TensorFlow Training Courses

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TensorFlow 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.

 

The 1-on-1 Advantage

Flexible Dates

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  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

Destination Training
(Dubai/London/Sydney
New York/Delhi/Goa
Singapore)

Flexi
(Video Recording of Live Online)

You will learn:

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
Live Online Training (Duration : 40 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

2050 + If you accept merging of other students.
4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
1800 Per Participant
Online
06 - 10 Feb 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
06 - 10 Mar 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Can’t Attend Live Online? Click to Buy Flexi For Only $99 (Video Recording of Live Online)
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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