AI & Deep Learning with TensorFlow

Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. 

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. 

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

Edureka Deep learning with Tensorflow course is designed for all those who want to learn Deep Leaning which would include understanding of Deep Learning methods, Neural Networks, Deep Learning uses Tensorflow, Restricted Boltzmann Machines (RBM) and Autoencoders.

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts 

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

Objectives:

At the end of this Module, you should be able to:

• Discuss the revolution of Artificial Intelligence

• Discuss the limitations of Machine Learning

• List the advantages of Deep Learning over Machine Learning

• Discuss Real-life use cases of Deep Learning

• Understand the Scenarios where Deep Learning is applicable

• Discuss relevant topics of Linear Algebra and Statistics

• Discuss Machine learning algorithms

• Define Reinforcement Learning 

• Discuss model parameters and optimization techniques

Topics:

• Deep Learning: A revolution in Artificial Intelligence

• Limitations of Machine Learning

• Discuss the idea behind Deep Learning

• Advantage of Deep Learning over Machine learning

• 3 Reasons to go Deep

• Real-Life use cases of Deep Learning

• Scenarios where Deep Learning is applicable

• The Math behind Machine Learning: Linear Algebra

o Scalars

o Vectors

o Matrices

o Tensors

o Hyperplanes

• The Math Behind Machine Learning: Statistics

o Probability

o Conditional Probabilities

o Posterior Probability

o Distributions

o Samples vs Population

o Resampling Methods

o Selection Bias

o Likelihood

• Review of Machine Learning Algorithms

o Regression

o Classification

o Clustering

• Reinforcement Learning 

• Underfitting and Overfitting

• Optimization

• Convex Optimization

Objectives: 

At the end of this Module, you should be able to:

• Define Neural Networks

• Discuss the Training Techniques of Neural Networks

• List Different Activation and Loss Functions

• Discuss the Different parameters of Neural Networks 

Topics:

• Defining Neural Networks

• The Biological Neuron

• The Perceptron

• Multi-Layer Feed-Forward Networks

• Training Neural Networks

• Backpropagation Learning

• Gradient Descent

• Stochastic Gradient Descent

• Quasi-Newton Optimization Methods

• Generative vs Discriminative Models

• Activation Functions

o Linear

o Sigmoid

o Tanh

o Hard Tanh

o Softmax

o Rectified Linear

• Loss Functions

• Loss Function Notation

• Loss Functions for Regression

• Loss Functions for Classification

• Loss Functions for Reconstruction

• Hyperparameters

• Learning Rate

• Regularization

• Momentum

• Sparsity

Objectives:

At the end of this Module, you should be able to:

• Define Deep Learning

• Discuss the Architectural Principals of Deep Networks

• List Different parameters of Deep Networks

• Discuss the Building Blocks of Deep Networks

• Discuss how reinforcement learning is used in Deep Networks

Topics:

• Defining Deep Learning

• Defining Deep Networks

• Common Architectural Principals of Deep Networks

• Reinforcement Learning application in Deep Networks

• Parameters

• Layers

• Activation Functions - Sigmoid, Tanh, ReLU

• Loss Functions

• Optimization Algorithms

• Hyperparameters

• Summary

Objectives:

At the end of this Module, you should be able to:

• Define TensorFlow

• Illustrate how TensorFlow works

• Discuss the Functionalities of TensorFlow

• Illustrate different ways to install TensorFlow

• Write and Run programs on TensorFlow

Topics:

• What is TensorFlow?

• Use of TensorFlow in Deep Learning

• Working of TensorFlow

• How to install Tensorflow

• HelloWorld with TensorFlow

• Running a Machine learning algorithms on TensorFlow

Objectives:

At the end of this Module, you should be able to:

• Define CNNs

• Discuss the Applications of CNN

• Explain the Architecture of a CNN

• List Convolution and Pooling Layers in CNN

• Illustrate CNN

• Discuss Fine-tuning and Transfer Learning of CNNs

Topics:

• Introduction to CNNs

• CNNs Application

• Architecture of a CNN

• Convolution and Pooling layers in a CNN

• Understanding and Visualizing a CNN

• Transfer Learning and Fine-tuning Convolutional Neural Networks

Objectives:

At the end of this Module, you should be able to:

• Define RNN

• Discuss the Applications of RNN

• Illustrate how RNN is trained

• Discuss Long Short-Term memory(LSTM)

• Explain Recursive Neural Tensor Network Theory

• Illustrate the working of Neural Network Model

Topics:

• Intro to RNN Model

• Application use cases of RNN 

• Modelling sequences

• Training RNNs with Backpropagation 

• Long Short-Term memory (LSTM)

• Recursive Neural Tensor Network Theory

• Recurrent Neural Network Model

Objectives:

At the end of this Module, you should be able to:

• Define RBM

• Discuss the Applications of RBM

• Illustrate Collaborative Filtering using RBM 

• Define Autoencoders

• Explain Deep Belief Networks

Topics:

• Restricted Boltzmann Machine

• Applications of RBM

• Collaborative Filtering with RBM

• Introduction to Autoencoders 

• Autoencoders applications

• Understanding Autoencoders

• Variational Autoencoders

• Deep Belief Network

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