Machine Learning Q and AI Beyond the Basics Book

The Supplementary Materials for the Machine Learning Q and AI book by Sebastian Raschka.

Please use the Discussions for any questions about the book!

About the Book

If you’ve locked down the basics of machine learning and AI and want a fun way to address lingering knowledge gaps, this book is for you. This rapid-fire series of short chapters addresses 30 essential questions in the field, helping you stay current on the latest technologies you can implement in your own work.

Each chapter of Machine Learning Q and AI asks and answers a central question, with diagrams to explain new concepts and ample references for further reading

• Multi-GPU training paradigms
• Finetuning transformers
• Differences between encoder- and decoder-style LLMs
• Concepts behind vision transformers
• Confidence intervals for ML
• And many more!

This book is a fully edited and revised version of Machine Learning Q and AI, which was available on Leanpub.

Reviews

“One could hardly ask for a better guide than Sebastian, who is, without exaggeration, the best machine learning educator currently in the field. On each page, Sebastian not only imparts his extensive knowledge but also shares the passion and curiosity that mark true expertise.”
-- Chris Albon, Director of Machine Learning, The Wikimedia Foundation

Links

• Preorder directly from No Starch press
• Preorder directly from Amazon
• Supplementary Materias and Discussions

Table of Contents

Title	URL Link	Supplementary Code
1	Embeddings, Representations, and Latent Space
2	Self-Supervised Learning
3	Few-Shot Learning
4	The Lottery Ticket Hypothesis
5	Reducing Overfitting with Data
6	Reducing Overfitting with Model Modifications
7	Multi-GPU Training Paradigms
8	The Keys to the Success of Transformers
9	Generative AI Models
10	Sources of Randomness	data-sampling.ipynb dropout.ipynb random-weights.ipynb
	PART II: COMPUTER VISION
11	Calculating the Number of Parameters	conv-size.ipynb
12	The Equivalence of Fully Connected and Convolutional Layers	fc-cnn-equivalence.ipynb
13	Large Training Sets for Vision Transformers
	PART III: NATURAL LANGUAGE PROCESSING
14	The Distributional Hypothesis
15	Data Augmentation for Text	backtranslation.ipynb noise-injection.ipynb sentence-order-shuffling.ipynb synonym-replacement.ipynb synthetic-data.ipynb word-deletion.ipynb word-position-swapping.ipynb
16	“Self”-Attention
17	Encoder- And Decoder-Style Transformers
18	Using and Finetuning Pretrained Transformers
19	Evaluating Generative Large Language Models	BERTScore.ipynb bleu.ipynb perplexity.ipynb rouge.ipynb
	PART IV: PRODUCTION AND DEPLOYMENT
20	Stateless And Stateful Training
21	Data-Centric AI
22	Speeding Up Inference
23	Data Distribution Shifts
	PART V: PREDICTIVE PERFORMANCE AND MODEL EVALUATION
24	Poisson and Ordinal Regression
25	Confidence Intervals	four-methods.ipynb four-methods-vs-true-value.ipynb
26	Confidence Intervals Versus Conformal Predictions	conformal_prediction.ipynb
27	Proper Metrics
28	The K in K-Fold Cross-Validation
29	Training and Test Set Discordance
30	Limited Labeled Data