Neural Networks and Machine Learning Lecture Notes

Overview

• Exploration of neural networks and machine learning.
• Focus on pivotal architectures like LLMs, RNNs, LSTMs, GRUs, and Transformers.
• Applications include text prediction, machine translation, sentiment analysis, and chatbots.

Large Language Models (LLMs)

• Example: GPT (Generative Pre-trained Transformer) by OpenAI.
• Trained on vast text data for generating human-like text.
• Capable of understanding context, answering questions, writing content, and generating code.
• "Large" refers to the number of parameters.

Language Models (LMs)

• Not limited by size—can be both small and large.
• Developed using neural networks and machine learning, including MLPs, RNNs, CNNs, and Transformers.
• Trained using large text datasets to learn speech rules and features.

Multilayer Perceptron (MLP)

• Class of feedforward artificial neural networks.
• Consists of input, hidden, and output layers.
• Features nodes (neurons) with nonlinear activation functions.
• Uses backpropagation to adjust weights from output to input layer.
• Suitable for supervised learning tasks like classification and regression.

Recurrent Neural Networks (RNNs)

• Neurons have states and can process sequential data with time dependencies.
• Incorporate loops to maintain information across sequences.
• Use backpropagation through time (BPTT) for training.
• Challenges: vanishing and exploding gradients.

Long Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU)

• Special RNN architectures designed to overcome RNN limitations.
• Efficiently handle long-term dependencies and information preservation.
• GRU provides a simpler alternative to LSTMs.

Transformers

• Introduced in "Attention is All You Need" (Vaswani et al., 2017).
• Handle sequential data using self-attention instead of order-based processing.
• Foundation for models like BERT, GPT, and T5.

Impact on NLP and AI

• Evolution of models like RNNs, LSTMs, GRUs, Transformers, and LLMs advanced NLP.
• Enabled new possibilities for human-computer interaction.
• Future promises more sophisticated AI applications transforming the digital world.

Conclusion

• These models expand boundaries of machine understanding and creation.
• Continue to transform human-computer interaction and digital applications.