About the Book
Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us.
Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter for the script files. You'll also learn how to utilize NumPy and Matplotlib in your deep learning applications. As you progress through the book, you'll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations before finally implementing a three-layer neural network and calculating multidimensional arrays.
By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning.
About the Authors
Koki Saitoh was born in Nagasaki, Japan in 1984. He graduated from the engineering department of the Tokyo Institute of Technology and completed a master's course at the Graduate School of Interdisciplinary Information Studies at the University of Tokyo. Currently, he conducts research and development in computer vision and machine learning. He has authored Deep Learning from the Basics series (Vol.1-3), which are published by O'Reilly, Japan.
Learning Objectives
- Use Python with minimum external libraries to implement deep learning programs
- Study the various deep learning and neural network theories
- Learn how to set initial values of weights
- Implement techniques such as batch normalization, dropout, and Adam
- Explore applications like automatic driving, image generation, and reinforcement learning
Audience
Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial but not essential.
Approach
This book takes a practical approach to deep learning. Exploring the concepts through guided practice, you will write code and implement mathematical algorithms from a programmer's point of view.
