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AKTU Deep Learning Lab (BAI751) Syllabus PDF – B.Tech 4th Year
Deep Learning Lab BAI751
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- Semester 7
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Official AKTU syllabus of Deep Learning Lab (BAI751) for B.Tech 4th Year – 1 credits, L-T-P 0-0-2. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Deep Learning Lab
Download PDFBAI751 · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of course, the student will be able to
| CO 1 | Understand the fundamentals of neural networks and activation functions | K2 |
| CO 2 | Implement and visualize basic feedforward and convolutional neural networks | K4 |
| CO 3 | Train, validate, and evaluate deep learning models on standard datasets | K5 |
| CO 4 | Use popular deep learning libraries such as TensorFlow/Keras or PyTorch | K3 |
| CO 5 | Apply deep learning concepts to real-world use cases like image classification and digit recognition | K5 |
Course Content
- Write a python program to Implement Perceptron for understanding single-layer neural network?
- Write a program to Visualize Activation Functions (Sigmoid, ReLU, Tanh)?
- Write a program to Build a Simple Feedforward Neural Network?
- Write a program to MNIST Digit Classification using Keras?
- Write a program to Create and Visualize CNN Layers .
- Write a program to CIFAR-10 Image Classification for understanding Multiclass image classification using CNN.
- Write a program to implement Image Augmentation Techniques for application of preprocessing & transformation?
- Write a program by Use of Dropout for Regularization to Improve model generalization?
- Write a program to Build Model Using PyTorch?
- Write a program to Compare Training with and without Batch Normalization for Analyzing performance and convergence? Note: The Instructor may add/delete/modify/tune experiments
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