Deep Learning for Coders with fastai & PyTorch

My notes from the book Deep Learning for Coders with fastai & PyTorch: AI Applications without a PhD by Jeremy Howard and Sylvain Gugger.
Published

March 14, 2022

Modified

September 12, 2024

Notes on fastai Book Ch. 1

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fastai
notes
pytorch
Chapter 1 covers the history of artificial neural networks, approaches for learning ML/DL, and using the fast.ai library for multiple deep learning tasks.
Mar 14, 2022
35 min

Notes on fastai Book Ch. 2

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fastai
notes
pytorch
Chapter 2 covers how to train an image classification model using a custom dataset and turn it into an online application.
Mar 14, 2022
23 min

Notes on fastai Book Ch. 3

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ethics
fastai
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Chapter 3 defines data ethics, introduces essential topics in data ethics, and explains how to identify and address ethical issues.
Mar 14, 2022
16 min

Notes on fastai Book Ch. 4

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fastai
notes
pytorch
Chapter 4 covers broadcasting, stochastic gradient descent, the MNIST loss function, and the sigmoid activation functions.
Mar 14, 2022
84 min

Notes on fastai Book Ch. 5

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fastai
notes
pytorch
Chapter 5 covers creating a custom DataBlock for an image classifier, pre-sizing, cross-entropy loss, model interpretation, picking learning rates, transfer learning, and discriminative learning rates.
Mar 14, 2022
33 min

Notes on fastai Book Ch. 6

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fastai
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pytorch
Chapter 6 covers multi-label classification and image regression.
Mar 14, 2022
20 min

Notes on fastai Book Ch. 7

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fastai
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pytorch
Chapter 7 covers data normalization, progressive resizing, test-time augmentation, mixup, and label smoothing.
Mar 14, 2022
26 min

Notes on fastai Book Ch. 8

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fastai
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pytorch
Chapter 8 provides a deep dive into collaborative filtering.
Mar 28, 2022
43 min

Notes on fastai Book Ch. 09

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fastai
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pytorch
Chapter 9 provides a deep dive into tabular modeling.
Mar 29, 2022
58 min

Notes on fastai Book Ch. 10

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fastai
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pytorch
Chapter 10 covers text preprocessing and training an RNN for text classification.
Mar 29, 2022
49 min

Notes on fastai Book Ch. 11

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fastai
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pytorch
Chapter 11 covers data munging with fast.ai’s mid-level data API.
Mar 29, 2022
50 min

Notes on fastai Book Ch. 12

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fastai
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pytorch
Chapter 12 covers building and training an LSTM from scratch.
Mar 29, 2022
49 min

Notes on fastai Book Ch. 13

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fastai
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pytorch
Chapter 13 provides a deep dive into convolutional neural networks.
Mar 29, 2022
59 min

Notes on fastai Book Ch. 14

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fastai
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pytorch
Chapter 14 covers building a ResNet from scratch.
Mar 29, 2022
43 min

Notes on fastai Book Ch. 15

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fastai
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pytorch
Chapter 15 provides a deep dive into different application architectures in the fast.ai library.
Mar 29, 2022
19 min

Notes on fastai Book Ch. 16

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fastai
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pytorch
Chapter 16 covers momentum, RMSProp, Adam, decoupled weight decay, and fast.ai callbacks.
Mar 29, 2022
36 min

Notes on fastai Book Ch. 17

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fastai
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pytorch
Chapter 17 covers building a neural network from the foundations.
Mar 29, 2022
33 min

Notes on fastai Book Ch. 18

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fastai
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pytorch
Chapter 18 covers interpreting convolutional neural networks using class-activation maps.
Mar 29, 2022
13 min

Notes on fastai Book Ch. 19

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fastai
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pytorch
Chapter 19 covers creating a fast.ai Learner from scratch.
Mar 29, 2022
47 min
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