Deep Learning Techniques in Python for Vision and Language

Mejbah Ahammad -

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.
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