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AKTU Deep Learning (BAI701) Syllabus PDF – B.Tech 4th Year
Deep Learning BAI701
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Official AKTU syllabus of Deep Learning (BAI701) for B.Tech 4th Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Deep Learning
Download PDFBAI701 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able:
| CO 1 | To present the mathematical, statistical and computational challenges of building neural networks | K 2 |
| CO 2 | To study the concepts of deep learning | K 2 |
| CO 3 | To introduce dimensionality reduction techniques | K 2 |
| CO 4 | To enable the students to know deep learning techniques to support real-time applications | K 3 |
| CO 5 | To examine the case studies of deep learning techniques | K 6 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | INTRODUCTION : Introduction to machine learning- Linear models (SVMs and Perceptrons, logistic regression)- Intro to Neural Nets: What a shallow network computes- Training a network: loss functions, back propagation and stochastic gradient descent- Neural networks as universal function approximates | 08 |
| II | DEEP NETWORKS : History of Deep Learning- A Probabilistic Theory of Deep Learning- Backpropagation and regularization, batch normalization- VC Dimension and Neural Nets-Deep Vs Shallow Networks-Convolutional Networks- Generative Adversarial Networks (GAN), Semi-supervised Learning | 08 |
| III | DIMENTIONALITY REDUCTION 9 Linear (PCA, LDA) and manifolds, metric learning - Auto encoders and dimensionality reduction in networks - Introduction to Convnet - Architectures – AlexNet, VGG, Inception, ResNet - Training a Convnet: weights initialization, batch normalization, hyper parameter optimization | 08 |
| IV | OPTIMIZATION AND GENERALIZATION : Optimization in deep learning– Non-convex optimization for deep networks- Stochastic Optimization Generalization in neural networks- Spatial Transformer Networks- Recurrent networks, LSTM - Recurrent Neural Network Language Models- Word-Level RNNs & Deep Reinforcement Learning - Computational & Artificial Neuroscience | 08 |
| V | CASE STUDY AND APPLICATIONS : Image net- Detection-Audio Wave Net-Natural Language Processing Word2Vec - Joint Detection-Bioinformatics- Face Recognition- Scene Understanding- Gathering Image Captions | 08 |
Text Books
- Cosma Rohilla Shalizi, Advanced Data Analysis from an Elementary Point of View, 2015.
- Deng & Yu, Deep Learning: Methods and Applications, Now Publishers, 2013.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.
- Michael Nielsen, Neural Networks and Deep Learning, Determination Press, 2015. SWAYAM/NPTEL/MOOCs Certification: https://onlinecourses.nptel.ac.in/noc18_cs41/preview
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