TensorFlow Deep Learning Training focuses particularly for the developers intending to learn Machine Learning Problems. The complete Open Source software library for the flow of data across the range of tasks leads to the development of TensorFlow Application.


  • Understanding the basic understanding of Linear Algebra and Calculus
  • Designing systems having capability to detect object in images
  • Understanding the concept of Neural Networks in detail


  • Gaining Expertise in Depth Knowledge on TensorFlow API and primitives
  • Understanding concept of training and tuning ML system with TensorFlow
  • TensorFlow concept with Convolutional Networks, recurrent Networks and LSTMs
Duration: 2 Days

Course Content:
  1. Basic Overview of Deep Learning

    • Understanding Deep Learning Primitives
    • Overview of Architecture in Deep Learning
  2. Introduction to TensorFlow Primitives

    • Overview of Tensors
    • Understanding basic computations in TensorFlow
    • Dealing with Imperative and Declarative Programming
  3. Understanding Linear and Logistic Regression

    • Using Mathematical Review
    • TensorFlow Learning concepts
    • Training Linear and Logistics Models
  4. Overview of Fully Connected Deep Networks

    • Introduction to Fully Connected Deep networks
    • Understanding Neurons in FCN
    • Concept of Training Fully Connected Neural Networks
    • TensorFlow Implementation in detail
  5. Understanding concept of Hyperparameter Optimization in detail

    • Overview of Model evaluation and Hyperparameter Optimization
    • Understanding the Metrics, Metrics and Metrics

      • Using Binary Classification Metrics
      • Using Multiclass Classification Metrics
      • Using Regression Metrics
    • Dealing with Hyperparameter Optimization Algorithms
  1. Overview of Convolutional Neural Networks

    • Basic Introduction to Convolutional Architectures
    • Basic Applications for Convolutional Architectures
    • Training a Convolutional Network in TensorFlow
  2. Dealing with Recurrent Neural Networks

    • Understanding Recurrent Architecture
    • Using recurrent Cells
    • Understanding Application of Recurrent Models
    • Using Neural Turing Machines
    • Working with Recurrent Neural Networks in practice
    • Processing of Penn Treebank Corpus
  3. Training Large Deep Networks

    • Using Custom hardware for Deep Networks
    • Understanding CPU Training
    • Dealing with Distributed Deep Network Training
    • Using Data Parallel Training with Multiple GPUs