TensorFlow Training

Mindmajix Technologies

Learn the core concepts of the TensorFlow framework and enrich your skills in developing deep neural networks.

Online

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

                                    Frequency of Classes
                                    Weekdays, Weekends

Introduction To Deep Learning

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

Scalars
Vectors
Matrices
Tensors
Hyperplanes

The Math Behind Machine Learning: Statistics

Probability
Conditional Probabilities
Posterior Probability
Distributions
Samples vs Population
Resampling Methods
Selection Bias
Likelihood

Review of Machine Learning Algorithms

Regression
Classification
Clustering

Reinforcement Learning

Underfitting and Overfitting

Optimization

Convex Optimization

Fundamentals 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

Linear
Sigmoid
Tanh
Hard Tanh
Softmax
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

Fundamentals Of Deep Networks

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

Introduction To 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)

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

Recurrent Neural Networks (RNN)

Introduction 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

Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Variational Autoencoders
Deep Belief Network

Practice Test & Interview Questions

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