Luis capelo alex galea (14 resultados)

paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Condición: New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. Applied Deep Learning with Python (Paperback or Softback).

Condición: New.

Paperback. Condición: New. A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examplesKey FeaturesDesigned to iteratively develop the skills of Python users who don't have a data science backgroundCovers the key foundational concepts you'll need to know when building deep learning systemsComplete with step-by-step exercises and activities to help you build the skills you need for the real worldBook DescriptionTaking an approach that uses the latest developments in the Python ecosystem, you'll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You'll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you'll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you'll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You'll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you'll delve into model optimization and evaluation. You'll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you'll be equipped with 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 customized machine learning classification strategyBuild, train and enhance your own models to solve unique problemsWork with production-ready frameworks such as TensorFlow and KerasUnderstand how neural networks operate in clear and simple termsDeploy your predictions to the webWho this book is forIf you're a Python programmer stepping into the world of data science, this is the ideal way to get started.…

Condición: New. pp. 334.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examplesKey FeaturesDesigned to iteratively develop the skills of Python users who don't have a data science backgroundCovers the key foundational concepts you'll need to know when building deep learning systemsComplete with step-by-step exercises and activities to help you build the skills you need for the real worldBook DescriptionTaking an approach that uses the latest developments in the Python ecosystem, you'll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You'll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you'll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you'll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You'll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you'll delve into model optimization and evaluation. You'll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you'll be equipped with 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 customized machine learning classification strategyBuild, train and enhance your own models to solve unique problemsWork with production-ready frameworks such as TensorFlow and KerasUnderstand how neural networks operate in clear and simple termsDeploy your predictions to the webWho this book is forIf you're a Python programmer stepping into the world of data science, this is the ideal way to get started.…

Condición: New. Print on Demand pp. 334.

Condición: New. PRINT ON DEMAND pp. 334.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 opportun.…