One of my favorite learning resources for understanding the math behind deep learning is “Math for Deep Learning” by Ronald T. Kneusel in No Starch Press. Deep Learning If you’re interested in quickly understanding how his algorithms work at a basic level, this book is for you. Understanding the treatment of this relatively short 316-page subject will enrich your knowledge and deepen your understanding of the algorithms that serve common problem domains such as computer vision, reinforcement learning, and NLP. Additionally, if you’re trying to understand how modern generative AI, generative pre-training transformers (GPT), and large language models like ChatGPT work, this book is a great first step (but There is much more to learn.) .
Here is the table of contents for the book:
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Possibilities
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differentiation
Chapter 8: Matrix Computation
Chapter 9: Neural Network Data Flow
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Chapters 1 through 4 are much better in nature and provide the reader with useful background information including topics such as Python Basics with NumPy, Probability Basics, and Statistics with Correlations and Hypothesis Testing. I will ask you Chapters 5 through 8 form the basis of the book, showing mathematical techniques underlying deep learning, including vectors, matrices, tensors, PCAs, SVDs, differential calculus, and matrix calculus. Chapter 9 focuses on convolutional neural networks (CNNs), which are used in computer vision problem domains. The most important chapters are Chapter 10 of Backprop and Chapter 11 of Gradient Descent. Readers should spend extra time studying these two chapters of his in detail, using mathematics and Python code.
Understanding mathematics is especially important. I recommend taking the time to do the math manually, following the guidance in the book (such as partial derivatives of the backprop loss function and non-linear activation functions like Sigmoid, ReLU, Tanh, etc.).
The best thing about this book is that after giving a detailed mathematical perspective on the topic, it also provides Python source code so you can try the calculations yourself. For example, there is a function for doing gradient descent.
The book is well structured and clearly explains the key mathematical concepts and techniques essential to understanding and applying deep learning algorithms. One of the book’s strengths is that it covers a wide range of topics, including linear algebra, calculus, probability theory, and optimization. This broad coverage makes it an ideal resource for beginners who may not have a good foundation in all these areas. Additionally, each topic is presented in a self-contained manner, allowing readers to focus on specific areas of interest rather than being overwhelmed with material.
The book consists of a logical progression, starting with the basics of linear algebra and progressing to more advanced topics such as matrix computation, eigenvalues and eigenvectors, and probability theory. Throughout the book, Kneusel uses clear, concise language and numerous examples and diagrams to help readers understand complex mathematical concepts.
One of the most valuable aspects of Mathematics for Deep Learning is the author’s emphasis on practical applications of mathematics. Kneusel provides many examples of how mathematics is used in deep learning algorithms to help readers understand the relevance of the material. In addition, exercises are provided at the end of each chapter to help readers solidify their understanding of the material.
This book is aimed at beginners, but assumes some familiarity with basic calculus and linear algebra. However, Kneusel provides a convenient appendix that reviews key concepts in these areas, allowing readers to quickly update their knowledge as needed.
Overall, “Math for Deep Learning” is an excellent resource for anyone seeking to gain a solid foundation in the mathematics underlying deep learning algorithms. The book is accessible, well-organized, and provides clear explanations and practical examples of key mathematical concepts. Highly recommended for anyone interested in this field.
C.Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist at insideBIGDATA. In addition to being a tech journalist, Daniel is also a data scientist, author, and educator consultant, and has served on many advisory boards for various start-ups.
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