Deeplearning4j Training Chennai

Deep Learning for Java


Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. DL4J is customizable in scale.

DL4J can import neural net models from most major frameworks via Keras, including TensorFlow, Caffe, and Theano, bridging the gap between the Python ecosystem and the JVM with a cross-team toolkit for data scientists, data engineers, and DevOps.

 2018-01-04 14_58_47-Deeplearning4j - Google Search

What You Will Learn

  • Explore Deep Learning and various models associated with it
  • Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
  • Implement Convolutional Neural Network (CNN) with deeplearning4j
  • Delve into the implementation of Restricted Boltzmann Machines (RBM)
  • Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
  • Get hands-on practice of deep learning and their implementation with Hadoop.


Course Contents

1. Introduction to Deep Learning

Getting started with deep learning
Deep feed-forward networks
Various learning algorithms
Unsupervised learning
Supervised learning
Semi-supervised learning

Deep learning terminologies

Deep learning: A revolution in Artificial Intelligence

Motivations for deep learning
The curse of dimensionality
The vanishing gradient problem
Distributed representation

Classification of deep learning networks
Deep generative or unsupervised models
Deep discriminate models

2. Distributed Deep Learning for Large-Scale Data

Deep learning for massive amounts of data

Challenges of deep learning for big data
Challenges of deep learning due to massive volumes of data (first V)
Challenges of deep learning from a high variety of data (second V)
Challenges of deep learning from a high velocity of data (third V)
Challenges of deep learning to maintain the veracity of data (fourth V)

Distributed deep learning and Hadoop
Iterative Map-Reduce
Yet Another Resource Negotiator (YARN)
Important characteristics for distributed deep learning design

Deeplearning4j – an open source distributed framework for deep learning
Major features of Deeplearning4j
Summary of functionalities of Deeplearning4j

Setting up Deeplearning4j on Hadoop YARN
Getting familiar with Deeplearning4j
Integration of Hadoop YARN and Spark for distributed deep learning
Rules to configure memory allocation for Spark on Hadoop YARN

3. Convolutional Neural Network

Understanding convolution
Background of a CNN
Architecture overview

Basic layers of CNN
Importance of depth in a CNN

Convolutional layer
Sparse connectivity
Improved time complexity
Parameter sharing
Improved space complexity
Equivariant representations

Choosing the hyperparameters for Convolutional layers
Mathematical formulation of hyperparameters
Effect of zero-padding

ReLU (Rectified Linear Units) layers
Advantages of ReLU over the sigmoid function

Pooling layer
Where is it useful, and where is it not?

Fully connected layer

Distributed deep CNN
Most popular aggressive deep neural networks and their configurations
Training time – major challenges associated with deep neural networks
Hadoop for deep CNNs

Convolutional layer using Deeplearning4j
Loading data
Model configuration
Training and evaluation

4. Recurrent Neural Network

What makes recurrent networks distinctive from others?

Recurrent neural networks(RNNs)
Unfolding recurrent computations
Advantages of a model unfolded in time
Memory of RNNs

Backpropagation through time (BPTT)
Error computation

Long short-term memory
Problem with deep backpropagation with time
Long short-term memory

Bi-directional RNNs
Shortfalls of RNNs
Solutions to overcome

Distributed deep RNNs
RNNs with Deeplearning4j

5. Restricted Boltzmann Machines

Energy-based models

Boltzmann machines
How Boltzmann machines learn

Restricted Boltzmann machine
The basic architecture
How RBMs work

Convolutional Restricted Boltzmann machines
Stacked Convolutional Restricted Boltzmann machines

Deep Belief networks
Greedy layer-wise training

Distributed Deep Belief network
Distributed training of Restricted Boltzmann machines
Distributed training of Deep Belief networks
Distributed back propagation algorithm
Performance evaluation of RBMs and DBNs
Drastic improvement in training time

Implementation using Deeplearning4j
Restricted Boltzmann machines
Deep Belief networks

6. Autoencoders

Regularized autoencoders

Sparse autoencoders
Sparse coding
Sparse autoencoders
The k-Sparse autoencoder
How to select the sparsity level k
Effect of sparsity level

Deep autoencoders
Training of deep autoencoders
Implementation of deep autoencoders using Deeplearning4j

Denoising autoencoder
Architecture of a Denoising autoencoder
Stacked denoising autoencoders
Implementation of a stacked denoising autoencoder using Deeplearning4j

Applications of autoencoders

7. Miscellaneous Deep Learning Operations using Hadoop

Distributed video decoding in Hadoop

Large-scale image processing using Hadoop
Application of Map-Reduce jobs

Natural language processing using Hadoop
Web crawler
Extraction of keyword and module for natural language processing
Estimation of relevant keywords from a page

Deeplearning4j Training


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