Deep Learning Techniques
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Follow on Instagram for UpdatesDeep Learning Techniques Syllabus — Unit-wise Content
The official GTU curriculum for Deep Learning Techniques (subject code ME02062061) as prescribed for IOT · Semester 2 divides the course into 6 units, covering 34 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 (ME02062061)Unit 1 Introduction to IoT and Deep Learning: Introduction to
- IOT: layer architecture
- key challenges in iot
- Introduction to deep learning: AI vs ML vs DL
- Role of deep learning in IOT
Unit 2 Machine Learning Foundations for IoT: Overview of
- Machine Learning Algorithms: Supervised
- Unsupervised, and Reinforcement Learning
- Key Concepts: Feature Extraction
- Overfitting, and Generalization
- Data Collection from IoT Devices: Preprocessing
- Data Cleaning
- Feature Engineering
Unit 3 Fundamentals of Deep Learning: Artificial neuron, ANN,
- forward and back propagation
- activation function
- loss function
- learning rate
- Introduction to Deep Learning Architectures: Feedforward Networks
- Training DL models
- ( Overfitting and Under fitting
- train- test-validation
- batch size
Unit 4 Deep Learning for Time-Series and Streaming IoT Data
- Time-Series Data in IoT: Sensors
- Streaming Data, and Challenges
- Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRU)
- Real-time Data Processing with Edge AI
Unit 5 Convolutional Neural Networks for IoT Applications: IoT
- Applications using CNN: Image Recognition
- Object Detection, and Smart Surveillance
- CNN Architectures: LeNet
- AlexNet, VGG, ResNet
- Edge AI and Lightweight CNN Models for Embedded IoT Devices
Unit 6 Deep Learning and IoT Security: Security Challenges in
- IoT Networks
- Intrusion Detection using Deep Learning Models
- Anomaly Detection in IoT Data
- Adversarial Attacks and Defenses in Deep Learning Models for IoT
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