Get the essential know-how on working with deep learning algorithms using Java.
About This Getting Started with Java Deep Learning Video course
- Go beyond the theory and put deep learning into practice with Java
- Work with powerful libraries to enhance your deep learning algorithms
- Whether you’re a data scientist or Java developer, dive in and find out how to tackle deep learning
Java Deep Learning course In Detail
AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success.
You will learn how to install 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.
By the end of the video course, you’ll be ready to tackle deep learning with Java. Wherever you’ve come from—whether you’re a data scientist or Java developer—you will become a part of the deep learning revolution!
Java, Data Science training table of contents (duration : 1h54m)
Installation and Setup
- The Course Overview free 00:05:15
- Installing on Windows 00:10:07
- Quick Start 00:02:28
- Building NN Using GPU 00:02:31
- Classification and Clustering 00:09:29
- Softmax Function 00:02:33
- Multilinear Regression 00:03:39
- Logistic Regression 00:04:29
Implementing Neural Nets
- Gradient Descent 00:05:08
- Multilayer Perceptron 00:07:26
- Feed-Forward Neural Networks 00:04:17
- Recurrent Neural Networks 00:05:51
- Long Short Term Memory Units 00:03:51
- Convolutional Neural Networks 00:06:50
- Denoising Autoencoders 00:13:13
- Restricted Boltzmann Machine 00:11:27
- Hyper-Parameter Space 00:04:04
- Fixing and Selecting Parameters 00:04:42
- Early Stopping 00:03:55
- Testing and Evaluating 00:03:01