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Deep Learning with TensorFlow Training in Hyderabad

Deep Learning 

Most of the Business collect large amounts of data and analyze it to obtain a great competitive advantage. To process a large amount of data for deep learning requires a large amount of computational power. This Deep Learning Techniques are used to create predictive applications for fraud detection, click prediction, demand forecasting and other data-intensive analyses as well.

Deep Learning training

Deep Learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course with TensorFlow is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.

Duration: 40hrs

Course Content:

  • Introduction to TensorFlow
    • Intro to TensorFlow
    • Computational Graph
    • Key highlights
    • Creating a Graph
    • Regression example
    • Gradient Descent
    • TensorBoard
    • Modularity
    • Sharing Variables
    • Keras
  • Perceptrons
    • What is a Perceptron
    • XOR Gate
  • Activation Functions
    • Sigmoid
    • ReLU
    • Hyperbolic Fns
    • Softmax
  • Artificial Neural Networks
    • Introduction
    • Perceptron Training Rule
    • Gradient Descent Rule
  • Gradient Descent and Backpropagation
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
    • Some problems in ANN
  • Optimization and Regularization
    • Overfitting and Capacity
    • Cross Validation
    • Feature Selection
    • Regularization
    • Hyperparameters
  • Intro to Convolutional Neural Networks
    • Intro to CNNs
    • Kernel filter
    • Principles behind CNNs
    • Multiple Filters
    • CNN applications
  • Intro to Recurrent Neural Networks
    • Intro to RNNs
    • Unfolded RNNs
    • Seq2Seq RNNs
    • LSTM
    • RNN applications
  • Deep Learning applications
    • Image Processing
    • Natural Language Processing
    • Speech Recognition
    • Video Analytics

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