Inside Deep Learning, Video Edition - Edward Raff

Inside Deep Learning, Video Edition - Edward Raff is your go-to guide for mastering deep learning using the PyTorch framework. This video series helps you understand and implement deep learning techniques to solve real-world data problems. You’ll learn to choose the right components, train and evaluate models, and fine-tune them for maximum performance. The content is presented in clear, simple language, making complex concepts easy to grasp, even if you're new to the field.

Deep learning can seem like a mysterious black box, but it doesn’t have to be. This video series breaks down how models and algorithms work, giving you the confidence to understand and explain your projects. You don’t need to be a math expert or a professional data scientist to follow along. The videos offer practical insights and explanations that make deep learning accessible to everyone.

Through plain language, annotated code, and practical examples, Inside Deep Learning, Video Edition - Edward Raff explains deep learning concepts. You’ll explore various neural network types without getting bogged down in complex math. Each solution is designed to be run with readily available GPU hardware, so you can apply what you learn immediately. By the end of the series, you'll have the knowledge and skills to use deep learning effectively in your projects.

Inside Deep Learning, Video Edition - Edward Raff Table of Contents:

1. Part 1. Foundational methods
2. Chapter 1. The mechanics of learning
3. Chapter 1. The world as tensors
4. Chapter 1. Automatic differentiation
5. Chapter 1. Optimizing parameters
6. Chapter 1. Loading dataset objects
7. Chapter 1. Summary
8. Chapter 2. Fully connected networks
9. Chapter 2. Building our first neural network
10. Chapter 2. Classification problems
11. Chapter 2. Better training code
12. Chapter 2. Training in batches
13. Chapter 2. Summary
14. Chapter 3. Convolutional neural networks
15. Chapter 3. What are convolutions?
16. Chapter 3. How convolutions benefit image processing
17. Chapter 3. Putting it into practice: Our first CNN
18. Chapter 3. Adding pooling to mitigate object movement
19. Chapter 3. Data augmentation
20. Chapter 3. Summary
21. Chapter 4. Recurrent neural networks
22. Chapter 4. RNNs in PyTorch
23. Chapter 4. Improving training time with packing
24. Chapter 4. More complex RNNs
25. Chapter 4. Summary
26. Chapter 5. Modern training techniques
27. Chapter 5. Learning rate schedules
28. Chapter 5. Making better use of gradients
29. Chapter 5. Hyperparameter optimization with Optuna
30. Chapter 5. Summary
31. Chapter 6. Common design building blocks
32. Chapter 6. Normalization layers: Magically better convergence
33. Chapter 6. Skip connections: A network design pattern
34. Chapter 6. 1 × 1 Convolutions: Sharing and reshaping information in channels
35. Chapter 6. Residual connections
36. Chapter 6. Long short-term memory RNNs
37. Chapter 6. Summary
38. Part 2. Building advanced networks
39. Chapter 7. Autoencoding and self-supervision
40. Chapter 7. Designing autoencoding neural networks
41. Chapter 7. Bigger autoencoders
42. Chapter 7. Denoising autoencoders
43. Chapter 7. Autoregressive models for time series and sequences
44. Chapter 7. Summary
45. Chapter 8. Object detection
46. Chapter 8. Transposed convolutions for expanding image size
47. Chapter 8. U-Net: Looking at fine and coarse details
48. Chapter 8. Object detection with bounding boxes
49. Chapter 8. Using the pretrained Faster R-CNN
50. Chapter 8. Summary
51. Chapter 9. Generative adversarial networks
52. Chapter 9. Mode collapse
53. Chapter 9. Wasserstein GAN: Mitigating mode collapse
54. Chapter 9. Convolutional GAN
55. Chapter 9. Conditional GAN
56. Chapter 9. Walking the latent space of GANs
57. Chapter 9. Ethics in deep learning
58. Chapter 9. Summary
59. Chapter 10. Attention mechanisms
60. Chapter 10. Adding some context
61. Chapter 10. Putting it all together: A complete attention mechanism with context
62. Chapter 10. Summary
63. Chapter 11. Sequence-to-sequence
64. Chapter 11. Machine translation and the data loader
65. Chapter 11. Inputs to Seq2Seq
66. Chapter 11. Seq2Seq with attention
67. Chapter 11. Summary
68. Chapter 12. Network design alternatives to RNNs
69. Chapter 12. Averaging embeddings over time
70. Chapter 12. Pooling over time and 1D CNNs
71. Chapter 12. Positional embeddings add sequence information to any model
72. Chapter 12. Transformers: Big models for big data
73. Chapter 12. Summary
74. Chapter 13. Transfer learning
75. Chapter 13. Transfer learning and training with CNNs
76. Chapter 13. Learning with fewer labels
77. Chapter 13. Pretraining with text
78. Chapter 13. Summary
79. Chapter 14. Advanced building blocks
80. Chapter 14. Improved residual blocks
81. Chapter 14. MixUp training reduces overfitting
82. Chapter 14. Summary
83. Appendix. Setting up Colab

Who is this course for?

1. Designed for Python programmers with basic machine learning skills.

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