Sebastian Klaas - Neural Network for Beginners: Build Deep Neural Networks and Develop Strong Fundamentals using Python’s NumPy, and Matplotlib (English Edition)

My lovely little daughters: Julie and Rene.
Your endless love and energy charge me every day.
And to my beautiful and patient wife Tifany.
That's all thanks to you.
You are the light of my life.

I love you

Sebastian Klaas, data science professional who has great organizational and communication skills. He enjoys solving problems and coming up with unique solutions. I have 10+ year of experience working in data consultancy, customer experience, product analytics, and survey analytics.

He is passionate about Big Data Analysis, Data Driven Decision Making, Data Mining, Data Wrangling, Data Modelling and Predictions, Forecasting the Future, Data Visualization.

Technical statistical and data analysis tools he has grip on includes R, Python, Microsoft Excel, Microsoft SQL.

Leonardo Machado is a full stack software engineer and a graduate of General Assembly's Software Engineering Immersive bootcamp. He loves creating mobile friendly applications and designing products that solve problems. He is specialized in JavaScript, React, MERN-stack apps, Python and Django.

There are a few people I want to thank for the support they have given me during the writing of this book. First and foremost, I would like to thank my parents for continuously encouraging me to write the book. I could have never completed this book without their support.

My gratitude also goes to the team at BPB Publications for being supportive enough to provide me quite a long time to finish the book and also giving us the opportunity and providing us the necessary support in writing this book.

We would like to thank our family members for the support they have provided for us to focus on the book during our personal time.

This book deals with profound learning and covers the knowledge needed to comprehend it gradually from the fundamentals, including what it is, what it means, and how the reader may understand the essential technologies as easily as possible.

So what are we gonna do to get deep learning better? Well, one of the greatest methods to do anything is to do practical activities such as creating a programme from scratch that encourages critical thinking when reading a source code. In this sense, "from scratch" means utilizing as few as possible external objects (such libraries and tools). The objective of this book is to use these "black boxes," the content of which is unknown, to start with a minimum level of fundamental information on which you may construct, analyze and execute in order for profound education programmes to be understandable and state-of-the-art. If you compare this book with a handbook for a car, it is not a manual that demonstrates how a car is driven; it is one that is focused on comprehending the concept of a car. It enables you to open the car's hood, to remove and study each part for its shape, function, and position before it is assembled and your model is constructed to its precise proportions and operations. This book is designed to make you feel like you can construct an automobile and familiarize yourself with the technology behind it. We shall utilize Python to carry out profound learning in this book. Python is a most popular and user-friendly programming language. It is ideal for the production of prototypes. You may quickly test your ideas and do different experiments while checking the outcomes. This book outlines the theoretical components of profound knowledge during the course of Python programmes. You may frequently detect, by reading and executing a source code, what you cannot grasp only by a mathematical expression or theoretical description. This book focuses on "engineering" comprehending profound learning through creating programmes. From a programmer's point of view, you will see much math and also many source codes.

The book consists of eight chapters, in which the reader will learn the following:

describes how to install and use Python.

will describe a perceptron and use one to solve easy problems.

provides an overview of neural networks and focuses on what distinguishes them.

we will be introduced to the method of using the gradient of a function, called a gradient method, to discover the smallest loss function value.

covers backpropagation, which is a more efficient way to calculate the gradients of weight parameters.

describes important ideas in neural network training, including the optimization techniques that are used to search for optimal weight parameters, the initial values of weight parameters, and the method for setting hyperparameters.

will detail the mechanisms of CNNs and how to implement them in Python.

will describe the characteristics, problems, and possibilities of deep learning, as well as an overview of current deep learning practices.

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