Explained

The SyncNet Research Paper, Clearly Explained

Introduction Ever watched a badly dubbed movie where the lips don’t match the words? Or been on a video call where someone’s mouth moves out of sync with their voice? These sync issues are greater...

RAG Explained: Understanding Embeddings, Similarity, and Retrieval

, I walked through constructing an easy RAG pipeline using OpenAI’s API, LangChain, and native files, in addition to effectively chunking large text files. These posts cover the fundamentals of organising a RAG pipeline...

FastSAM  for Image Segmentation Tasks — Explained Simply

segmentation is a well-liked task in computer vision, with the goal of partitioning an input image into multiple regions, where each region represents a separate object. Several classic approaches from the past involved taking...

Reinforcement Learning from Human Feedback, Explained Simply

The looks of ChatGPT in 2022 completely modified how the world began perceiving artificial intelligence. The incredible performance of ChatGPT led to the rapid development of other powerful LLMs. We could roughly say that ChatGPT...

Explained: How Does L1 Regularization Perform Feature Selection?

is the technique of choosing an optimal subset of features from a given set of features; an optimal feature subset is the one which maximizes the performance of the model on the given...

Layers of the AI Stack, Explained Simply

of Contents Introduction The AI space is an enormous and sophisticated landscape. Matt Turck famously does his Machine Learning, AI, and Data (MAD) landscape yearly, and it all the time seems to get crazier and crazier....

Vision Transformers (ViT) Explained: Are They Higher Than CNNs?

1. Introduction Ever for the reason that introduction of the self-attention mechanism, Transformers have been the highest alternative relating to Natural Language Processing (NLP) tasks. Self-attention-based models are highly parallelizable and require substantially fewer parameters,...

6 Common LLM Customization Strategies Briefly Explained

Why Customize LLMs? Large Language Models (Llms) are deep learning models pre-trained based on self-supervised learning, requiring an enormous amount of resources on training data, training time and holding numerous parameters. LLM have revolutionized natural...

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