Deep Learning Training in Pune

deep learning



 1 on 1 sessions


Free cloud access


24*7 call support

Course Features

    VM for practice.


   Free cloud storage


    CCDH preparation



    Video Lectures


    Complete modules


    Multiple batches




Introduction to Data Science

  • Data Science Life Cycle
  • Business Statistics

Introduction to Keras

  • Overview of Keras
  • Installation Procedure
  • Guiding Principles
  • When to use KERAS

RMachine Learning Fundamentals

  • intuition
  • linear & Logistic regression
  • Binary Classification
  • Generalization
  • Regularization L1
  • Regularization L2

  Introduction to TensorFlow

  • Intro to TensorFlow
  • Computational Graph
  • Key highlights
  • Creating a Graph
  • Regression example
  • Gradient Descent
  • TensorBoard
  • Modularity
  • Sharing Variables
  • Keras


  • TensorFlow installation
  • TensorFlow APIs
  • Tensors
  • Importing TensorFlow
  • Building & Running a computational graph
  • Variables: Creation, Initialization, Saving, and Loading
  • Tensor Ranks, Shape, and Types
  • Reading Data
  • Supervisor: Training Helper for Days-Long training
  • TensorFlow Debugger, Command line Interface
  • How to use TensorFlow Debugger with tf.contrib.learn
  • Exporting and importing a MetaGraph
  • TensorFlow version Semantics
  • TensorFlow Data versioning: GraphDefs and Checkpoints
  • TensorBoard: Suite of visualization tools



  • What is a Perceptron
  • Logic Gates with Perceptrons
  • Activation Functions
  • Sigmoid
  • ReLU
  • Softmax
  • Hyperbolic Functions

 How to train ANNs


  • Introduction
  • Perceptron Learning Rule
  • Gradient Descent Rule
  • Minimize Cost Function
  • Tuning Learning Rate
  • Stochastic vs Batch Gradient Descent

Multi-layer ANN


  • Intro to MLP
  • Forward propagation
  • Minimize Cost Function
  • Back propagation
  • Convergence in a neural net
  • Over fitting and Capacity
  • Hyper parameters in an ANN

Training Deep Neural Nets

  • Vanishing/Exploding Gradients
  • Xavier Initialization
  • Leaky ReLUs and ELUs
  • Batch Normalization
  • Transfer Learning
  • Unsupervised Pre-training
  • Optimizers
  • Regularization

Convolutional Neural Networks


  • Intro to CNNs

  • Convolution Operation

  • Convolution Detector Pooling Building Block
  • Convolution Variants
  • Intuition behind CNNs
  • Kernel filter

  • Feature Maps

  • Pooling Operation

  • CNN Architecture

  • Implement CNN in TensorFlow


Recurrent Neural Networks


  • Intro to RNNs

  • Unfolded RNNs

  • Basic RNN Cell

  • Dynamic RNN

  • Training RNNs

  • Time-series predictions

  • LSTM(Long Short Term Memory) with time Series

  • Gradient Clipping 
  • Word Embeddings

  • Seq2Seq Models

  • Implement RNN in TensorFlow



  • Autoencoders
  • Custom Metrics
  • GPU Programming in Cloud
  • Distrubuted TensorFlow
  • Hyperpameter tuning




FAQs For Deep Learning

Software engineers, Data scientists, Data analysts, Statisticians with an interest in deep learning.

Proficiency in Python, Good Knowledge of Machine earning

Yes, we have multiple centers in Pune and Mumbai

Your profile would be evaluated by experts, your resume would be rated and you would start getting calls after completion of your module.

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21st Mar 2020 – Data Science : 2pm/2pm(sun) Online Demo