Автор: Avendi Michael Название: PyTorch Computer Vision Cookbook ISBN: 1838644830 ISBN-13(EAN): 9781838644833 Издательство: Неизвестно Рейтинг: Цена: 41370.00 T 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.
Автор: Auffarth Ben Название: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch ISBN: 1789133963 ISBN-13(EAN): 9781789133967 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Liu Yuxi (Hayden) Название: PyTorch 1.0 Reinforcement Learning Cookbook ISBN: 1838551964 ISBN-13(EAN): 9781838551964 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.
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: Gridin Название: Automated Deep Learning Using Neural Network Intelligence ISBN: 1484281489 ISBN-13(EAN): 9781484281482 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn * Know the basic concepts of optimization tuners, search space, and trials * Apply different hyper-parameter optimization algorithms to develop effective neural networks * Construct new deep learning models from scratch * Execute the automated Neural Architecture Search to create state-of-the-art deep learning models * Compress the model to eliminate unnecessary deep learning layers Who This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development
Автор: Pajankar Ashwin, Joshi Aditya Название: Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch ISBN: 1484279204 ISBN-13(EAN): 9781484279205 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L
Автор: Jadon Shruti, Garg Ankush Название: Hands-On One-shot Learning with Python ISBN: 1838825460 ISBN-13(EAN): 9781838825461 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.
Автор: Sanghi Nimish Название: Deep Reinforcement Learning with Python: With Pytorch, Tensorflow and Openai Gym ISBN: 1484268083 ISBN-13(EAN): 9781484268087 Издательство: Springer Цена: 30620.00 T Наличие на складе: Поставка под заказ. 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
Автор: Hany John, Walters Greg Название: Hands-On Generative Adversarial Networks with PyTorch 1.x ISBN: 1789530512 ISBN-13(EAN): 9781789530513 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book will help you understand how GANs architecture works using PyTorch. You will get familiar with the most flexible deep learning toolkit and use it to transform ideas into actual working codes. You will apply GAN models to areas like computer vision, multimedia and natural language processing using a sample-generation perspective.
Автор: Vasilev Ivan Название: Advanced Deep Learning with Python ISBN: 178995617X ISBN-13(EAN): 9781789956177 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T 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.
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.
Автор: Ketkar Nikhil, Moolayil Jojo Название: Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch ISBN: 1484253639 ISBN-13(EAN): 9781484253632 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn
Review machine learning fundamentals such as overfitting, underfitting, and regularization.
Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
Apply in-depth linear algebra with PyTorch
Explore PyTorch fundamentals and its building blocks
Work with tuning and optimizing models
Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
