Build your Own Neural Network today. Through easy-to-follow instruction and examples, you’ll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on big and boring textbooks, we recommend getting the same pieces of information for a fraction of the cost. So
Get Your Copy Now!!
Why this book?Book Objectives
The following are the objectives of this book:- To help you understand deep learning in detail
- To help you know how to get started with deep learning in Python by setting up the coding environment.
- To help you transition from a deep learning Beginner to a Professional.
- To help you learn how to develop a complete and functional artificial neural network model in Python on your own.
Who this Book is for?
The author targets the following groups of people:- Anybody who is a complete beginner to deep learning with Python.
- Anybody in need of advancing their Python for deep learning skills.
- Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning.
What do you need for this Book?
You are required to have installed the following on your computer:- Python 3.X.
- TensorFlow .
- Keras .
- PyTorch
The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest. What is inside the book?
- What is Deep Learning?
- An Overview of Artificial Neural Networks.
- Exploring the Libraries.
- Installation and Setup.
- TensorFlow Basics.
- Deep Learning with TensorFlow.
- Keras Basics.
- PyTorch Basics.
- Creating Convolutional Neural Networks with PyTorch.
- Creating Recurrent Neural Networks with PyTorch.
From the back cover.
Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.
Build your Own Neural Network today. Through easy-to-follow instruction and examples, you’ll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on big and boring textbooks, we recommend getting the same pieces of information for a fraction of the cost. So
Get Your Copy Now!!
Why this book?
Book Objectives
The following are the objectives of this book:- To help you understand deep learning in detail
- To help you know how to get started with deep learning in Python by setting up the coding environment.
- To help you transition from a deep learning Beginner to a Professional.
- To help you learn how to develop a complete and functional artificial neural network model in Python on your own.
Who this Book is for?
The author targets the following groups of people:
- Anybody who is a complete beginner to deep learning with Python.
- Anybody in need of advancing their Python for deep learning skills.
- Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning.
What do you need for this Book?
You are required to have installed the following on your computer:
- Python 3.X.
- TensorFlow .
- Keras .
- PyTorch
The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest.
What is inside the book?
- What is Deep Learning?
- An Overview of Artificial Neural Networks.
- Exploring the Libraries.
- Installation and Setup.
- TensorFlow Basics.
- Deep Learning with TensorFlow.
- Keras Basics.
- PyTorch Basics.
- Creating Convolutional Neural Networks with PyTorch.
- Creating Recurrent Neural Networks with PyTorch.
From the back cover.
Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.
