Deep Learning

Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]
Introduction to NN
- Lab 1: Understanding AND, OR, NAND and XOR problem with basic ANN [ Lab1.ipynb ]
- Lab 2: Implementation and training NN with 2 inputs to learn AND, OR, NAND and XOR functions [ Lab2.ipynb ]
- Lab 3: Implementation and training NN with 3 inputs to learn AND, OR, NAND and XOR functions [ Lab3.ipynb ]
Convolutional Neural Network
- Lab4: Convolution Layer and Pooling Layer [ Lab4.ipynb ]
- Lab 5: Implement Digit Recognition using Deep Learning
  - Implementation of LeNet-5 Using Keras [ Lab5.ipynb ]

Lab 7: Model Improvement using Dropout [ Lab7.ipynb ]
Lab 8: Model Improvement using Data Augmentation and Early Stopping [ Lab8.ipynb ]
- Confusion Matrix [ Confusion Matrix Lab.ipynb ]
  - Confusion Matrix Example [ Spreadsheet.xls ]
  - Confusion Matrix Example [ PDF ]
  - Example: Multiclass Confusion Matrix [ PDF ]
Optimization
Lab 9: Implement optimization algorithm [ Lab9.ipynb ]
Lab9a Adam Optimizer.ipynb [Lab9a.ipynb

Recurrent Neural Network (RNN)
- Lab 10: Implementation of RNN with Keras [ Lab10.ipynb ]
- Lab11: Implementation LSTM to Predict Stock Prices [ Lab11.ipynb ]
- Lab12: Image Captioning [ Lab12.ipynb ]
Generative Adversarial Networks
- Lab 13: Text-to-Image Stable Diffusion Model .ipynb [ Lab13.ipynb ]
Autoencoders
- Lab 14: Create an autoencoder to reconstruct image.ipynb [ Lab 14.ipynb ]

Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]
Introduction to NN
- Lab 1: Understanding AND, OR, NAND and XOR problem with basic ANN [ Lab1.ipynb ]
- Lab 2: Implementation and training NN with 2 inputs to learn AND, OR, NAND and XOR functions [ Lab2.ipynb ]
- Lab 3: Implementation and training NN with 3 inputs to learn AND, OR, NAND and XOR functions [ Lab3.ipynb ]
Convolutional Neural Network
- Lab4: Convolution Layer and Pooling Layer [ Lab4.ipynb ]
- Lab 5: Implement Digit Recognition using Deep Learning
  - Implementation of LeNet-5 Using Keras [ Lab5.ipynb ]

Lab 7: Model Improvement using Dropout [ Lab7.ipynb ]
Lab 8: Model Improvement using Data Augmentation and Early Stopping [ Lab8.ipynb ]
Lab 9: Implement optimization algorithm [ Lab9.ipynb ]

Recurrent Neural Network (RNN)
- Lab 10: Implementation of RNN with Keras [ Lab10.ipynb ]
- Lab11: Implementation LSTM to Predict Stock Prices [ Lab11.ipynb ]
Generative Adversarial Networks
- Lab 11:
Autoencoders
- Lab 12:

2023-24
- Internal Assessments
  - IA-1: [ IA-1 QP PDF ] [ IA-1 QP Solution PDF ]
  - IA-2: [ IA-2 QP PDF ] [ IA-2 Solution PDF ]
- Assignments:
  - Assignment #1: QP PDF [ Solution PDF ]
M. Tech 2022-23
- Internal Assessment #1: [ IA1 QP PDF ]
- Assignment #1 : [ Assign1 Question PDF ]
- Practice Quiz #1 [ Google Form ]
- Theory Examination [ June 2023 QP PDF ]