Deep Learning with TensorFlow

Live Online (VILT) & Classroom Corporate Training Course

This Deep Learning with ensorFlow course is designed to help you master deep learning techniques and enables you to build deep learning models using the TensorFlow frameworks.

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    24x7 Support
  • Lifetime Access
    Lifetime Access


In this Deep Learning course with TensorFlow training, participants will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, they will be able to build deep learning models, interpret results, and build your own deep learning project.



At the end of Deep Learning with TensorFlow training course, participants will be able to

  • Understand the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
  • Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
  • Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
  • Build deep learning models using Keras and TensorFlow frameworks and interpret the results
  • Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
  • Troubleshoot and improve deep learning models
  • Build your own deep learning project
  • Differentiate between machine learning, deep learning, and artificial intelligence


Participants in this training should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.


Course Outline

  • What is AI and Deep Learning
  • Brief History of AI
  • Recap: SL, UL and RL
  • Deep Learning: Successes Last Decade
  • Demo and Discussion: Self-Driving Car Object Detection
  • Applications of Deep Learning
  • Challenges of Deep Learning
  • Demo and Discussion: Sentiment Analysis Using LSTM
  • Full Cycle of a Deep Learning Project

  • Biological Neuron Vs Perceptron
  • Shallow Neural Network
  • Training a Perceptron
  • Backpropagation
  • Role of Activation Functions and Backpropagation
  • Optimization
  • Regularization
  • Dropout layer

  • Deep Neural Network: Why and Applications
  • Designing a Deep Neural Network
  • How to Choose Your Loss Function?
  • Tools for Deep Learning Models
  • Keras and its Elements
  • Tensorflow and Its Ecosystem
  • TFlearn
  • Pytorch and its Elements

  • Optimization Algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Batch Normalization
  • Exploding and Vanishing Gradients
  • Hyperparameter Tuning
  • Interpretability

  • Success and History
  • CNN Network Design and Architecture
  • Deep Convolutional Models

  • Sequence Data
  • Sense of Time
  • RNN Introduction
  • LSTM (Retail Sales Dataset Kaggle)
  • Word Embedding and LSTM
  • GRUs
  • LSTM vs GRUs

  • Introduction to Autoencoders
  • Applications of Autoencoders
  • Autoencoder for Anomaly Detection