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35 - ITC - Information Technology - Miscellaneous

ITC 231 - Artificial Intelligence (AI) Deep Learning

Code Start Date Duration Venue Fees
ITC 231 14 August 2021 5 Days Istanbul $ 3950 Registration Form Link
ITC 231 11 September 2021 5 Days Istanbul $ 3950 Registration Form Link
ITC 231 09 October 2021 5 Days Istanbul $ 3950 Registration Form Link
ITC 231 06 November 2021 5 Days Istanbul $ 3950 Registration Form Link
ITC 231 04 December 2021 5 Days Istanbul $ 3950 Registration Form Link


Course Description

Today, Artificial Intelligence (AI) is a thriving field with many practical applications and active research topics. We look to intelligent software to automate routine labor, understand  speech or images, make diagnoses in medicine and support basic scientific research.

In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straight-forward for computers—problems that can be described by a list of formal, math-ematical rules. The true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally—problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images.

Course Objectives

  • Understanding Machine Learning with ANN and CNN
  • Handling Image and Video data for Machine Learning
  • Discussing the concept of Deep Learning and Computer vision with examples
  • Introduction and setting up Libraries that support Computer Vision
  • Getting hands on experience on each of the concepts

Who Should Attend?

  • Data Scientists
  • Data Engineers
  • Data Architects

Course Details/Schedule

Day 1

  • Artificial Intelligence & Deep Learning
  • Deep Learning
  • Self- Taught Learning
  • Deep Networks
  • Introduction to ANN and CNN
  • Introduction to Parallel Programming
  • Multicore Programming – OpenMP
  • Introduction to GPU Programming (NVIDIA GPU’s required )
  • CUDA Programming concepts and ideas
  • Softmax Regression

Day 2

  • ANN Structure with biological neurons and artificial neurons 
  • Forward propagation flow of ANN with pictorial representation of ANN 
  • Different types of ANN & their typical usage in different domains
  • Different types of transfer or activation functions used in ANN
  • Multiclass classification
  • Back propagation algorithm for updating & optimizing weights in ANN
  • Training and convergence, functional approximation with back propagation
  • Gradient descent: full vs batch vs stochastic gradient descent
  • Different types of error function available to use with SGD

Day 3

  • CNN architecture & how it works
  • CNN layers & their functionality 
  • Different variations of CNN
  • Data augmentation 
  • Batch normalization
  • Introduction to OpenCV & image operations 
  • Complete lifecylce of developing a computer visionmodel 

Day 4

  • Hands on training to build, train & use a Computer Vision Model from scratch. 
  • Understanding and using pre-trained Computer Vision Models
  • Keras, Tensor Flow, DLib are preferred libraries for this section

Day 5

  • Object detection (e.g. Person or Face Detection)
  • Object classification (e.g. YOLO)
  • Object recognition (e.g. Face Recognition, FaceNet Model)
  • Object tracking (Person Tracking)