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Deep Learning Course Slides & Lab Assignments
Deep Learning Course Slides & Lab Assignments
Upload Lab assignments to: Dropbox folder
Upload Lab assignments to: Dropbox folder
Session 1.1:
Session 1.1:
Deep Learning Course Introduction
Session 1.2:
Session 1.2:
Neural Networks Basics: Data, Pre-Processing & Net Architecture, Forward Prop
Lab 1
Lab 1
Follow lecture slides of 1.2 and implement a neural network in python upto the point discussed in the slide
extra points for implementing gradient descent and cost function.
Session 2.1:
Session 2.1:
Neural Networks Basics: Cost function, Gradient Descent & BackProp
Session 2.2:
Session 2.2:
Neural Networks Basics: Numerical Gradient Check, Optimization & Training
Additional topics for session 2.1 and 2.2 (to be uploaded)
Miscellaneous
Miscellaneous
BFGS: Broyden–Fletcher–Goldfarb–Shanno Algorithm, BFGS WIKI, BFGS reference lecture slide
Lab 2
Lab 2
Follow lecture slides 2.1 - 2.2 and implement the remainder of the neural network in python: Back prop, Batch Gradient Descent, Numerical Gradient Check, Training the net, Optimisation
Session 3.1:
Session 3.1:
Convolution Neural Nets - CNN Architecture & Case Studies
Session 3.2:
Session 3.2:
Localization and Detection through CNN
Lab 3
Lab 3
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