https://developers.google.com/machine-learning/resources/intro-llms
Learn what language models and large language models (LLMs) are, how they work, and what they can do. Explore key concepts, use cases, and challenges of LLMs, such as Transformers and self-attention.

https://www.cloudskillsboost.google/course_templates/539
Learn what large language models (LLM) are, how they can be used, and how to tune them with Google tools. This is a free, introductory level micro-learning course with a quiz and a badge.

https://www.youtube.com/watch?v=zizonToFXDs
Enroll in this course on Google Cloud Skills Boost → https://goo.gle/3nXSmLsLarge Language Models (LLMs) and Generative AI intersect and they are both part o

https://github.com/datainsightat/introduction_llm
Learn about the history, architecture, techniques, applications, and limitations of large language models (LLMs) with this introductory repository. Explore popular LLMs like GPT, BERT, and T5, and interact with them using Jupyter notebooks.

https://www.coursera.org/learn/introduction-to-large-language-models
Learn what large language models (LLM) are, how they can be used, and how to tune them with Google tools. This is a free micro-learning course that covers the basics of LLM and Gen AI development.

https://www.baeldung.com/cs/large-language-models
Neural Networks. 1. Introduction. In this tutorial, we'll discuss Large Language Models (LLMs), the force behind several innovations in artificial intelligence recently. This will cover the fundamental concepts behind LLMs, the general architecture of LLMs, and some of the popular LLMs available today. 2.

https://www.cs.princeton.edu/courses/archive/fall22/cos597G/
A graduate course on cutting-edge research topics in natural language processing based on pre-trained language models. Learn about their technical foundations, capabilities, limitations, system design, and ethical challenges.

https://arxiv.org/html/2307.06435v7
This article provides a systematic survey of the recent developments in LLM research, covering diverse topics such as architectural innovations, training strategies, context length improvements, fine-tuning, multi-modal LLMs, and more. It aims to serve as a quick reference for researchers and practitioners to draw insights from the existing works and advance the LLM research.

https://www.pluralsight.com/courses/introduction-large-language-models
Learn what large language models (LLM) are, how they can be used, and how to tune them with Google Cloud tools. This is a beginner-level micro-learning course with 15 minutes of content.

https://deeplearning.cs.cmu.edu/S24/document/slides/lec20.LLM.pdf
Tell the model what to do in natural language. For example, generate a textual summary of this paragraph: Can be as short or long as required. Prompt Engineering. The task of identifying the correct prompt needed to perform a task. General rule of thumb be as specific and descriptive as possible.

https://www.youtube.com/playlist?list=PLDqi6CuDzubwpag1cdbcvaycJneOf96Lg
In this series, Mike Chambers from AWS will onboard you into the world of LLMs (Large Language Models) and provide you with the concepts and steps for you to

https://arxiv.org/pdf/2307.06435
Large Language Models, LLMs, chatGPT, Augmented LLMs, Multimodal LLMs, LLM training, LLM Benchmarking 1.Introduction Language plays a fundamental role in facilitating commu-nication and self-expression for humans, and their interaction with machines. The need for generalized models stems from the growing demand for machines to handle complex

https://learn.microsoft.com/en-us/training/modules/introduction-large-language-models/
Learning objectives. After completing this module, you'll be able to: Explain what a large language model (LLM) is. Describe what LLMs can and can't do. Understand core concepts like prompts, tokens, and completions. Distinguish between different models to understand which one to choose for what purpose.

https://www.udacity.com/course/introduction-large-language-models-google-cloud--cd12959
Learn what large language models (LLM) are, how to use prompt tuning to enhance them, and how to develop Gen AI apps with Google Cloud tools. This is a free microlearning course with no prerequisites and a completion certificate.

https://leena.ai/blog/large-language-models-llms-guide/
Learn what large language models (LLMs) are, how they work, and what applications they have in natural language processing and text generation. This guide covers the evolution, architecture, training, and frameworks of LLMs, as well as their limitations and challenges.

https://arxiv.org/abs/2307.06435
This article provides a systematic survey of the recent developments in LLM research, covering diverse topics such as architectures, training strategies, datasets, benchmarking, and more. It aims to serve as a quick reference for researchers and practitioners to draw insights from the existing literature on LLMs.

https://web.stanford.edu/class/cs124/lec/LLM2024.pdf
Language models. Remember the simple n-gram language model. Assigns probabilities to sequences of words. Generate text by sampling possible next words. Is trained on counts computed from lots of text. Large language models are similar and different: Assigns probabilities to sequences of words. Generate text by sampling possible next words.

https://medium.com/the-llmops-brief/introduction-to-large-language-models-9ac028d34732
Limited generalization: While large language models can perform well on specific language tasks, they may struggle with generalizing to new or unseen data [9]. This can be a challenge in real

https://www.lakera.ai/blog/large-language-models-guide
Emergent Abilities of Large Language Models. We've discussed how LLMs work. But not why. So, why do large language models work? This is an entire area of research outside the scope of this introduction, but, miraculously, deep learning models seem to defy (to a degree) the statistician's notion of over-parameterization.

https://pub.towardsai.net/a-gentle-introduction-to-large-language-models-49cc2d1128fd
Once we grasp the concepts of AI and ML, it becomes essential to understand the significance of Language Models (LLMs). To comprehend LLMs, we must first grasp the meaning of a "model" (which makes up one-third of the term). Think of it as the mind or intelligence behind a machine that learns from data examples, rules, and patterns.

https://attri.ai/blog/introduction-to-large-language-models
Since their introduction, large language models have been used in a variety of tasks, ranging from text understanding and generation to question answering and recommendation systems. They have also been used to power a variety of natural language processing (NLP) applications, such as machine translation and speech recognition.

https://www.youtube.com/watch?v=zjkBMFhNj_g
This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What the

https://www.quantamagazine.org/the-unpredictable-abilities-emerging-from-large-ai-models-20230316/
Large language models like ChatGPT are now big enough that they've started to display startling, unpredictable behaviors. ... Introduction. Language models have been around for decades. Until about five years ago, the most powerful were based on what's called a recurrent neural network. These essentially take a string of text and predict

https://machinelearning.apple.com/research/introducing-apple-foundation-models
Figure 1: Modeling overview for the Apple foundation models. Pre-Training. Our foundation models are trained on Apple's AXLearn framework, an open-source project we released in 2023.It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs.

https://arxiv.org/pdf/2307.05782
Large Language Models Michael R. Douglas CMSA, Harvard University Dept. of Physics, Stony Brook University mdouglas@cmsa.fas.harvard.edu July 2023 ... 1 Introduction At the end of November 2022, OpenAI released a system called ChatGPT which interacts with its users in natural language. It can answer questions, engage in

https://aws.amazon.com/blogs/machine-learning/build-safe-and-responsible-generative-ai-applications-with-guardrails/
Large language models (LLMs) enable remarkably human-like conversations, allowing builders to create novel applications. LLMs find use in chatbots for customer service, virtual assistants, content generation, and much more. However, the implementation of LLMs without proper caution can lead to the dissemination of misinformation, manipulation of individuals, and the generation of undesirable

https://www.mdpi.com/2072-6694/16/13/2311
Purpose: This study aimed to develop a retrained large language model (LLM) tailored to the needs of HN cancer patients treated with radiotherapy, with emphasis on symptom management and survivorship care. Methods: A comprehensive external database was curated for training ChatGPT-4, integrating expert-identified consensus guidelines on supportive care for HN patients and correspondences from

https://developer.nvidia.com/blog/leverage-our-latest-open-models-for-synthetic-data-generation-with-nvidia-nemotron-4-340b/
Since the introduction and subsequent wide adoption of Large Language Models (LLMs) - data has been the lifeblood of businesses building accurate and safe AI systems. A company's data represents its

https://arxiv.org/abs/2406.11831
Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models. However, the paradigm for utilizing current advanced LLMs in text-to-image diffusion models remains to be explored. We observed an unusual phenomenon: directly using a large language model as the prompt encoder significantly degrades

https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.13886
NLP, particularly large language models (LLMs) popularized with the introduction of ChatGPT, has emerged as a powerful tool in health care, showing reliable performance in various tasks. 28-30 By leveraging LLMs, we open up new frontiers in AD research, leading to the development of automated screening tools. Specifically, we consider the