Deep Learning and Reinforcement Learning Lab (Integrated) BAI701
Course Code: BAI701
Credits: 04
CIE Marks: 50
SEE Marks: 50
Total Marks: 100
Exam Hours: 03
Teaching Hours/Weeks: [L:T:P:S] 3:0:2:0
Note: It’s recommended to run this program in PyCharm IDE for the best experience and easier execution of Python programs.
Design and implement a neural based network for generating word embedding for words in a document corpus.
Write a program to demonstrate the working of a deep neural network for classification task.
Desing and implement a Convolutional Neural Network(CNN) for classification of image dataset.
Build and demonstrate an autoencoder network using neural layers for data compression on image dataset.
Desing and implement a deep learning network for classification of textual documents.
Design and implement a deep learning network for forecasting time series data.
Write a program to enable pre-train models to classify a given image dataset.
Simple Grid World Problem: Design a custom 2D grid world where the agent navigates from a start position to a goal, avoiding obstacles. Environment: Custom grid (easily implemented in Python)
We sincerely appreciate the efforts of VTU Circle for providing useful notes and study materials for students. The resources shared are really helping students in their academic preparation.
We kindly request VTU Circle to also provide notes for the CSE (AI & ML) branch along with updated lab manuals containing proper instructions and guidelines. This would be very beneficial for students of the new scheme and help them in both theory and laboratory sessions.
Thank you for your continuous support to students.