TensorFlow Certification Training

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Multisoft Systems is offering the TensorFlow Certification Training to the candidates for giving them a deeper insight into the core subjects. The training is designed to throw the lights on the TensorFlow essentials and its main functions. As TensorFlow is one of the best libraries, which is needed for the execution of Deep Learning, this TensorFlow training program also helps the candidates in gaining the hands-on experience and acquiring the complete understanding of the Deep Learning Application building process.

When candidates complete the TensorFlow course, they will be able to:

  • Explain Deep learning
  • Define the motivations behind the Deep Learning
  • Describe Neural networks
  • Train the Neural networks
  • Discuss Backpropagation
  • Define Variational Autoencoders and Autoencoders
  • Run “Hello World” program in the TensorFlow
  • Define Convolutional Neural Networks
  • Discuss the RNN
  • Explain the theory of Recursive Neural Tensor Network
  • Execute the model of Recursive Neural Network
  • Explain Unsupervised Learning
  • Discuss the various applications of Unsupervised Learning
  • Describe the Restricted Boltzmann Machine
Target Audience
  • Application Developers
  • Data Analysts
  • Data Scientists
  • Professionals with basic knowledge of Machine Learning and Programming
  • Academic Researchers
Prerequisites
  • Candidates must have basic knowledge of Programming mainly in the C++ or Python.
  • Having knowledge of Arrays Concepts would be an added advantage.
  • A clear understanding of the Machine Learning Concepts is compulsory.

1. An Introduction to Deep Learning

  • An overview of Deep Learning
  • Deep Learning- A massive change in the Artificial Intelligence
  • An overview of Machine Learning
  • Limitations of Machine Learning
  • Reasons to go with Deep Learning over Machine Learning

2. Understanding Fundamentals of Neural Networks using TensorFlow

  • Work process of Deep Learning
  • Different Activation Functions
  • How Deep Learning Works?
  • Activation Functions
  • A Brief of Perceptron
  • Training a Perceptron
  • Key Parameters of Perceptron
  • An explanation of Tensorflow?
  • TensorFlow and its code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step-by-Step process of Use-Case Implementation

3. Explanation of the Neural Networks using TensorFlow

  • An overview of the limitations of a single Perceptron
  • Knowing the limitations of A Single Perceptron
  • Know Neural Networks in-depth
  • Explanation of Multi-layer Perceptron
  • Backpropagation- Learning Algorithm
  • An overview of Backpropagation- Using Neural Network with Examples
  • MLP Digit-Classifier using TensorFlow
  • TensorBoard
  • Summary

4. Master Deep Networks

  • Why go to the Deep Learning? 
  • Classification of the SONAR Dataset 
  • What is Deep Learning? 
  • Extraction of the Features
  • Work process of the Deep Network 
  • Training using Backpropagation 
  • Options of Gradient Descent 
  • Different Types of Deep Networks

5. Convolutional Neural Networks (CNN)

  • An introduction to Convolutional Neural Networks
  • Applications of the Convolutional Neural Networks
  • Architecture of Convolutional Neural Networks
  • Pooling and Convolutional layers in the Convolutional Neural Networks
  • Visualizing the Convolutional Neural Networks
  • Fine-tuning and transfer learning Convolutional Neural Networks

6. Recurrent Neural Networks (RNN)

  • An introduction to Recurrent Neural Networks
  • Applying use cases of Recurrent Neural Networks
  • Modelling sequences of Recurrent Neural Networks
  • Training RNNs with Backpropagation 
  • Long and short-term memory (LSTM)
  • Theory of Neural Tensor Network
  • Different Models of Recurrent Neural Network

7. Restricted Boltzmann Machine (RBM) and Autoencoders

  • An overview of Restricted Boltzmann Machine
  • Different applications of RBM
  • Combined Filtering with RBM
  • An overview of Autoencoders
  • Applications of Autoencoders
  • Understanding of Autoencoders

8. An Introduction to Keras

  • An overview of Keras
  • Ways to create models in Keras
  • Functional and Sequential Compositions
  • Predefined Neural Network Layers
  • Batch Normalization: What exactly it is?
  • Saving and loading the models with Keras
  • Customization of the training process
  • Uses of TensorBoard with Keras
  • Process of Use-Case Implementation with Keras

9. An Introduction to TFlearn

  • An overview of TFlearn
  • Composing models in TFlearn
  • Functional and Sequential Compositions
  • Predefined Neural Network Layers
  • Batch Normalization: What exactly it is?
  • Saving and loading the models with TFlearn
  • Customization of the training process
  • Uses of TensorBoard with TFlearn
  • Process of Use-Case Implementation with TFlearn

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