Learn LLMs from Pros

Hi Readers,

Learning about Large Language Models (LLMs) can be an exciting journey for beginners in the field of machine learning and deep learning. Here’s a step-by-step guide with informal explanations and reference links to help newcomers get started:

1. yUnderstanding the Basics of Machine Learning:
— Start with the basics of machine learning to build a strong foundation. Learn about supervised and unsupervised learning, classification, regression, and clustering.
— Reference: [Machine Learning by Andrew Ng on Coursera](https://www.coursera.org/learn/machine-learning)

2. Introduction to Natural Language Processing (NLP):
— Familiarize yourself with the fundamentals of Natural Language Processing, as LLMs heavily rely on NLP techniques.
— Reference: [Natural Language Processing in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)

3. Exploring Neural Networks:
— Dive into the basics of neural networks, understanding concepts like neurons, activation functions, and backpropagation.
— Reference: [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)

4. Getting Started with Deep Learning:
— Learn about deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
— Reference: [Deep Learning Specialization by Andrew Ng on Coursera](https://www.coursera.org/specializations/deep-learning)

5. Introduction to Transformers:
— Get familiar with the transformer architecture, the backbone of many LLMs.
— Reference: [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)

6. BERT (Bidirectional Encoder Representations from Transformers):
— Understand the key concepts behind BERT, one of the most popular LLMs.
— Reference: [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270)

7. Tokenization and Word Embeddings:
— Learn about tokenization, word embeddings, and how they contribute to understanding language context.
— Reference: [Word Embeddings: A Guide](https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/)

8. Hands-on Practice with LLMs:
— Apply your knowledge by working with pre-trained LLMs like GPT-3 or BERT. Experiment with tasks like text generation or sentiment analysis.
— Reference: [Hugging Face Transformers Library](https://huggingface.co/transformers/)

9. Fine-Tuning LLMs for Specific Tasks:
— Explore how to fine-tune pre-trained models for your specific use case, such as sentiment analysis or named entity recognition.
— Reference: [Fine-tuning BERT for Sentiment Analysis](https://skimai.com/fine-tuning-bert-for-sentiment-analysis/)

10. Stay Updated and Engage with the Community:
— Follow blogs, research papers, and join forums like Reddit or Stack Overflow to stay updated on the latest developments in LLMs.
— Reference: [ArXiv.org](https://arxiv.org/) for research papers, [Reddit — Machine Learning](https://www.reddit.com/r/MachineLearning/)

Remember to combine theoretical learning with practical implementation, as hands-on experience is crucial in mastering LLMs. Happy learning!