[下载]Deep Learning: Practical Neural Networks with Java

Deep Learning: Practical Neural Networks with Java

出版时间:2017.06

官网链接:Packt

下载地址:天翼云盘

内容简介:

Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.

The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:

  1. Java Deep Learning Essentials
  2. Machine Learning in Java
  3. Neural Network Programming with Java, Second Edition

您将了解到:

  • Get a practical deep dive into machine learning and deep learning algorithms
  • Explore neural networks using some of the most popular Deep Learning frameworks
  • Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
  • Apply machine learning to fraud, anomaly, and outlier detection
  • Experiment with deep learning concepts, algorithms, and the toolbox for deep learning
  • Select and split data sets into training, test, and validation, and explore validation strategies
  • Apply the code generated in practical examples, including weather forecasting and pattern recognition

关于作者:

Yusuke Sugomori

Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users’ respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot of attention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015.

In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis. He was also featured as one of eight “new generation” creators by the Japanese magazine Web Designing.

In April 2016, he joined a medical start-up as cofounder and CTO.

Bostjan Kaluza

Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. Bostjan has extensive experience in Java and Python, and he also lectures on Weka in the classroom. Focusing on machine learning and data science, Bostjan has published numerous articles in professional journals, delivered conference papers, and authored or contributed to a number of patents. In 2013, Bostjan published his first book on data science, Instant Weka How-to, Packt Publishing, exploring how to leverage machine learning using Weka.

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