CervellAi (en)

Excerpts from the Stanford conferences and Yann LeCun's commentary offer an overview of the field of self-supervised learning (SSL), an emerging paradigm in artificial intelligence. The sources explain that SSL allows you to train large-scale deep learning models using untagged data, which addresses the limitation of the need for large-tagged data sets in traditional supervised learning. They discuss how SSL works by defining a pretext task where monitoring is automatically generated from input

We offer an overview of Adversary Generative Networks (GAN), a type of machine learning algorithm that uses an adversarial learning framework with two submodules: a generator and a discriminator. The fundamental concept of GANs is explained with an analogy of a counterfeiter and the police, and generative modeling is deepened, highlighting the problem of intractable normalization constants and how GANs address it. It also examines the loss function used to train GANs, its relationship with zero-sum

The source material, an excerpt from a transcript of the IBM Technology video titled "What is Retrieval-Augmented Generation (RAG)?," explains a framework designed to enhance the accuracy and timeliness of large language models (LLMs). Marina Danilevsky, a research scientist at IBM Research, describes how LLMs often face challenges such as providing outdated information or lacking sources for their responses, which can lead to incorrect answers or hallucinations. The RAG framework addresses these

An overview of word embeddings, explaining that they are numerical representations of words—often in the form of vectors—that capture their semantic and contextual relationships. The need to transform raw text into numbers arises from the inability of most machine learning algorithms to process plain text, making word embeddings a fundamental tool in natural language processing (NLP). The video describes various applications of embeddings, including text classification and named entity recognition