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  • - Over 60 recipes to design, develop, and deploy self-learning AI models using Python
    av Yuxi (Hayden) Liu
    524,-

    This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.

  • - Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
    av Pablo Maldonado, Joshua F. Wiley, Yuxi (Hayden) Liu & m.fl.
    596,-

    This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R.

  • - Create deep neural networks to solve computational problems using TensorFlow and Keras
    av Yuxi (Hayden) Liu & Saransh Mehta
    421

    This book explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations to help you understand the concepts and ideas required to build efficient artificial intelligence systems, this book will help you construct deep models using popular frameworks and datasets.

  • av Yuxi (Hayden) Liu
    480

  • - Master the techniques to design and develop neural network models in R
    av Yuxi (Hayden) Liu & Pablo Maldonado
    480

    R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text ...

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