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Deep Learning for Intermediate

Deep Learning
13
Mar

Duration:

3 days or 24 hours

Pre-requisites:

  • knowledge on python programming is appreciated
  • Knowledge on Basics of image processing
  • Note 1: Pre-requisites are strictly to be considered for the smooth maneuver of the workshop
  • Note 2: Case studies will be covered in much detail and will be implemented in real time.

 Detailed Coverage:

  • Session-1 -Introduction:
  • What does artificial intelligence really mean?
  • Environment Setup for tensorflow
  • Session -2 – Python Basics:
  • Crash Course on Python
  • Session-3 – Image Basics:
  • Loading, Displaying and saving Images.,Drawing Operations.
  • Image Basic operations
  • Kernals
  • Morphological Operations,Gradient &Edge Detection
  • Thresholding
  • Contours
  • Session – 4 – Feature Extraction:
  • Color Channel Statistics
  • Color Histograms
  • Local Binary Patterns
  • HOG
  • Key Point Detectors
  • Session – 5 – Machine Learning:
  • K-Nearest Neighbors
  • Support Vector Machines
  • Logistic Regression
  • Random forests
  • Case Studies
  • Session – 6 – Introduction to Deep Learning:
  • Machine learning Algorithms and primer
  • Why Deep learning is becoming so popular?
  • Introduction to Deep Neural Networks
  • Sneak peek into open source python libraries
  • Session – 7 – Convolution Neural Network:
  • The perception Algorithm
  • Multi-layer Network
  • Develop your first neural network using keras
  • Diving deep in to Convolution Neural Network – CNN Primer
  • It is all about dataset!! And How to gather them
  • Session – 8 – CNN Implementation:
  • Training your own CNN (Shallow network) for Mnist Dataset
  • Training a Very deep CNN for Mnist Dataset
  • Improving Model Performance with Image Augmentation
  • Object Detection using CNN for CIFAR 10 dataset
  • Session – 9 – CNN Architechures:
  • Implementing CNN Architechures o LeNet o KarpathyNet o MiniVGGNet o Over Feat Framework
  • Session – 10 – ImageNet architechures:
  • State of the art ImageNet algorithms
  • Flower 17 Classification using
  • VGG16 o VGG19
  • Inception-v3 o ResNet50
  • Squeeze net CNN a simplified model for remote processor
  • Session – 11 – Case Study:
  • Case Study 1 – Emotion Detection
  • Case Study 2 – Age and gender Detection
  • Case Study 3 – Car Model Identification
  • Session – 12 – Best Practices:
  • Creating your own state of the art accuracy using Ensemble of features ,Creating Datasets and Primer

Enroll Now

Course Content

Total learning: / 1 quiz Time: 3 days

Instructor

4.5

2 rating

5 stars
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  • Vikas R

    Great Experience

    I was looking for a program that would provide a strong foundation in coding with an emphasis on the tools and methods used in Data Science. I was excited to get a comprehensive introduction to a number of machine learning methods and theory.
  • Kiran

    "Great Academy for Transitioning to Data Science"

    The academy provided me the training and guidance build a small portfolio of data science projects, so that I was able to secure a job as a Senior Data Analyst at a great company.
Free

Money-Back Guarantee, Condition Applied...

Includes

  • Real time virtual classess
  • Pre course reading material
  • Suppliment resources
  • Language: English
  • Certificate of completion

Enroll Now

Your Dream Course Is Only A Step Away




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Your Dream Course Is Only A Step Away




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Your Dream Course Is Only A Step Away




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