Deep Learning with TensorFlow

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Course Details

Course Objectives
After the completion of this Deep Learning with TensorFlow course, you should be able to:
Define Deep Learning
Express the motivation behind Deep Learning
Apply Analytical mathematics on the data
Choose between different Deep networks
Explain Neural networks
Train Neural networks
Discuss Backpropagation
Describe Autoencoders and varitional Autoencoders
Run a “Hello World” program in TensorFlow
Implement different Regression models
Describe Convolutional Neural Networks
Discuss the application of Convolutional Neural Networks
Discuss Recurrent Neural Networks
Describe Recursive Neural Tensor Network Theory
Implement Recursive Neural Network Model
Explain Unsupervised Learning
Discuss the applications of Unsupervised Learning
Explain Restricted Boltzmann Machine
Implement Collaborative Filtering with RBM
Define Autoencoders and discuss their Applications
Discuss Deep Belief Network
Why learn Tensorflow?
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.
Who should go for this training?
IteanzDeep 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 (M.L) 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.
What are the pre-requisites for this course?
Required Pre-requisites
Basic programming knowledge in Python
Concept of Arrays
Concepts about Machine Learning
Iteanz offers you a complimentary self-paced course - A Module on Stats and Machine learning algorithms: Supervised and Unsupervised learning algorithms, once you have enrolled in Deep Learning with TensorFlow course
Introduction to Deep Learning
> 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
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
The Math Behind Machine Learning: Statistics
Conditional Probabilities
Posterior Probability
Samples vs Population
Resampling Methods
Selection Bias
Review of Machine Learning Algorithms
Reinforcement Learning
Underfitting and Overfitting
Convex Optimization
Fundamentals of Neural Networks
Discuss the Training Techniques of Neural Networks
List Different Activation and Loss Functions
Discuss the Different parameters of Neural Networks
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
Hard Tanh
Rectified Linear
Loss Functions
Loss Function Notation
Loss Functions for Regression
Loss Functions for Classification
Loss Functions for Reconstruction
Learning Rate
Fundamentals of Deep Networks
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
Defining Deep Learning
Defining Deep Networks
Common Architectural Principals of Deep Networks
Reinforcement Learning application in Deep Networks
Activation Functions - Sigmoid, Tanh, ReLU
Loss Functions
Optimization Algorithms
Introduction to TensorFlow
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
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
Convolutional Neural Networks (CNN) 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
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
Recurrent Neural Networks (RNN) Objectives: 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
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
Restricted Boltzmann Machine(RBM) and Autoencoders
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
Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Variational Autoencoders
Deep Belief Network
This course does not have any sections.

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