Explore Deep Learning algorithms with the most famous Java Framework: DL4J
Are you looking forward for a guide that trains you to get proficient with concepts of Deep Learning with Java? Then this is the perfect course for you.
This is a hands-on, practical approach, designed to guide you in implement real-world deep learning models in Deeplearning4j and JavaML. This course can be of utmost important as it teaches you the use the DL4J library and apply Deep Learning to a range of real-world use cases. It also provides deep driven into Neural Networks, working with Perceptron, XOR, and Gradient Descent on code examples. Along with focusing into Convolutional Networks with extraction, Max pooling, and Softmax. Hands-on with working examples at the end of each section. It also covers Recurrent Neural Network, explaining all the features of its Architecture.
By end of this learning path you'll have complete hands-on experience of neural networks using some of the most popular deep learning frameworks along with implementing real-world deep learning models in Deeplearning4j and JavaML
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Getting Started with Java Deep Learning starts with installing the environment, where Git is used as version control, Eclipse or IntelliJ as an IDE, and mostly Gradle with a little bit of Maven as a build tool. You will learn how to use the DL4J and apply deep learning to a range of real-world use cases. You will then be introduced to Neural networks and later you will learn how to implement them. You will also be given an insight about various deep learning algorithms. You will then be trained to tune Apache Spark.
The Second course, Java Deep Learning Solutions start by installing Deep Learning software for Java. You learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. The course will take you into Neural Networks, working with Perceptron, XOR, and Gradient Descent on code examples. It then covers Convolutional Networks with extraction, Max pooling, and Softmax. You'll get hands-on with working examples at the end of each section. It also covers Recurrent Neural Network, explaining all the features of its Architecture. Finally, you'll learn about Word2Vec's different models and how to work with them via practical examples.
About the Authors:
Sercan Karaoglu gained his BSc in Mathematics Engineering at Istanbul Technical University. Karaoglu also completed a Research and Development project at age 23, at Foreks, in collaboration with The Scientific and Technological Research Council of Turkey (TUBITAK). This project was related to the application of Artificial Neural Networks in Financial Trading Decision Support Systems and Market Simulation for Intraday and Daily Trading. Currently, he develops High Throughput-Low Latency Reactive Micro services and Reactive Stream applications at work and researches the topics of Deep Learning and Machine Learning. He is Java Software Engineer at the Dissemination Department of Foreks Information Systems, which is one of the leading IT companies in Turkey’s financial sector. It has specialized in software that is directly integrated with financial professionals and Istanbul Stock Market for over 26 years. He is currently studying for his MSc in Computer Engineering at Bahcesehir University in the field of Big Data Analytics and Management.
Shreenidhi Sudhakar is a Deep Learning enthusiast. He has completed his Masters from Florida Institute of Technology with specialization in Machine Learning. On the work front, he has around 3 years of experience in IT as a Principal Deep Learning Intern and Full Stack Java Developer. He also writes articles on Deep Learning for the blog Towards Data Science.
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