Module 5 Deep Learning with nlp and.pptx
- 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