Deep Learning from the Basics

Deep Learning from the Basics serves as your practical guide to understanding and implementing deep learning techniques. Through hands-on examples in Python, this book covers key concepts like neural networks, backpropagation, and modern training methods in an accessible way. By the end, you'll have the knowledge to create your own deep learning systems with confidence.

What this Book will help me do

• Understand the basics of deep learning and its applications.
• Implement foundational neural networks in Python.
• Explore advanced techniques like Batch Normalization and Dropout.
• Apply modern training algorithms like Adam for optimization.
• Develop deep learning models for real-world applications like image recognition and reinforcement learning.

Author(s)

Koki Saitoh and Shigeo Yushita are experienced professionals in data science and machine learning with a commitment to practical education. Their focus is on demystifying complex concepts and making them accessible and useful to learners at all levels. Their writing blends theory with hands-on practice, ensuring a rich learning experience for the reader.

Who is it for?

This book is ideal for data scientists, developers, and data analysts seeking to expand their expertise in deep learning. It is designed for readers with a foundational knowledge of Python and an interest in applying theory to practical coding challenges. With this book, you can take the next step in your machine learning journey.