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. Python is the language of deep learning. Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning. Deep learning teaches using botorganizeded anorganizedtured data. Rather than being numerical, the majority of the data is in an unstructured format, such as audio, image, text, and video. 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. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own. This deep learning course in Ballarat is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning. Because there is a strong demand for skilled deep learning engineers in various fields, this deep learning course in Ballarat certification training is ideal for intermediate and advanced experts.

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