One Pager Cheat Sheet

• This tutorial focuses on Large Language Models (LLMs), a type of AI that's trained on vast text data to learn the statistical relationships between words and phrases, enabling them to generate text, translate languages, and answer questions informatively.
• Large language models (LLMs) operate using deep learning, specifically utilizing artificial neural networks to learn from data with a core task of predicting subsequent words in a text sequence, employing word embeddings and recurrent neural networks (RNNs) to account for semantic and syntactical relationships among words and process sequential data respectively.
• Large Language Models (LLMs) are a type of artificial intelligence that utilize machine learning and deep learning with artificial neural networks to train on massive amounts of text data. Key technical elements such as word embeddings and recurrent neural networks are used to learn from this data, enabling LLMs to generate its own text using a seed phrase.
• The transformer architecture is a popular LLM (Language model learning) architecture due to its parallel processing and self-attention capabilities, while other popular LLM architectures include Recurrent neural networks (RNNs) for sequential data processing and Convolutional neural networks (CNNs) for spatial data processing.
• Transfer learning is a machine learning strategy where a model, such as a pre-trained Language Model (LLM), is initially trained on a large benchmark dataset to recognize general patterns and then further fine-tuned using task-specific data, leveraging the model's pre-existing knowledge to improve the performance of specific tasks such as text classification or sentiment analysis.
• The tutorial provides steps to build a simple Language Model (LLM) in Python using TensorFlow, spaCy and NLTK libraries, covering aspects like library importation, loading and preprocessing the training data using the Gutenberg corpus, and defining the LLM architecture using either Recurrent Neural Network (RNN) or transformer architecture, with the latter being more powerful but computationally expensive.
• To train the LLM, one needs to create a training dataset from preprocessed text in sequence of words, compile the LLM model with suitable loss function and optimizer, and then train it using the dataset, while evaluating the LLM involves generating text from the LLM model and comparing it with the original text; these steps are implemented with tf.data.Dataset.from_tensor_slices, model.compile, and model.fit for training, and model.predict for evaluation.
• The concept of modern versatile synapses is not a recognized language model architecture in machine learning or deep learning, unlike familiar architectures like Recursive Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer Models (BERT, GPT-2/3, T5), and Convolutional Neural Networks (CNN).
• Transfer learning is a technique where a pre-trained model is fine-tuned for a new task, especially useful in training LLMs to save time and resources.
• Fine-tuning is the process of updating the parameters of a pre-trained model to enhance its performance on a specific task, but it can potentially lead to overfitting where the model cannot generalize to new data.
• The document contains the final code to build and train a Large Language Model (LLM) using Python, TensorFlow, and other libraries on the Gutenberg corpus, and also discusses basic LLM architectures, transfer learning, and fine-tuning.