Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Описание: If you are looking to build next-generation AI solutions for work or even for your pet projects, you`ll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving.
Описание: Chapter 1: Introduction to Deep Reinforcement LearningChapter Goal: Introduce the reader to field of reinforcement learning and setting the context of what they will learn in rest of the bookSub -Topics1. Deep reinforcement learning2. Examples and case studies3. Types of algorithms with mind-map4. Libraries and environment setup5. Summary Chapter 2: Markov Decision ProcessesChapter Goal: Help the reader understand models, foundations on which all algorithms are built. Sub - Topics 1. Agent and environment2. Rewards3. Markov reward and decision processes4. Policies and value functions5. Bellman equations Chapter 3: Model Based Algorithms Chapter Goal: Introduce reader to dynamic programming and related algorithms Sub - Topics: 1. Introduction to OpenAI Gym environment2. Policy evaluation/prediction3. Policy iteration and improvement4. Generalised policy iteration5. Value iteration Chapter 4: Model Free ApproachesChapter Goal: Introduce Reader to model free methods which form the basis for majority of current solutionsSub - Topics: 1. Prediction and control with Monte Carlo methods2. Exploration vs exploitation3. TD learning methods4. TD control5. On policy learning using SARSA6. Off policy learning using q-learning Chapter 5: Function Approximation Chapter Goal: Help readers understand value function approximation and Deep Learning use in Reinforcement Learning. 1. Limitations to tabular methods studied so far2. Value function approximation3. Linear methods and features used4. Non linear function approximation using deep Learning Chapter 6: Deep Q-Learning Chapter Goal: Help readers understand core use of deep learning in reinforcement learning. Deep q learning and many of its variants are introduced here with in depth code exercises. 1. Deep q-networks (DQN)2. Issues in Naive DQN 3. Introduce experience replay and target networks4. Double q-learning (DDQN)5. Duelling DQN6. Categorical 51-atom DQN (C51)7. Quantile regression DQN (QR-DQN)8. Hindsight experience replay (HER) Chapter 7: Policy Gradient Algorithms Chapter Goal: Introduce reader to concept of policy gradients and related theory. Gain in depth knowledge of common policy gradient methods through hands-on exercises1. Policy gradient approach and its advantages2. The policy gradient theorem3. REINFORCE algorithm4. REINFORCE with baseline5. Actor-critic methods6. Advantage actor critic (A2C/A3C)7. Proximal policy optimization (PPO)8. Trust region policy optimization (TRPO) Chapter 8: Combining Policy Gradients and Q-Learning Chapter Goal: Introduce reader to the trade offs between two approaches ways to connect together the two seemingly dissimilar approaches. Gain in depth knowledge of some land mark approaches.1. Tradeoff between policy gradients and q-learning2. The connection3. Deep deterministic policy gradient (DDPG)4. Twin delayed DDPG (TD3)5. Soft actor critic (SAC) Chapter 9: Integrated Learning and Planning Chapter Goal: Introduce reader to the scalable approaches which are sample efficient for scalable problems.1. Model based reinforcement learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Get a head start in the world of AI and deep learning by developing your skills with PyTorch
Key Features
Learn how to define your own network architecture in deep learning
Implement helpful methods to create and train a model using PyTorch syntax
Discover how intelligent applications using features like image recognition and speech recognition really process your data
Book Description
Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch.
It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.
The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.
By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.
What you will learn
Explore the different applications of deep learning
Understand the PyTorch approach to building neural networks
Create and train your very own perceptron using PyTorch
Solve regression problems using artificial neural networks (ANNs)
Handle computer vision problems with convolutional neural networks (CNNs)
Perform language translation tasks using recurrent neural networks (RNNs)
Who this book is for
This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.
Автор: Mitchell Laura, K Sri Yogesh, Subramanian Vishnu Название: Deep Learning with PyTorch 1.x - Second Edition ISBN: 1838553002 ISBN-13(EAN): 9781838553005 Издательство: Неизвестно Рейтинг: Цена: 5516.00 р. Наличие на складе: Нет в наличии.
Описание: With practical examples, this book teaches you how to effectively implement deep learning techniques to build neural network architectures. This book will be useful for anyone who wants to implement deep learning concepts using the latest version of PyTorch
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.
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Автор: Catrinescu Название: Deploying SharePoint 2016 ISBN: 1484219988 ISBN-13(EAN): 9781484219980 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
See how to install, configure, and maintain the latest release of Microsoft’s popular SharePoint Server, SharePoint 2016. This latest version brings with it many changes for IT professionals. Read Deploying SharePoint 2016 to find out how to create a performant and stable SharePoint environment for your company.
What You’ll Learn:
Install SharePoint Server 2016, both using the user interface provided by Microsoft, and by using PowerShell.Understand your authentication options and associated security considerations.Deploy add-ins, either from the store, or from your own custom app catalog.Configure Search Service Application using either the provided UI or PowerShell.Configure business intelligence components such as Excel Services, SQL Server Reporting Services, and PowerPivot.Migrate to SharePoint Server 2016 from either SharePoint Server 2010 or 2013.Understand approaches to high availability, disaster recovery, patching, and ways to monitor and maintain your SharePoint 2016 deployment once it’s up and running.
Who This Book Is For:
Anyone tasked with installing, configuring, and maintaining SharePoint Server 2016 in their organization. This book assumes some working knowledge of a previous release of SharePoint Server, such as SharePoint 2010 or SharePoint 2013.
Автор: Avendi Michael Название: PyTorch Computer Vision Cookbook ISBN: 1838644830 ISBN-13(EAN): 9781838644833 Издательство: Неизвестно Рейтинг: Цена: 6206.00 р. Наличие на складе: Нет в наличии.
Описание: This book enables you to solve the trickiest of problems in computer vision using deep learning algorithms and techniques. You will learn to use several different algorithms for different CV problems such as classification, detection, segmentation, and more using Pytorch. Packed with best practices in training and deployment of CV applications.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples
Key Features
Understand how to use PyTorch 1.x to build advanced neural network models
Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques
Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more
Book Description
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.
By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
What You Will Learn
Implement text and music generating models using PyTorch
Build a deep Q-network (DQN) model in PyTorch
Export universal PyTorch models using Open Neural Network Exchange (ONNX)
Become well-versed with rapid prototyping using PyTorch with fast.ai
Perform neural architecture search effectively using AutoML
Easily interpret machine learning (ML) models written in PyTorch using Captum
Design ResNets, LSTMs, Transformers, and more using PyTorch
Find out how to use PyTorch for distributed training using the torch.distributed API
Who this book is for
This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
