Packt Publishing - Hands-On Neural Networks (2019 EN)

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  1. Kanka

    Kanka Well-Known Member Loyal User

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    Author: Leonardo De Marchi, Laura Mitchell
    Full Title: Hands-On Neural Networks
    Publisher: Packt Publishing (May 30, 2019)
    Year: 2019
    ISBN-13: 9781788992596 (978-1-78899-259-6)
    ISBN-10: 1788992598
    Pages: 280
    Language: English
    Genre: Educational: Neural Networks
    File type: EPUB (True), PDF (True, but nonnative Cover), Code Files
    Quality: 10/10
    Price: 24.99 €


    Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras.

    Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics.

    Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks.

    By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.


    Learn:
    ✓ Learn how to train a network by using backpropagation
    ✓ Discover how to load and transform images for use in neural networks
    ✓ Study how neural networks can be applied to a varied set of applications
    ✓ Solve common challenges faced in neural network development
    ✓ Understand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) network
    ✓ Get up to speed with advanced and complex deep learning concepts like LSTMs and NLP
    ✓ Explore innovative algorithms like GANs and deep reinforcement learning

    Features:
    ✓ Explore neural network architecture and understand how it functions
    ✓ Learn algorithms to solve common problems using back propagation and perceptrons
    ✓ Understand how to apply neural networks to applications with the help of useful illustrations

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