Deep Learning Beginners Guide by Rivas Pablo (11 results)

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Paperback or Softback. Condition: New. Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python 1.63. Book.

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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2021-11-01 Pages: 300 Publisher: Machinery Industry Press This book is divided into three parts.?Part 1 will help you quickly understand learning from data. the basic structure of deep learning. how to prepare data. and the basic concepts often used in deep learning.?The second part will focus on unsupervised learning algorithms.?Start with the autoencoder. and then move to a neural network model with deeper and larger scales.?The third part introduces su.

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Implementing supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine over TensorFlow.Key FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and already have the basic mathematical and programming knowledge required to get started.The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and you will even build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book.By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.What you will learnImplement RNNs and Long short-term memory for image classification and Natural Language Processing tasksExplore the role of CNNs in computer vision and signal processingUnderstand the ethical implications of deep learning modelingUnderstand the mathematical terminology associated with deep learningCode a GAN and a VAE to generate images from a learned latent spaceImplement visualization techniques to compare AEs and VAEsWho this book is forThis book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.