SKU/Artículo: AMZ-B07YLYH2KY

Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

Format:

Kindle

Paperback

Detalles del producto

Disponibilidad:
Fuera de stock

Peso con empaque:
0.15 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratchKey FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook DescriptionJava is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently.This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results.By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.What you will learnPerform data normalization and wrangling using DL4JBuild deep neural networks using DL4JImplement CNNs to solve image classification problemsTrain autoencoders to solve anomaly detection problems using DL4JPerform benchmarking and optimization to improve your model's performanceImplement reinforcement learning for real-world use cases using RL4JLeverage the capabilities of DL4J in distributed systemsWho this book is forIf you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.Table of ContentsIntroduction to Deep Learning in JavaData Extraction, Transform and LoadingBuilding Deep Neural Networks for Binary classificationBuilding Convolutional Neural NetworksImplementing NLPConstructing LTSM Network for time seriesConstructing LTSM Neural network for sequence classificationPerforming Anomaly detection on unsupervised dataUsing RL4J for Reinforcement learningDeveloping applications in distributed environmentApplying Transfer Learning to network modelsBenchmarking and Neural Network Optimization