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37 - ITC - Information Technology - Miscellaneous
ITC 230 - Fundamentals of Artificial Intelligence (AI) Deep Learning
| Code | Start Date | Duration | Venue | |
|---|---|---|---|---|
| ITC 230 | 04 November 2024 | 5 Days | Istanbul | Registration Form Link |
| ITC 230 | 09 December 2024 | 5 Days | Istanbul | 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
- Data preparation
- Data import and storage
- Data manipulations with pandas library
- Data transformations – Data wrangling
- Exploratory analysis
- Missing observations – detection and solutions
- Outliers – detection and strategies
- Standarization, normalization, binarization
- Qualitative data recoding
- Recommendations engines and collaborative filtering
- Recommendation data
- User- based and Item- based collaborative filtering
- Association pattern mining
- Frequent itemsets algorithm
- Market basket analysis
Day 2
- ANN Structure
- Biological neurons and artificial neurons
- Non- linear Hypothesis
- Model Representation
- Transfer Function/ Activation Functions
- Typical classes of network architectures
- Feed forward ANN.
- Structures of Multi- layer feed forward networks
- Back propagation algorithm
- Back propagation - training and convergence
- Functional approximation with back propagation
Day 3
- Introduction to Parallel Programming
- Multicore Programming – OpenMP
- Introduction to GPU Programming (NVIDIA GPU”s required )
- CUDA Programming concepts and ideas
- Deep Learning
- Artificial Intelligence & Deep Learning
- Softmax Regression
- Self- Taught Learning
- Deep Networks
- NVIDIA DIGITS
Day 4
- Setting up Keras
- Overview of Keras Features and Architecture
- Overview of Keras Syntax
- Understanding How a Keras Model Organize Layers
- Configuring the Keras Backend (TensorFlow or Theano)
- Implementing an Unsupervised Learning Model
- Analyzing Images with a Convolutional Neural Network (CNN)
Day 5
- Preprocessing Data
- Training the Model
- Training on CPU vs GPU vs TPU
- Evaluating the Model
- Using a Pre- trained Deep Learning Model
- Setting up a Recurrent Neural Network (RNN)
- Debugging the Model
- Saving the Model
- Deploying the Model
- Monitoring a Keras Model with Tensor Board
- Troubleshooting
