Accelerated Certificate Course Overview

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Course Overview

Day 0: Pre-requisites

As part of pre-requisites, a 2-hour video would be shared having following details:

  • Installing Python, and other packages/software required for the course
  • Introduction to Python
Day 1: Introduction to the AI/ML (Monday)

Duration: 4 hours Theory + 4 hours Lab

Objectives of the day:

  • Understand elements of statistical learning.
  • Build Linear/Non-linear hypotheses. 
  • Use Loss Functions for regression and classification problems such as least squares, cross-entropy, etc.
  • Find solutions using gradient descent.
  • Understand issues with Bias & Variance.
  • Understand Regularization concepts. 
  • In the lab
    • Perform pre-processing steps on a given dataset
    • Build a regression/classification model, with regularization
    • Report the error metrics
Day 2: Shallow Learning Fundamentals (Tuesday)

Duration: 4 hours Theory + 4 hours Lab

Objectives of the day:

  • Understand Neural Networks (NN) Basics
  • Deep dive into Perceptron concepts,  and limitation
  • Get to know about Back Propagation, and how Gradient Descent is used in Back Propagation
  • Learn practical ways of building Shallow Networks
  • Understand Best Practices, and application to real-world problems. 
  • Learn Neural nets for word2vec representations. 
  • In the Lab:
    • 10-15 minute simple MCQ based quiz (ROTe – Recall Only Test) on topics covered on Day 1 of Week 1
    • Work on the same dataset as used on Day 1, and achieve better accuracies
Day 3: Deep Learning - Multi-Layered Perceptron (Wednesday)

Duration: 4 hours Theory + 4 hours Lab

Objectives of the day:

  • Get to know issues in deepening the nets and techniques to overcome these issues.
  • Learn Deep Learning(DL) Basics. 
  • Deep dive into Regularization, auto-encoders, RELU activation, hyper-parameter tuning and transfer learning. 
  • Hyper parameter tuning
  • In the Lab:
    • 10-15 minute simple MCQ based quiz (ROTe – Recall Only Test) on topics covered on Day 2 of Week 1
    • Learn to remove noise in data, use NN as feature generator for other models, or use NN as a predictor
    • Unsupervised learning using NN. Take a high dimensional data and reduce the dimensionality, and apply clustering
Day 4: Convolution Neural Net - for images (Thursday)

Duration: 4 hours Theory + 4 hours Lab

Objectives of the Day:

  • Start with Architecting a Convolution Neural Network (CNN)
  • Learn the Geometry of CNN
  • Understand practical aspects of building CNN
    • Data augmentation
    • Object Localization
  • How to visualize a convolution net
  • Discuss limitations of Deep Neural Networks, Architecture
  • In the Lab:
    • 10-15 minute simple MCQ based quiz (ROTe – Recall Only Test) on topics covered on Day 3 of Week 1
    • Build CNN, step-by-step, with CIFAR dataset (including augmentation, ensemble)
    • CNN for text (based on latest research paper)
Day 5: Recurrent Neural Net - for Text (Friday)

Duration: 4 hours Theory + 4 hours Lab

Objectives of the Day:

  • Learn basics of Recurrent Neural Networks (RNN)
  • Understand architectural differences
  • Deep dive into Long Short Term Memory (LSTM) nets for text mining and time series
  • Learn how to scale up Deep Neural Network, and other issues
  • In the Lab:
    • 10-15 minute simple MCQ based quiz (ROTe – Recall Only Test) on topics covered on Day 1 of Week 2
    • Build RNN for Entity Extraction
    • Build RNN for Sentiment Classification/Analysis
Final Project submission & evaluation (remote work in the 2nd week)

Objective:

  • ~30  hours during the Project, with primary focus on
    • Pre-Process
    • Build a forecasting or regression model
    • Image Captioning using CNN and RNN
  • An online MCQ based test with 50 Min, 30 questions – 5 PM to 6 PM
  • Submit final project paper the following Friday
* * * * * Graduation * * * * *

Infrastructure access provided:

  • 3 Deep learning servers each has 512 GB of Ram, 4 TB of storage and 40 vCPU’s
  • Each server has 4 * GEFORCE GTX 1080 Ti cards
  • Each graphics card is packed with extreme gaming horsepower, next-gen 11 Gbps GDDR5X memory, and a massive 11 GB frame buffer
  • Users will connect through VPN tunnel to our network/ server to work on projects

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