Python for Beginners: Ride the Wave of Artificial Intelligence and Machine Learning with This Crash Course on Python Programming, Deepening, Python in Deep Learning
Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.
Discover The Incredible World Of Machine Learning With This Amazing Guide
Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above questions, you have come to the right place.
Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it?
Apart from this, you will also learn more about:
The Different Types Of Learning Algorithm That You Can Expect To Encounter
The Numerous Applications Of Machine Learning And Deep Learning
The Best Practices For Picking Up Neural Networks
What Are The Best Languages And Libraries To Work With
The Various Problems That You Can Solve With Machine Learning Algorithms
And much more...
Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network?
So, what are you waiting for? Grab a copy of this book now
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.
Автор: Graesser Laura Harding, Wah Loon Keng Название: Deep Reinforcement Learning in Python: A Hands-On Introduction ISBN: 0135172381 ISBN-13(EAN): 9780135172384 Издательство: Pearson Education Рейтинг: Цена: 7522.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
Understand each key aspect of a deep RL problem
Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
Understand how algorithms can be parallelized synchronously and asynchronously
Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
Explore algorithm benchmark results with tuned hyperparameters
Understand how deep RL environments are designed
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Автор: Galea, Alex Capelo, Luis Название: Applied deep learning with python ISBN: 1789804744 ISBN-13(EAN): 9781789804744 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Getting started with data science can be overwhelming, even for experienced developers. In this two-part, hands-on book we`ll show you how to apply your existing understanding of the Python language to this new and exciting field that`s full of new opportunities (and high expectations)!
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: Pattanayak Santanu Название: Intelligent Projects Using Python ISBN: 1788996925 ISBN-13(EAN): 9781788996921 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your own intelligent applications for smart ...
Автор: Vasilev Ivan Название: Advanced Deep Learning with Python ISBN: 178995617X ISBN-13(EAN): 9781789956177 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.
Описание: This book will help you successfully implement deep learning in Python to create smart web applications from scratch. You will learn how deep learning can transform a simple web app into a smart, business-friendly product. You will also develop neural networks using open-source libraries and also integrate them with different web stack front-ends.
Автор: Julian David Название: Deep Learning with PyTorch Quick Start Guide ISBN: 1789534097 ISBN-13(EAN): 9781789534092 Издательство: Неизвестно Рейтинг: Цена: 6068.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
DO YOU WANT TO LEARN THE BASICS OF PYTHON PROGRAMMING QUICKLY?
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it.
Some of the topics that we will discuss include:
The Fundamentals of Machine Learning, Deep learning, And Neural Networks
How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You
How To Master Neural Network Implementation Using Different Libraries
How Random Forest Algorithms Are Able To Help Out With Machine Learning
How To Uncover Hidden Patterns And Structures With Clustering
How Recurrent Neural Networks Work And When To Use
The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning
And Much More
This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like.
If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you.
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