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Deep Learning Training in Jaipur

  • Deep Learning with TensorFlow

    Deep Learning certification course with TensorFlow is crafted by reputed industry professionals to meet the latest business needs. After completing this TensorFlow training In Jaipur, you will get hold on the concepts like SoftMax function, Autoencoder Neural Networks, Convolutional Neural Network (CNN), TensorFlow, TensorFlow-Code, graph visualization, transfer learning, recurrent neural networks, Restricted Boltzmann Machine (RBM), GPU in deep learning, backpropagation, hyperparameters through practical projects and deep learning libraries like Keras & TFLearn.

    Zeetron Networks helps you to master in TensorFlow library for Machine Learning Applications like Neural Networks. TensorFlow library used for developing google brain Product recently by Alphabet. In the future, all the Applications like Machine Learning, Deep Learning, AI & neural network needed TensorFlow maths.

  • What are the objectives of our Deep Learning with TensorFlow Course?

    Deep Learning with TensorFlow Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers:

    • In-depth knowledge of Deep Neural Networks
    • Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders
    • Implementation of Collaborative Filtering with RBM
    • The exposure to real-life industry-based projects which will be executed using TensorFlow library
    • Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices

What You Will Learn?

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
  • How Deep Learning Works?
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Important Parameters of Perceptron
  • What is TensorFlow?
  • TensorFlow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step - Use-Case Implementation
  • Understand the limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlo
  • TensorBoard
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation on SONAR dataset
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
  • Types of Deep Networks
  • Introduction to CNNs
  • CNN’s Application
  • The architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Introduction to RNN Model
  • Application use cases of RNN
  • Modeling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term Memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras