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

Syllabus

 

 

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

  • 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
 

Perseptron

 

  • 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

 

Residual

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

Gallery

More

 

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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