Description: Applied Deep Learning with Python by Alex Galea, Luis Capelo Getting started with data science can be overwhelming, even for experienced developers. In this two-part, hands-on book well show you how to apply your existing understanding of the Python language to this new and exciting field thats full of new opportunities (and high expectations)! FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examplesKey FeaturesDesigned to iteratively develop the skills of Python users who dont have a data science backgroundCovers the key foundational concepts youll need to know when building deep learning systemsFull of step-by-step exercises and activities to help build the skills that you need for the real-worldBook DescriptionTaking an approach that uses the latest developments in the Python ecosystem, youll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. Well explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. Its okay if these terms seem overwhelming; well show you how to put them to work.Well build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. Its after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.By guiding you through a trained neural network, well explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. Well do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.What you will learnDiscover how you can assemble and clean your very own datasetsDevelop a tailored machine learning classification strategyBuild, train and enhance your own models to solve unique problemsWork with production-ready frameworks like Tensorflow and KerasExplain how neural networks operate in clear and simple termsUnderstand how to deploy your predictions to the webWho this book is forIf youre a Python programmer stepping into the world of data science, this is the ideal way to get started. Author Biography Alex Galea has been professionally practicing data analytics since graduating with a masters degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks. Luis Capelo is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. He worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector. Table of Contents Table of ContentsJupyter FundamentalsData Cleaning and Advanced Machine LearningWeb Scraping and Interactive VisualizationsIntroduction to Neural Networks and Deep LearningModel ArchitectureModel EvaluationProductization Long Description A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who dont have a data science background Covers the key foundational concepts youll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, youll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. Well explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. Its okay if these terms seem overwhelming; well show you how to put them to work. Well build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. Its after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, well explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. Well do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If youre a Python programmer stepping into the world of data science, this is the ideal way to get started. Details ISBN1789804744 Author Luis Capelo Pages 334 Language English Year 2018 ISBN-10 1789804744 ISBN-13 9781789804744 Format Paperback Publication Date 2018-08-31 Publisher Packt Publishing Limited Short Title Applied Deep Learning with Python UK Release Date 2018-08-31 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2018-08-31 NZ Release Date 2018-08-31 Subtitle Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:119061858;
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ISBN-13: 9781789804744
Book Title: Applied Deep Learning with Python
Item Height: 93 mm
Item Width: 75 mm
Author: Luis Capelo, Alex Galea
Publication Name: Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
Format: Paperback
Language: English
Publisher: Packt Publishing Limited
Subject: Computer Science
Publication Year: 2018
Type: Textbook
Number of Pages: 334 Pages
