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Data Extraction, Transformation, and Loading

Let's discuss the most important part of any machine learning puzzle: data preprocessing and normalization. Garbage in, garbage out would be the most appropriate statement for this situation. The more noise we let pass through, the more undesirable outputs we will receive. Therefore, you need to remove noise and keep signals at the same time.

Another challenge is handling various types of data. We need to convert raw datasets into a suitable format that a neural network can understand and perform scientific computations on. We need to convert data into a numeric vector so that it is understandable to the network and so that computations can be applied with ease. Remember that neural networks are constrained to only one type of data: vectors.

There has to be an approach regarding how data is loaded into a neural network. We cannot...

Key benefits

Install and configure Deeplearning4j to implement deep learning models from scratch

Explore recipes for developing, training, and fine-tuning your neural network models in Java

Model neural networks using datasets containing images, text, and time-series data

Description

Java 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.

Who is this book for?

If 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.

What you will learn

Perform data normalization and wrangling using DL4J

Build deep neural networks using DL4J

Implement CNNs to solve image classification problems

Train autoencoders to solve anomaly detection problems using DL4J

Perform benchmarking and optimization to improve your model s performance

Implement reinforcement learning for real-world use cases using RL4J

Leverage the capabilities of DL4J in distributed systems

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Maxwell Dec 16, 2019

This is an excellently put together book on deep learning. The author puts you on a path to truly understanding how to use java to build learning applications. Granted, you will need the skills to back it up, but if you're like me and just needed a bit of help to get you started -- then this is book is an excellent choice.I liked how well this book flowed as well. Seeing a huge wall of text can be be off-putting when trying to learn something complex like this. I can confidently say I came away with more knowledge than I had when I started. So for me, it was worth the purchase.

Amazon Verified review

Cliente Amazon Mar 23, 2020

Thee book is awesome if you don't have knowledge about DL4J. It explains quite well how the basic of algorithms for Machine Learning work. I found really good examples, but incompletes in some way. My humble opinion: it's a good choice if you don't have prior knowledge in DL4J.

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

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Java Deep Learning Cookbook

Data Extraction, Transformation, and Loading

Technical requirements

Reading and iterating through data

Getting ready

Performing schema transformations

How to do it...

Building a transformation process

How to do it...

Serializing transforms

How to do it...

Executing a transform process

How to do it...

Normalizing data for network efficiency

How to do it...

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

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