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Deep Learning

Non Certification Program

Schedules Play Course Overview

Overview


Deep Learning is one of the promising segments of Artificial Intelligence and machine learning technologies. This deep learning course will help you master deep learning techniques and build deep learning model.

Course Curriculum


1. Getting Started with Deep Learning
Introducing machine learning
Supervised learning, Unsupervised learning, Reinforcement learning
Neural networks, The biological neuron, An artificial neuron
How does an artificial neural network learn?
The backpropagation algorithm
Weights optimization
Stochastic gradient descent
Neural network architectures
Multilayer perceptron, DNNs architectures, Convolutional Neural Networks
Restricted Boltzmann Machines
Autoencoders, Recurrent Neural Networks
Deep learning framework comparisons

2. First Look at TensorFlow
General overview
What's new with TensorFlow 1.x?
How does it change the way people use it?
Installing and getting started with TensorFlow
Installing TensorFlow on Linux
Which TensorFlow to install on your platform?
Requirements for running TensorFlow with GPU from NVIDIA
Step 1: Install NVIDIA CUDA
Step 2: Installing NVIDIA cuDNN v5.1+
Step 3: GPU card with CUDA compute capability 3.0+
Step 4: Installing the libcupti-dev library
Step 5: Installing Python (or Python3)
Step 6: Installing and upgrading PIP (or PIP3)
Step 7: Installing TensorFlow
How to install TensorFlow
Installing TensorFlow with native pip
Installing with virtualenv
Installing TensorFlow on Windows
Installation from source
Install on Windows
Test your TensorFlow installation
Computational graphs
Why a computational graph?
Neural networks as computational graphs
The programming model
Data model, Rank, Shape, Data, Data types, Variables, Fetches, Feeds
TensorBoard
How does TensorBoard work?
Implementing a single input neuron
Source code for the single input neuron
Migrating to Tensorflow 1.x, How to upgrade using the script
Limitations, Upgrading code manually, Variables
Summary functions, Simplified mathematical variants, Miscellaneous changes

3. Using TensorFlow on a Feed-Forward Neural Network
Introducing feed-forward neural network
Classification of handwritten digits
Exploring the MNIST dataset
Softmax classifier
How to save and restore a TensorFlow model
Implementing a five-layer neural network
ReLU classifier
Dropout optimization

4. TensorFlow on a Convolutional Neural Network
The following topics are covered in the chapter:
Introducing CNN networks
CNN architecture
A model for CNNs - LeNet
Building your first CNN
Emotion recognition with CNNs

5. Optimizing TensorFlow Autoencoders
The topics covered are:
Introducing autoencoders
Implementing an autoencoder
Improving autoencoder robustness
Building denoising autoencoders
Convolutional autoencoders

6. Recurrent Neural Networks
RNN basic concepts, RNNs at work, 
Unfolding an RNN,
The vanishing gradient problem
LSTM networks
An image classifier with RNNs, 
Bidirectional RNNs, Text processing

7. GPU Computing
This chapter is organized as follows:
GPGPU computing
GPGPU history
The CUDA architecture
GPU programming model
TensorFlow GPU set up
TensorFlow GPU management
Assigning a single GPU on a multi-GPU system
Using multiple GPUs

8. Advanced TensorFlow Programming
Introducing Keras
Building deep learning models
Sentiment classification of movie reviews
Adding a convolutional layer
Pretty Tensor, Digit classifier, TFLearn
Titanic survival predictor
 

Prerequisite


Understanding the fundamentals of Python programming. Basic knowledge of statistics. Basic machine learning knowledge 

Certification


On successful completion of the training program, participants will receive the certificate of participation.

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