Python for Data Analysis: Master the Basics of Data Analysis in Python Using Numpy, Pandas and Ipython, Samuel Burns
Автор: Samuel Burns Название: Python Deep Learning: Develop Your First Neural Network in Python Using Tensorflow, Keras, and Pytorch ISBN: 1092562222 ISBN-13(EAN): 9781092562225 Издательство: Неизвестно Цена: 17230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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 ObjectivesThe 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.
Автор: Samuel Burns Название: Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn and Tensorflow ISBN: 1090434162 ISBN-13(EAN): 9781090434166 Издательство: Неизвестно Цена: 17230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Get your copy NOW Why this guide is the best one for Data Scientist? Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics.
A simple language has been used.
Many examples have been given, both theoretically and programmatically.
Screenshots showing program outputs have been added.
The book is written chronologically, in a step-by-step manner.Book Objectives: The Aims and Objectives of the Book:
To help you understand the basics of machine learning and deep learning.
Understand the various categories of machine learning algorithms.
To help you understand how different machine learning algorithms work.
You will learn how to implement various machine learning algorithms programmatically in Python.
To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.
To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.
Who this Book is for?Here are the target readers for this book:
Anybody who is a complete beginner to machine learning in Python.
Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.
Professionals in data science.
Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.
Students and academicians, especially those focusing on 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
Numpy
Pandas
Matplotlib
The Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning.
What is inside the book:
Getting Started
Environment Setup
Using Scikit-Learn
Linear Regression with Scikit-Learn
k-Nearest Neighbors Algorithm
K-Means Clustering
Support Vector Machines
Neural Networks with Scikit-learn
Random Forest Algorithm
Using TensorFlow
Recurrent Neural Networks with TensorFlow
Linear Classifier
This book will teach you machine learning classifiers using scikit-learn and tenserflow . The book provides a great overview of functions you can use to build a support vector machine, decision tree, perceptron, and k-nearest neighbors. Thanks of this book you will be able to set up a learning pipeline that handles input and output data, pre-processes it, selects meaningful features, and applies a classifier on it. This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Concepts and the background of these concepts are explained clearly in this tutorial.
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