Course Start Date: October 22, 2024
Total Classes: 25
Schedule: Every Tuesday and Friday, 8:00 PM - 10:00 PM
Delivery Mode: Online via Zoom
Week 1: Introduction to Deep Learning
Class 1: Overview of Deep Learning
Key Terms:
- Deep Learning
- Neural Networks
- Activation Functions (Sigmoid, Tanh, ReLU)
- Supervised vs. Unsupervised Learning
Content:
- Introduction to deep learning concepts, significance, and applications in various fields.
Class 2: Deep Learning Frameworks
Key Terms:
- TensorFlow, PyTorch, Keras
- Tensors
- Computational Graphs
- High-Level vs. Low-Level APIs
Content:
- Overview of popular frameworks, installation procedures, and basic usage for building models.
Week 2: Fundamentals of Neural Networks
Class 3: Neural Network Architecture
Key Terms:
- Neurons and Layers (Input, Hidden, Output)
- Feedforward Network
- Backpropagation
- Weight Initialization
Content:
- Understanding the structure and functioning of neural networks.
Class 4: Loss Functions and Optimization Algorithms
Key Terms:
- Loss Functions (MSE, Cross-Entropy)
- Gradient Descent and Variants (SGD, Adam, RMSprop)
- Learning Rate
- Overfitting and Regularization
Content:
- Exploring how loss functions guide training and the role of optimization in improving model performance.
Week 3: Computer Vision Fundamentals
Class 5: Image Processing Techniques
Key Terms:
- Pixels, Color Spaces (RGB, HSV)
- Image Normalization
- Data Augmentation (Flipping, Rotation, Cropping)
- Convolution and Filtering
Content:
- Essential techniques for preprocessing image data for model training.
Class 6: Convolutional Neural Networks (CNNs)
Key Terms:
- Convolutional Layers
- Pooling Layers (Max Pooling, Average Pooling)
- Feature Maps
- Stride and Padding
Content:
- Detailed exploration of CNN architecture and its effectiveness in image analysis tasks.
Week 4: Advanced Computer Vision Techniques
Class 7: Object Detection Algorithms
Key Terms:
- Bounding Boxes
- Intersection over Union (IoU)
- Mean Average Precision (mAP)
- Region-based CNN (R-CNN), Fast R-CNN, Faster R-CNN
Content:
- Overview of state-of-the-art object detection algorithms and their applications.
Class 8: Advanced Object Detection
Key Terms:
- You Only Look Once (YOLO)
- Single Shot Detector (SSD)
- RetinaNet
- Anchor Boxes
Content:
- Deep dive into modern object detection methods and their implementation.
Week 5: Image Segmentation
Class 9: Semantic Segmentation
Key Terms:
- Semantic vs. Instance Segmentation
- Pixel-wise Classification
- Fully Convolutional Networks (FCN)
Content:
- Techniques for segmenting images into meaningful components for detailed analysis.
Class 10: Instance Segmentation Models
Key Terms:
- Mask R-CNN
- U-Net Architecture
- Object Proposals
Content:
- Exploration of instance segmentation techniques and their real-world applications.
Week 6: Natural Language Processing Basics
Class 11: Text Representation Techniques
Key Terms:
- Tokenization, Stop Words, Stemming, Lemmatization
- Bag-of-Words Model
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word Embeddings (Word2Vec, GloVe)
Content:
- Methods for converting text data into numerical formats suitable for modeling.
Class 12: Recurrent Neural Networks (RNNs)
Key Terms:
- Sequence Data
- Hidden States
- Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRU)
Content:
- Understanding RNN architectures and their ability to process sequential data.
Week 7: Advanced NLP Techniques
Class 13: Sequence-to-Sequence Models
Key Terms:
- Encoder-Decoder Architecture
- Attention Mechanism
- Teacher Forcing
Content:
- Applications of sequence-to-sequence models in tasks like translation and summarization.
Class 14: Natural Language Understanding (NLU)
Key Terms:
- Named Entity Recognition (NER)
- Intent Recognition
- Slot Filling
- Dependency Parsing
Content:
- Techniques for extracting structured information from unstructured text data.
Week 8: Generative Models
Class 15: Generative Adversarial Networks (GANs)
Key Terms:
- Generator and Discriminator
- Adversarial Training
- Conditional GANs
- CycleGAN
Content:
- Understanding how GANs generate new data and their diverse applications in various fields.
Class 16: Variational Autoencoders (VAEs)
Key Terms:
- Latent Variables
- Reconstruction Loss
- Sampling Techniques
Content:
- Overview of VAEs and their capability to learn efficient representations of data.
Week 9: Model Evaluation and Deployment
Class 17: Model Evaluation Techniques
Key Terms:
- Confusion Matrix
- Precision, Recall, F1-Score
- ROC Curve and AUC
- Cross-Validation
Content:
- Metrics for evaluating model performance and techniques for assessing generalization.
Class 18: Deployment Strategies
Key Terms:
- Model Serving
- API Endpoints
- Docker Containers
- Cloud Deployment (AWS, Azure)
Content:
- Practical guidance on deploying models to production environments and making them accessible.
Week 10: Practical Applications and Case Studies
Class 19: Computer Vision Applications
Key Terms:
- Facial Recognition
- Medical Imaging (e.g., tumor detection)
- Augmented Reality (AR)
Content:
- Real-world applications of computer vision techniques across various sectors.
Class 20: NLP Applications
Key Terms:
- Sentiment Analysis
- Chatbots and Conversational Agents
- Text Classification
- Document Summarization
Content:
- Exploration of practical NLP applications and their importance in enhancing user experiences.
Week 11: Future Trends and Ethical Considerations
Class 21: Trends in Deep Learning
Key Terms:
- Explainable AI (XAI)
- Transfer Learning
- Few-Shot Learning
- Reinforcement Learning
Content:
- Discussion of emerging trends in deep learning and their potential impact on technology.
Class 22: Ethics in AI
Key Terms:
- Bias in AI Models
- Data Privacy
- Fairness and Accountability
- Responsible AI Practices
Content:
- Ethical considerations surrounding AI development and deployment, focusing on bias and fairness.
Week 12: Hands-On Projects
Class 23: Project Setup and Data Collection
Key Terms:
- Data Sourcing
- Preprocessing Techniques
- Exploratory Data Analysis (EDA)
Content:
- Guidelines for selecting project topics, gathering datasets, and preparing them for analysis.
Class 24: Project Development (Part 1)
Key Terms:
- Model Architecture Design
- Training and Validation Phases
- Hyperparameter Tuning
Content:
- Step-by-step implementation of a computer vision project (e.g., image classification).
Class 25: Project Development (Part 2)
Key Terms:
- Performance Metrics
- Model Deployment
- User Testing and Feedback
Content:
- Completing an NLP project (e.g., sentiment analysis) and preparing for presentation.
Additional Features
- Interactive Q&A Sessions: Live Q&A at the end of each class for real-time clarification of doubts.
- Collaborative Learning: Group projects and peer review sessions to enhance teamwork and collaborative problem-solving.
- Recorded Sessions: All classes will be recorded for student access to review and reinforce learning.
- Internship Certification: Offered upon successful completion of the course, highlighting hands-on project experience and mastery of deep learning skills.