The document outlines a comprehensive curriculum on natural language processing (NLP) with deep learning, covering modules on transformers, model fine-tuning, and multimodal NLP. It also discusses advanced AI and machine learning algorithms, including ensemble methods, deep learning architectures, and reinforcement learning techniques. Key tools and frameworks such as Python, TensorFlow, and PyTorch are highlighted for practical applications.

1. Module 4: Natural Language Processing (NLP)
with Deep Learning (5 hours)
Transformers and Attention Mechanisms
Transformer architecture, multi-head attention,
positional encoding
Applications of BERT, GPT, and T5 for NLP tasks
Language Model Fine-Tuning
Fine-tuning GPT-based models for domain-specific
tasks
Adapting models for conversational agents,
summarization, translation
Multimodal NLP
Combining text, speech, and image for NLP tasks

2. Module 4: Natural Language Processing (NLP)
with Deep Learning (5 hours)
Transformers and Attention Mechanisms
Transformer architecture, multi-head attention,
positional encoding
Applications of BERT, GPT, and T5 for NLP tasks
Language Model Fine-Tuning
Fine-tuning GPT-based models for domain-specific
tasks
Adapting models for conversational agents,
summarization, translation
Multimodal NLP
Combining text, speech, and image for NLP tasks

3. Artificial Intelligence, Machine Learning, and Data Science (AIML & DS)
Key Points:- AI algorithms and models
- Machine Learning techniques
- Data preprocessing and visualisation
Tools:- Python, TensorFlow, Keras, PyTorch, Scikit-learn, Pandas, Jupyter Notebook
Module 1: Advanced AI and Machine Learning Algorithms (5 hours)
Ensemble Learning and Boosting Techniques
Advanced boosting algorithms: XGBoost, LightGBM, CatBoost
Stacking, bagging, and boosting: Differences and applications
Advanced Supervised Learning Algorithms
Support Vector Machines (SVM): Non-linear classification and kernels
k-Nearest Neighbors (k-NN) with distance metrics and optimization
Anomaly Detection Algorithms
Isolation forests, DBSCAN for anomaly detection
One-Class SVM for outlier detection in imbalanced data
Module 2: Advanced Deep Learning Architectures (5 hours)
Convolutional Neural Networks (CNNs) and Variants
Residual Networks (ResNet), DenseNet, and Inception Network
Attention mechanisms and Transformer networks for image processing
Recurrent Neural Networks (RNNs) and Variants
Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) networks
Bidirectional LSTM and attention-based mechanisms
Generative Adversarial Networks (GANs)
Advanced GAN architectures: DCGAN, CycleGAN, WGAN
Applications of GANs in image generation, style transfer
Module 3: Reinforcement Learning and Advanced Applications (5 hours)
Deep Q-Networks (DQN)
Training Q-learning agents with deep neural networks
Techniques for stabilizing DQN (Experience Replay, Target Networks)
Policy Gradient Methods
REINFORCE algorithm, Actor-Critic methods
Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO)
Multi-Agent Reinforcement Learning
Cooperative and competitive environments, game-theory-based techniques