Deep learning is a new superpower which will let you build AI systems that just weren't possible a few years ago. It's time to utilize intelligent automation to help your business grow, keep organized, and stay on top of the competition. 

Why should one take this certification?

Deep Learning is currently enabling numerous exciting applications in speech recognition, music synthesis, machine translation, natural language understanding, and many others. AI is transforming multiple industries.

After finishing this course, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.


Who will benefit from taking this certification?

Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in Deep learning with Python framework benefits Data Science professionals, students and professionals across various Industries.


Companies that hire Vskills Deep Learning with Python Professionals

IT companies, MNCs, Consultancies hire Pytorch professionals for Data Science related opportunities. Companies employing Data Science include Capgemini, JP Morgan Chase, TCS, Wipro, Zensar, Accenture, Infor etc.

Deep Learning with Python Table of Contents

https://www.vskills.in/certification/deep-learning-with-python-table-of-content


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TABLE OF CONTENTS


Module 1

UNDERSTANDING DEEP LEARNING

  • The Course Overview
  • A Brief History of Deep Learning
  • Deep Learning Today
  • Tools, Requirements, and Setup
  • BUILDING THE BASIC BLOCKS OF MACHINE LEARNING
  • Exploring Supervised Learning
  • Representational Learning and Feature Engineering
  • Linear Regression
  • The Perceptron

DIVING INTO DEEP NEURAL NETWORKS

  • Feedforward Networks
  • Backpropagation
  • Neural Networks from Scratch
  • Overfitting and Regularization

DISCOVERING CONVOLUTIONAL NEURAL NETWORKS (CNNS)

  • Understanding CNNs
  • Implementing a CNN
  • Deep CNNs

USING CNNS TO SOLVE INCREASINGLY COMPLEX TASKS

  • Very Deep CNNs
  • Batch Normalization
  • Fine-Tuning

LEARNING ABOUT DETECTION AND SEGMENTATION

  • Semantic Segmentation
  • Fully Convolutional Networks

EXPLORING RECURRENT NEURAL NETWORKS

  • Recurrent Neural Networks
  • LSTM and Advancements

OBJECT DETECTION USING CNNS

  • Building a CNN to Detect General Images
  • Training and Deploying on a Cluster


MOVING FORWARD WITH DEEP LEARNING AND AI

  • Comparison of DL Frameworks
  • Exciting Areas for Upcoming Research


Module 2

GETTING STARTED WITH DEEP LEARNING

  • The Course Overview
  • Fundamentals of Neural Networks
  • Training Deep Neural Networks
  • Using Forward Propagation, Backprop, and SGD
  • Logistic Regression with a Neural Network Mindset
  • Convolutional Neural Network Handwriting Recognition

DEEP MODELS WITH MXNET AND TENSORFLOW

  • Working with MxNet and Gluon
  • Defining and Training Neural Networks in MxNet/Gluon
  • Working with TensorFlow and Keras
  • Defining and Training Neural Networks in Keras/TensorFlow
  • Comparing the Two Frameworks
  • Mini Project - CIFAR Classification

IMPROVING DEEP NEURAL NETWORKS

  • Weight Initialization for Deep Networks
  • Regularization and Dropout
  • Normalizing and Vanishing/Exploding Gradients
  • Mini Project – SIGNS Dataset

OPTIMIZATION ALGORITHMS

  • Understanding Stochastic Gradient Descent
  • Adaptive Learning Algorithms - RMSProp and Adam
  • Mini Project - Language Modeling

HYPERPARAMETER TUNING

  • Hyperparameters
  • Tuning Hyperparameters - Grid Search
  • Tuning Hyperparameters - Random Search
  • Mini Project -Music Synthesis
“Exam scheduling to be done through user account” / “Exam once scheduled cannot be cancelled”
Date of Examination
07-Dec-2019
08-Dec-2019
21-Dec-2019
22-Dec-2019
04-Jan-2020
05-Jan-2020
18-Jan-2020
19-Jan-2020
01-Feb-2020
02-Feb-2020
15-Feb-2020
16-Feb-2020
07-Mar-2020
08-Mar-2020
21-Mar-2020
22-Mar-2020
04-Apr-2020
05-Apr-2020
18-Apr-2020
19-Apr-2020
Examination Time
01:00 PM - 02:00 PM
02:30 PM - 03:30 PM
04:00 PM - 05:00 PM
05:30 PM - 06:30 PM
10:00 AM - 11:00 AM
11:30 AM - 12:30 PM

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Tags: Deep Learning with Python, Python, Deep Learning, Data Science