What you'll learn:
- Understands deep learning fundamentals
- Understand convolutional neural networks (CNNs)
- Implement convolutional neural networks with DL4J library in Java
- Understand recurrent neural networks (RNNs)
- Understand the word2vec approach
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 relyheavily on this algorithm. First you will learn aboutdensly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with thedeeplearning4j 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!
