Deep Learning & Applications
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Follow on Instagram for UpdatesDeep Learning & Applications Syllabus — Unit-wise Content
The official GTU curriculum for Deep Learning & Applications (subject code ME02095031) as prescribed for AIAD · Semester 2 divides the course into 5 units, covering 49 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02095031)Unit 1 Fundamentals of Machine Learning
- Four branches of machine learning
- Evaluating machine-learning models
- Data preprocessing
- feature engineering, and feature learning
- Overfitting and underfitting
- The universal workflow of machine learning
Unit 2 Artificial Neural Networks and Deep Neural Networks
- A first look at a neural network
- Data representations for neural networks
- The gears of neural networks: tensor operations
- The engine of neural networks: gradient-based optimization
- Anatomy of a neural network
- Introduction to Keras
- Setting up a deep-learning workstation
- Classifying movie reviews: a binary classification example
- Classifying newswires: a multiclass classification example
- Predicting house prices: a regression example
Unit 3 Deep Learning for Computer Vision
- Introduction to convenets
- The convolution operation
- The max-pooling operation
- Training a convnet from scratch on a small dataset
- The relevance of deep learning for small-data problems
- Downloading the data
- Building your network
- Data preprocessing
- Using data augmentation
- Using a pretrained convnet
- Feature extraction
- Fine- tuning
- Visualizing what convnets learn
- Visualizing intermediate Program Name: Master of Engineering Level: PG Branch: Artificial Intelligence and Data Science Subject Code: ME02095031 Course/Subject Name: Deep Learning & Applications activations
- Visualizing convnet filters
- Visualizing heatmaps of class activation
Unit 4 Deep Learning for Text and Sequences
- Working with text data
- One-hot encoding of words and characters
- Using word embeddings
- Putting it all together: from raw text to word embeddings
- Understanding recurrent neural networks
- A recurrent layer in Keras
- Understanding the LSTM and GRU layers
- A concrete LSTM example in Keras
- Advanced use of recurrent neural networks
- A temperature-forecasting problem
- Preparing the data
- non-machine-learning baseline
- A basic machine-learning approach
- A first recurrent baseline
- Using recurrent dropout to fight overfitting
- Stacking recurrent layers
- Using bidirectional RNNs
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