Description
Neural Networks from Scratch in Python is a book designed to teach readers how to build neural networks independently, without relying on libraries. This approach helps learners gain a deeper understanding of deep learning and its components. The book is accompanied by free tutorial videos and sample code available on YouTube, providing a multi-medium learning experience. The physical book is available in both softcover and hardcover formats, printed in full color to enhance the learning experience with images, charts, and Python syntax highlighting.
Read More
The book begins with an introduction to neural networks and machine learning algorithms, quickly moving into practical coding exercises. Readers will learn to code neurons, connect them in layers, program activation functions like ReLU and Softmax, calculate cross-entropy loss, and perform backpropagation with optimizers such as SGD and Adam. The book emphasizes coding in pure Python before introducing NumPy for mathematical operations, offering a dual learning benefit.
Supplementary animations and QR codes are included to clarify complex concepts, making the material more digestible. The book also provides access to an ebook version in PDF and Google Documents formats, allowing readers to highlight and comment for interactive learning and support. This feature enables direct assistance from the authors and the community, enhancing the learning experience.
Neural Networks from Scratch is ideal for those with basic Python and object-oriented programming knowledge, but no prior experience with neural networks or advanced mathematics is required. The book aims to equip readers with the skills to build and train neural networks from scratch, fostering a deeper understanding of data processing, activation functions, and model building.
- CategoryEducation & Research
Book Details
Build neural networks without libraries
Includes tutorial videos and sample code
Available in softcover and hardcover
Full-color printing with syntax highlighting
Covers neurons, layers, and activation functions
Teaches backpropagation and optimizers
Supplementary animations and QR codes
Interactive ebook with commenting feature
Who This Book Is For
- Beginner learning
- Deep learning education
- Python programming practice
- Interactive learning
- Supplementary tutorials
