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 Jobs in Saar

Enjoy the demand

Find jobs related to Deep Learning in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Saar, chennai and europe countries. You can find many jobs for freshers related to the job positions in Saar.

  • Software Engineer
  • Research Analyst
  • Data Analyst
  • Data Scientist
  • Data Engine
  • Image Recognition
  • Software Developer
  • Research Scientist
  • Instructor for Deep Learning
  • Applied Scientist

Deep Learning Internship/Course Details

Deep Learning internship jobs in Saar
Deep Learning Students receive practical experience by working on real-world projects. Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning. Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. The foundations of deep learning and neural networks are covered, as well as techniques for improving neural networks, strategies for organizing and completing machine learning projects, convolutional neural networks, and their applications, recurrent neural networks and their methods and applications, and advanced topics such as deep reinforcement learning, generative adversarial networks, and adversarial attacks. Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets. 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. 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.

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