Artificial Intelligence III – Deep Learning in Java

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications – natural language processing and sentiment analysis!
So you’ll learn about the following topics:
Section #1:
•multi-layer neural networks and deep learning theory
•activtion functions (ReLU and many more)
•deep neural networks implementation
•how to use deeplearning4j (DL4J)
Section #2:
•convolutional neural networks (CNNs) theory and implementation
•what are kernels (feature detectors)?
•pooling layers and flattening layers
•using convolutional neural networks (CNNs) for optical character recognition (OCR)
•using convolutional neural networks (CNNs) for smile detection
•emoji detector application from scratch
Section #3:
•recurrent neural networks (RNNs) theory
•using recurrent neural netoworks (RNNs) for natural language processing (NLP)
•using recurrent neural networks (RNNs) for sentiment analysis
These are the topics we’ll consider on a one by one basis.
You will get lifetime access to over 40+ lectures!
This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back. Let’s get started!