Eep Learning With Python
Download Eep Learning With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Eep Learning With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Deep Learning with Python, Second Edition
Author: Francois Chollet
language: en
Publisher: Simon and Schuster
Release Date: 2021-12-21
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
Hands-On Python Deep Learning for the Web
Author: Anubhav Singh
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-05-15
Use the power of deep learning with Python to build and deploy intelligent web applications Key Features Create next-generation intelligent web applications using Python libraries such as Flask and Django Implement deep learning algorithms and techniques for performing smart web automation Integrate neural network architectures to create powerful full-stack web applications Book DescriptionWhen used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices.What you will learn Explore deep learning models and implement them in your browser Design a smart web-based client using Django and Flask Work with different Python-based APIs for performing deep learning tasks Implement popular neural network models with TensorFlow.js Design and build deep web services on the cloud using deep learning Get familiar with the standard workflow of taking deep learning models into production Who this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.
Hands-On Deep Learning Architectures with Python
Author: Yuxi (Hayden) Liu
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-04-30
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key Features Explore advanced deep learning architectures using various datasets and frameworks Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more Discover design patterns and different challenges for various deep learning architectures Book DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.What you will learn Implement CNNs, RNNs, and other commonly used architectures with Python Explore architectures such as VGGNet, AlexNet, and GoogLeNet Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architecture Understand deep learning architectures for mobile and embedded systems Who this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book