English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 54m | 414 MB
Get the essential know-how on working with deep learning algorithms using Java
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!
What You Will Learn
- Get a practical deep dive into deep learning algorithms
- Implement well-known algorithms related to deep learning
- Explore neural networks using some of the most popular deep learning frameworks
- Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms
- Discover more Deep Learning algorithms with Convolutional Neural Networks
- Get a practical insight about how to tune models
Table of Contents
Installation and Setup
The Course Overview
Installing on Windows
Quick Start
Building NN Using GPU
Neural Networks
Classification and Clustering
Softmax Function
Multilinear Regression
Logistic Regression
Implementing Neural Nets
Gradient Descent
Multilayer Perceptron
Feed-Forward Neural Networks
Recurrent Neural Networks
Deeper Architectures
Long Short Term Memory Units
Convolutional Neural Networks
Denoising Autoencoders
Restricted Boltzmann Machine
Tuning
Hyper-Parameter Space
Fixing and Selecting Parameters
Early Stopping
Testing and Evaluating
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