Natural Language Processing (NLP)
- Basics of NLP:
- Text Preprocessing (Tokenization, Lemmatization, Stop Words, etc.)
- Word Embeddings (Word2Vec, GloVe, FastText)
- Bag of Words, TF-IDF
- Advanced NLP Techniques:
- Sequence Models: RNNs, LSTMs, GRUs
- Attention Mechanism and Transformers
- BERT, GPT, and other Pretrained Models
- Named Entity Recognition (NER), Part-of-Speech Tagging
- NLP Tasks:
- Sentiment Analysis, Text Classification, Machine Translation
- Question Answering Systems, Summarization
- Key Concepts:
- Vector Representation of Text
- Sequence Modeling and Language Modeling
- Self-Attention and Multi-Head Attention Mechanisms
- Hands-On:
- Implementation of Text Classification using RNN/LSTM
- Fine-tuning a Pretrained Transformer Model (BERT or GPT)
- Building a Chatbot using NLP Techniques
- Projects:
- Sentiment Analysis on Social Media Data
- Text Summarization for News Articles
- Machine Translation System (English to French)
Computer Vision (CV)
- Basics of Computer Vision:
- Image Representation and Preprocessing (Resizing, Normalization, etc.)
- Convolutional Neural Networks (CNNs)
- Pooling Layers, Activation Functions
- Advanced Computer Vision Techniques:
- Transfer Learning with Pretrained Models (ResNet, VGG, Inception)
- Object Detection (YOLO, SSD, Faster R-CNN)
- Image Segmentation (UNet, Mask R-CNN)
- Generative Models: GANs, Variational Autoencoders (VAEs)
- CV Applications:
- Image Classification, Object Detection, Image Segmentation
- Face Recognition, OCR, Image Style Transfer
- Key Concepts:
- Convolutional Filters and Feature Maps
- Transfer Learning and Fine-tuning
- Generative Adversarial Networks (GANs) and Their Applications
- Hands-On:
- Building and Training a CNN for Image Classification
- Object Detection using YOLO or Faster R-CNN
- Image Segmentation using UNet
- Projects:
- Face Mask Detection in Real-Time Video Streams
- OCR for Handwritten Digit Recognition
- Style Transfer between Images
Large Language Models (LLMs)
Introduction to Large Language Models:
- Overview of LLMs (GPT, BERT, T5)
- Pretraining and Fine-tuning
- Transfer Learning in LLMs
Advanced LLM Architectures:
- GPT Family (GPT-2, GPT-3, GPT-4)
- T5, BART, and Text Generation Models
- In-context Learning and Prompt Engineering
Applications of LLMs:
- Text Generation and Completion
- Conversational AI (Chatbots, Virtual Assistants)
- Zero-shot and Few-shot Learning
- LLM for Creative Writing and Content Generation
Key Concepts:
- Self-Attention and Transformer Architectures
- Scaling and Pretraining Large Models
- Prompt Design and Optimization
Hands-On:
- Fine-tuning GPT for Custom Text Generation
- Implementing a Conversational AI using GPT or BERT
- Training LLMs for Summarization Tasks
Projects:
- Building a Virtual Assistant using GPT-4
- Generating Creative Stories using LLMs
- Summarizing Long Documents using BART or T5
MLOps for NLP, CV, and LLMs
- MLOps Fundamentals:
- Data Pipelines and Model Deployment
- Monitoring and Maintaining AI Models in Production
- Version Control and Model Management (MLFlow, DVC)
- AI in Production:
- Deployment Strategies (Batch vs. Real-Time Inference)
- Scalable AI Systems in Cloud (AWS, GCP, Azure)
- Model Retraining and Continuous Integration/Continuous Deployment (CI/CD) for AI
- Special Considerations for NLP and CV Models:
- Managing Drift in NLP and CV Models
- Optimization and Latency Reduction in Real-Time Systems
- Key Concepts:
- Best Practices for Model Deployment and Maintenance
- CI/CD Pipelines for AI Systems
- Scaling AI Systems in Cloud Environments
- Hands-On:
- Deploying NLP Models as Web Services using Flask/FastAPI
- Implementing a CI/CD Pipeline for CV Models
- Monitoring and Retraining LLMs in Production
- Projects:
- Deploying a Sentiment Analysis API on Cloud
- Real-Time Object Detection in a Video Stream with Cloud Deployment
- Continuous Retraining Pipeline for a Chatbot