The Little Book of Deep Learning
This book is a short introduction to deep learning for readers with
a STEM background, originally designed to be read on a phone
screen. It is distributed under a non-commercial Creative Commons
license and was downloaded
500'000 times in
eight months. Here is the bibtex entry.
You can either:
The version sold on Amazon and other on-line platforms for $40
is "unauthorized": someone stole the content and sells
it. Preventing this would require to sue and I have better things to
do with my time.
Updates
V1.1 to V1.1.1 (Sep 20, 2023)
- Section 4.2. Added a paragraph about the equivariance of
convolution layers.
- Section 5.3. Fixed the description of the original
Transformer, and modified Figures 5.6, 5.7, 5.8, and 5.9
accordingly.
V1 to V1.1 (Sep 8, 2023)
- Miscellaneous. Fixed minor typos and phrasings.
- Section 1.3. Reformulated the text to clarify that overfitting is not
particularly related to noise, but to any properties specific to the
training set, as it is the case on the Figure 1.2.
- Section 3.2. Clarified the phrasing and changed the
Figure 3.1.
- Section 3.4. Fixed the indexing of the mappings in the example of a
composition.
- Section 3.7. Fixed the label "1TWh" in Figure 3.7, that should be
"1GWh".
- Section 4.5. Added a figure to illustrate the functioning of 2D
dropout.
- Section 4.6. Changed the Figure 4.8 so that in the top
part illustrating the re-scaling / translating after
normalization, the highlighted sub-blocks correspond to groups
of activations that are re-scaled / translated with the same
factor / bias.
- Section 6.6. Restricted the Figure 6.4. to three
sub-images to make the text more legible.
- Section 7.1. Added two paragraphs to introduce the
notion of Reinforcement Learning from Human Feedback.
- The missing bits. Removed the fine-tuning
sub-section, since most of it was moved to Section 7.1.
