TensorFlow Certification Training Course Overview

TensorFlow is a through and through open source arrange for AI. It has a sweeping, versatile condition of gadgets, libraries, and system resources that lets researchers push the front line in ML and fashioners adequately develop and pass on ML-energized applications.

TensorFlow was at first made by experts and originators working on the Google Brain bunch inside Google's Machine Intelligence Research relationship to lead AI and significant neural frameworks explore. The system is adequately broad to be material in a wide collection of various spaces, as well.

TensorFlow gives stable Python and C++ APIs, similarly as non-guaranteed backward ideal API for various tongues.

 

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TensorFlow Training Courses (Duration : 40 Hours) Download Course Contents

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Course Modules

Module 1: Introduction
  • Introduction to TensorFlow
  • Architecture of Tensorflow
  • Installation on Local Machine
  • Using TensorFlow in Google Colab
  • Working with Tensors and Operations
  • Keras Low level api
Module 2: Introduction to Artificial Neural Networks
  • From Biological to Artificial Neurons
  • Different activation functions
  • What is perceptron?
  • Multilayer perceptron and back propagation
  • Working with sequential api
  • Working with the functional api
  • Using callbacks
  • Tensorboard for visualization
Module 3: Training Deep Neural Nets
  • Challenges of Deep Neural Network
  • Vanishing and Exploding gradients
  • Glorot and He Initialization
  • Non Saturating Activation Functions
  • Different activation functions effect on deep neural nets
  • Batch normalization
  • Reusing the pre-trained layers in Neural nets
  • Faster optimizers
  • L1 and L2 regularization
  • Dropouts and their purposes
Module 4: Loading and preprocessing the data
  • The data api
  • Chaining transformations
  • Pre-processing the data
  • TFR record format and compressed files
  • Introduction to protocol buffer
  • Processing the Input features
  • TF transform
  • Tensorflow datasets project
Module 5: Computer Vision using CNN
  • Inspiration to the CNN
  • Architecture of CNN
  • Convolution layers in CNN
  • Filters in CNN
  • Pooling layer in CNN
  • Depth pooling in CNN
  • Different architectures of CNN
Module 6: Processing Sequences using RNN
  • Single neuron RNN
  • Working with RNN neural network
  • Input and Output Sequences in RNN
  • Introduction to Deep RNN
  • Forecasting using RNN
  • Unstable gradient problem
  • Architecture of LSTM
  • Architecture of GRU
Module 7: Natural Language Processing
  • Introduction to Natural Language processing
  • Shakespear text generation using char RNN
  • Stateless and stateful RNN
  • Concept of sentiment analysis
  • Encoder and Decoder Network for Neural Machine Translation
  • Pre-processing required for encoder and decoder
  • Concept of Beam Search
  • Overview of attention mechanism
Module 8: Representative Learning and Generative Learning using Autoencoders and GANs
  • Introduction to Autoencoders and GAN
  • Efficient data representation
  • Dimensionality reduction using autoencoders
  • Introduction to stacked autoencoders
  • Training one autoencoder at a time
  • Recurrent autoencoders
  • Sparse autoencoders
  • Generative adversarial networks
  • Deep Convolutional GANs
Module 9: Reinforcemnent Learning
  • Introduction to reinforcement learning
  • Learning to optimize rewards
  • Policy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Credit Assignment Problem
  • Markov Decision Process
  • Q Learning
  • Deep Q Learning
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Course Prerequisites
  • Basic Computer Knowledge.

You will learn how to :

  • Explore the criticalness of TensorFlow
  • Make a computational and default chart in tensorflowimplement
  • Exhibit reshaping of a tensor with tf.reshape.
  • Actualize direct relapse and slope plummet in tensorflow.
  • Talk about the importance and utilization of layers and keras in tensorflow.
  • Show the utilization of tensorboard. in the following area, let us find out about tensorflow.

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