kolodach/llm-study

LLM Study Plan

Large Language Model (LLM) Learning

Study Plan

Week 1: NLP Fundamentals and Python Programming

Study the basics of NLP, including text preprocessing, tokenization, and stemming. Learn Python programming and practice manipulating text data using libraries like NLTK or spaCy. Problem-solving exercise: Implement a text classification model to classify movie reviews as positive or negative.

Week 2: Deep Learning Foundations

Understand the basics of neural networks, including feedforward networks and backpropagation. Implement a simple feedforward neural network using TensorFlow or PyTorch. Problem-solving exercise: Build a sentiment analysis model using a feedforward neural network to classify customer reviews as positive, negative, or neutral.

Week 3: Sequence Modeling

Dive deeper into sequence modeling techniques like recurrent neural networks (RNNs) and LSTMs. Implement an RNN or LSTM model to generate text character by character. Problem-solving exercise: Train an LSTM language model on a text corpus and generate new text samples.

Week 4: Language Modeling

Explore language modeling architectures such as RNNLMs and Transformer-based models. Study techniques like masked language modeling and autoregressive decoding. Problem-solving exercise: Fine-tune a pretrained language model like GPT-2 on a specific text dataset for text completion or generation tasks.

Week 5: Pretrained Models

Familiarize yourself with pretrained models like GPT-3 and understand their architectures. Learn how to integrate and utilize pretrained models for various NLP tasks. Problem-solving exercise: Use a pretrained model like GPT-3 for text summarization or question-answering tasks.

Week 6: Evaluation and Metrics

Understand evaluation metrics for language models, such as perplexity and BLEU score. Learn techniques for evaluating the quality and fluency of generated text. Problem-solving exercise: Evaluate the performance of your language model using perplexity and generate text samples for human evaluation.

Week 7: Advanced Topics

Dive into advanced topics such as transfer learning and multi-modal language understanding. Explore research papers and techniques in these areas. Problem-solving exercise: Implement a transfer learning approach to adapt a pretrained language model for a specific domain or task.

Week 8: Hands-on Projects

Apply your knowledge by working on real-world language modeling projects. Build chatbots, text generators, or other NLP applications. Problem-solving exercise: Develop a chatbot using a language model to provide information or answer user queries. Throughout the study plan, you can supplement your learning with online tutorials, documentation, and resources available for each topic. Also, consider participating in coding challenges, Kaggle competitions, or open-source projects to further practice and showcase your skills.