Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
- 3h 33m
- Sebastian Raschka
- No Starch Press
- 2024
If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about.
Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises.
WHAT'S INSIDE:
FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts.
WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing.
PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more.
You’ll also explore how to:
- Manage the various sources of randomness in neural network training
- Differentiate between encoder and decoder architectures in large language models
- Reduce overfitting through data and model modifications
- Construct confidence intervals for classifiers and optimize models with limited labeled data
- Choose between different multi-GPU training paradigms and different types of generative AI models
- Understand performance metrics for natural language processing
- Make sense of the inductive biases in vision transformers
If you’ve been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.
About the Author
Sebastian Raschka, PhD, is a machine learning and AI researcher with a passion for education. As Lead AI Educator at Lightning AI, he is excited about making AI and deep learning more accessible. Raschka previously was Assistant Professor of Statistics at the University of Wisconsin-Madison, where he specialized in researching deep learning and machine learning, and is the author of the bestselling books Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn. You can find out more about his research on his website at https://sebastianraschka.com.
In this Book
-
Foreword
-
Introduction
-
Embeddings, Latent Space, and Representations
-
Self-Supervised Learning
-
Few-Shot Learning
-
The Lottery Ticket Hypothesis
-
Reducing Overfitting with Data
-
Reducing Overfitting with Model Modifications
-
Multi-GPU Training Paradigms
-
The Success of Transformers
-
Generative AI Models
-
Sources of Randomness
-
Calculating the Number of Parameters
-
Fully Connected and Convolutional Layers
-
Large Training Sets for Vision Transformers
-
The Distributional Hypothesis
-
Data Augmentation for Text
-
Self-Attention
-
Encoder- and Decoder-Style Transformers
-
Using and Fine-Tuning Pretrained Transformers
-
Evaluating Generative Large Language Models
-
Stateless and Stateful Training
-
Data-Centric AI
-
Speeding up Inference
-
Data Distribution Shifts
-
Poisson and Ordinal Regression
-
Confidence Intervals
-
Confidence Intervals Vs. Conformal Predictions
-
Proper Metrics
-
The k in k-Fold Cross-Validation
-
Training and Test Set Discordance
-
Limited Labeled Data
-
Afterword
