Seneque / Chua Hands-On Deep Learning with Go

A practical guide to building and implementing neural network models using Go
1. Auflage 2019
ISBN: 978-1-78934-788-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 242 Seiten

No detailed description available for "Hands-On Deep Learning with Go".

Autoren/Hrsg.

Seneque, Gareth

Chua, Darrell

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Introduction to Deep Learning in Go
- What Is a Neural Network and How Do I Train One?
- Beyond Basic Neural Networks - Autoencoders and RBMs
- CUDA - GPU-Accelerated Training
- Next Word Prediction with Recurrent Neural Networks
- Object Recognition with Convolutional Neural Networks
- Maze Solving with Deep Q-Networks
- Generative Models with Variational Autoencoders
- Building a Deep Learning Pipeline
- Scaling Deployment

Über Autor(innen)

Seneque Gareth:

Gareth Seneque is a machine learning engineer with 11 years' experience of building and deploying systems at scale in the finance and media industries. He became interested in deep learning in 2014 and is currently building a search platform within his organization, using neuro-linguistic programming and other machine learning techniques to generate content metadata and drive recommendations. He has contributed to a number of open source projects, including CoREBench and Gorgonia. He also has extensive experience with modern DevOps practices, using AWS, Docker, and Kubernetes to effectively distribute the processing of machine learning workloads.Chua Darrell:

Darrell Chua is a senior data scientist with more than 10 years' experience. He has developed models of varying complexity, from building credit scorecards with logistic regression to creating image classification models for trading cards. He has spent the majority of his time working with in fintech companies, trying to bring machine learning technologies into the world of finance. He has been programming in Go for several years and has been working on deep learning models for even longer. Among his achievements is the creation of numerous business intelligence and data science pipelines that enable the delivery of a top-of-the-line automated underwriting system, producing near-instant approval decisions.