Deep Learning Training by Experts

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Our Training Process

Deep Learning - Syllabus, Fees & Duration

MODULE 1

  • Introduction to Tensor Flow
  • Computational Graph
  • Key highlights
  • Creating a Graph
  • Regression example
  • Gradient Descent
  • TensorBoard
  • Modularity
  • Sharing Variables
  • Keras Perceptrons
  • What is a Perceptron?
  • XOR Gate

MODULE 2

  • Activation Functions
  • Sigmoid
  • ReLU
  • Hyperbolic Fns, Softmax Artificial Neural Networks
  • Introduction
  • Perceptron Training Rule
  • Gradient Descent Rule

MODULE 3

  • 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

MODULE 4

  • Introduction to Convolutional Neural Networks
  • Introduction to CNNs
  • Kernel filter
  • Principles behind CNNs
  • Multiple Filters
  • CNN applications Introduction to Recurrent Neural Networks
  • Introduction to RNNs
  • Unfolded RNNs
  • Seq2Seq RNNs
  • LSTM
  • RNN applications

MODULE 5

  • Deep learning applications
  • Image Processing
  • Natural Language Processing
  • Speech Recognition
  • Video Analytics
  • Download Syllabus - Deep Learning
    This syllabus is not final and can be customized as per needs/updates
 
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Deep Learning Internship/Course Details

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Deep Learning Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. Deep learning is important because it automates feature generation, works well with unstructured data, has improved self-learning capabilities, supports parallel and distributed algorithms, is cost-effective, has advanced analytics, and is scalable. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. Python is the language of deep learning. Students receive practical experience by working on real-world projects. Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network. Deep learning teaches using botorganizeded anorganizedtured data. Companies like to hire people who have completed this deep learning course. . Participants in the deep learning course should have a thorough understanding of the principles of programming, as well as a solid understanding of the fundamentals of statistics and mathematics, as well as a clear grip on the critical knowledge portions of machine learning.

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