensors Create, reshape, slice, move to GPU.mp4
20 MB
3 - Math You Need for Deep Learning/10. Section Introduction.mp4
9.8 MB
3 - Math You Need for Deep Learning/11. Linear algebra basics — vectors, matrices, dot product.mp4
27 MB
3 - Math You Need for Deep Learning/12. Demonstration - Matrix operations in NumPy & PyTorch.mp4
106 MB
3 - Math You Need for Deep Learning/13. Demonstration - Tensor Manipulation and Combining Tensors.mp4
103 MB
3 - Math You Need for Deep Learning/14. Functions, slopes & the idea of a derivative.mp4
47 MB
3 - Math You Need for Deep Learning/15. Partial derivatives & gradients — what they mean visually.mp4
52 MB
3 - Math You Need for Deep Learning/16. Demonstration - Visualising gradients with matplotlib.mp4
73 MB
3 - Math You Need for Deep Learning/17. The chain rule — why it matters for neural networks.mp4
39 MB
3 - Math You Need for Deep Learning/18. Sigmoid & Softmax math unpacked.mp4
37 MB
3 - Math You Need for Deep Learning/19. Demonstration - Plotting activation functions & their derivatives.mp4
78 MB
4 - How Neural Networks Learn/20. Section Introduction.mp4
13 MB
4 - How Neural Networks Learn/21. From biological neuron to artificial neuron — visual walkthrough.mp4
61 MB
4 - How Neural Networks Learn/22. The perceptron — the simplest neural network.mp4
37 MB
4 - How Neural Networks Learn/23. Demonstration - Build a perceptron from scratch with NumPy.mp4
85 MB
4 - How Neural Networks Learn/24. The forward pass weighted sums, bias, activation layer by layer.mp4
39 MB
4 - How Neural Networks Learn/25. Loss functions MSE for regression, Cross-Entropy for classification.mp4
50 MB
4 - How Neural Networks Learn/26. Gradient Descent - Rolling down the Hill.mp4
42 MB
4 - How Neural Networks Learn/27. Backpropagation — chain rule applied to a network (step by step).mp4
51 MB
4 - How Neural Networks Learn/28. Demonstration-How a Neural Network Learns From Forward Pass to Gradient Descent.mp4
103 MB
4 - How Neural Networks Learn/29. Epochs and Batches.mp4
39 MB
4 - How Neural Networks Learn/30. Demonstration-Training a 2layer network on MNIST & visualising training progress.mp4
70 MB
5 - Training a Network That Actually Works/31. Section Introduction.mp4
20 MB
5 - Training a Network That Actually Works/32. Overfitting vs underfitting — the bias-variance tradeoff.mp4
39 MB
5 - Training a Network That Actually Works/33. L1 & L2 Regularization — math & intuition.mp4
36 MB
5 - Training a Network That Actually Works/34. Dropout — randomly switching off neurons.mp4
70 MB
5 - Training a Network That Actually Works/35. Batch Normalization — why it speeds up training.mp4
78 MB
5 - Training a Network That Actually Works/36. Demonstration - L2 Regularization effect on overfitting in PyTorch (Part-1).mp4
30 MB
5 - Training a Network That Actually Works/37. Demonstration - L2 Regularization effect on overfitting in PyTorch (Part-2).mp4
82 MB
5 - Training a Network That Actually Works/38. Demonstration - L2 Regularization effect on overfitting in PyTorch (Part-3).mp4
64 MB
5 - Training a Network That Actually Works/39. Demonstration - L2 Regularization effect on overfitting in PyTorch (Part-4).mp4
51 MB
5 - Training a Network That Actually Works/40. Demonstration - Dropout & Batch Norm layers in PyTorch.mp4
106 MB
5 - Training a Network That Actually Works/41. Optimizers deep dive SGD, Momentum, RMSProp, Adam.mp4
66 MB
5 - Training a Network That Actually Works/42. Learning rate scheduling — step decay, cosine annealing.mp4
51 MB
5 - Training a Network That Actually Works/43. Demonstration - Compare SGD vs Adam on same dataset.mp4
111 MB
5 - Training a Network That Actually Works/44. Demonstration - Customer Churn Prediction end-to-end.mp4
66 MB
5 - Training a Network That Actually Works/45. Demonstration - Early stopping implementation in PyTorch.mp4
58 MB
6 - Convolutional Neural Networks (CNNs)/46. Section Introduction.mp4
20 MB
6 - Convolutional Neural Networks (CNNs)/47. Why regular CNNs fail on images — the pixel problem.mp4
33 MB
6 - Convolutional Neural Networks (CNNs)/48. Convolution operation — filters, stride, padding explained visually.mp4
42 MB
6 - Convolutional Neural Networks (CNNs)/49. Feature maps and what filters actually learn.mp4
40 MB
6 - Convolutional Neural Networks (CNNs)/50. Pooling layers max pooling & average pooling.mp4
46 MB
6 - Convolutional Neural Networks (CNNs)/51. Demonstration - Build a CNN layer by layer in PyTorch.mp4
93 MB
6 - Convolutional Neural Networks (CNNs)/52. Famous architectures AlexNet → VGG → ResNet → EfficientNet.mp4
60 MB
6 - Convolutional Neural Networks (CNNs)/53. Transfer learning — why train from scratch when you don't have to.mp4
48 MB
6 - Convolutional Neural Networks (CNNs)/54. Demonstration - Transfer Learning Fine-tune ResNet50 on custom images.mp4
70 MB
6 - Convolutional Neural Networks (CNNs)/55. Demonstration - Dog vs Cat Classifier end to end.mp4
96 MB
6 - Convolutional Neural Networks (CNNs)/56. Demonstration - Visualising CNN feature maps & filters.mp4
91 MB
7 - Sequence Models — RNNs & LSTMs/57. Section Introduction.mp4
13 MB
7 - Sequence Models — RNNs & LSTMs/58. What makes sequences special — why order matters.mp4
33 MB
7 - Sequence Models — RNNs & LSTMs/59. RNN architecture — the hidden state loop explained.mp4
55 MB
7 - Sequence Models — RNNs & LSTMs/60. The vanishing gradient problem in RNNs — math intuition.mp4
63 MB
7 - Sequence Models — RNNs & LSTMs/61. LSTM — the memory cell, forget gate, input gate, output gate.mp4
34 MB
7 - Sequence Models — RNNs & LSTMs/62. GRU — a lighter alternative to LSTM.mp4
44 MB
7 - Sequence Models — RNNs & LSTMs/63. Demonstration - Build an RNN from scratch in PyTorch.mp4
69 MB
7 - Sequence Models — RNNs & LSTMs/64. Demonstration - Build an LSTM in PyTorch.mp4
66 MB
7 - Sequence Models — RNNs & LSTMs/65. Demonstration - Sentiment Analysis on Movie Reviews with LSTM.mp4
91 MB
7 - Sequence Models — RNNs & LSTMs/66. Demonstration - Time Series Forecasting with LSTM.mp4
71 MB
8 - Transformers & Attention - How GPT Works/67. Section Introduction.mp4
22 MB
8 - Transformers & Attention - How GPT Works/68. The encoder-decoder bottleneck — why attention was invented.mp4
37 MB
8 - Transformers & Attention - How GPT Works/69. Attention mechanism — query, key, value intuition.mp4
68 MB
8 - Transformers & Attention - How GPT Works/70. Self-attention math scaled dot-product attention step by step.mp4
60 MB
8 - Transformers & Attention - How GPT Works/71. Demonstration - Implement scaled dot-product attention in PyTorch.mp4
67 MB
8 - Transformers & Attention - How GPT Works/72. Multi-head attention — why multiple heads.mp4
59 MB
8 - Transformers & Attention - How GPT Works/73. Positional encoding — giving the model a sense of order.mp4
62 MB
8 - Transformers & Attention - How GPT Works/74. Transformer encoder & decoder architecture full walkthrough.mp4
78 MB
8 - Transformers & Attention - How GPT Works/75. BERT vs GPT — encoders vs decoders, masked vs causal attention.mp4
46 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/76. Section Introduction.mp4
17 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/77. Autoencoders — encoder, bottleneck, decoder explained.mp4
77 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/78. Demonstration - Build an Autoencoder in PyTorch.mp4
104 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/79. Demonstration - Anomaly Detection with Autoencoder on Credit Card Data Part-1.mp4
52 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/80. Demonstration - Anomaly Detection with Autoencoder on Credit Card Data Part-2.mp4
38 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/81. Demonstration - Anomaly Detection with Autoencoder on Credit Card Data Part-3.mp4
102 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/82. Demonstration - Anomaly Detection with Autoencoder on Credit Card Data Part-4.mp4
57 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/83. GANs — generator vs discriminator, the minimax game.mp4
129 MB
9 - Generative Deep Learning - Autoencoders, GANs & Diffusion Models/84. Diffusion models — how Stable Diffusion works (conceptual overview).mp4
118 MB
10 - Capstone Project - From Dense Networks to BERT/85. Section Introduction.mp4
24 MB
10 - Capstone Project - From Dense Networks to BERT/86. Problem Definition & Dataset Overview.mp4
31 MB
10 - Capstone Project - From Dense Networks to BERT/87. Demonstration - Data Loading & Preprocessing.mp4
154 MB
10 - Capstone Project - From Dense Networks to BERT/88. Demonstration - Dense Neural Network (Baseline DL Model).mp4
102 MB
10 - Capstone Project - From Dense Networks to BERT/89. Demonstration - Regularized Neural Network.mp4
99 MB
10 - Capstone Project - From Dense Networks to BERT/90. Demonstration - LSTM Based Sequence Model.mp4
66 MB
10 - Capstone Project - From Dense Networks to BERT/91. Demonstration - Transformer Based Model (BERT Fine-Tuning).mp4
114 MB
10 - Capstone Project - From Dense Networks to BERT/92. Demonstration - Model Comparison, Evaluation and Visualization.mp4
91 MB
10 - Capstone Project - From Dense Networks to BERT/93. Key Insights.mp4
7.5 MB
11 - Course Conclusion/94. Course Conclusion.mp4
22 MB