Artificical Intelligence
LLM Inferencing Basics
LLMs inferencing costs way more than its training. One might think that by optimizing model training and its architecture, its inferencing will also be optimal. But this is not the case. LLMs generates there output iteratively and most of the time it is memory bound and not compute bound. Inference optimization techniques like KV cache, continuous batching, inflight batching, We're going to dive pretty deep into the rabbit hole, and having a good understanding of machine learning and deep learning is a must for this blog.
November 1, 2023
LLM Basics
The key characteristic of Large Language Models (LLMs) is their ability to understand and generate human language. LLMs based on transformer architecture are mainly of three types i.e., Mask Language Model (predict the masked words based on the surrounding context), Causal Language Model (predict the next word in a sequence given the preceding words) and Seq-to-Seq Model (translation, summarization etc.)
October 27, 2023
Generative Adversarial Networks
In this post, we’ll delve deep into Generative Adversarial Networks (GANs) architecture, its loss function and evaluation metrics. We’ll also explore the diverse types of GANs and its alternatives that have been pushing the boundaries of what’s possible in AI.
October 24, 2023
LLM Verse
In this post, we’ll provide an overview of different types of Large Language Models (LLMs), focusing on encoder, decoder, and encoder-decoder architectures.
October 1, 2023
Hypothesis Evaluation
Before making claims about the accuracy of a particular trained model or any hypothesis, it is crucial to conduct statistical significance tests. This ensures that the claimed accuracy is reliable and not just a result of chance. It is essential not only in the context of machine learning or deep learning models but also in various other experiments.
April 1, 2023
Reinforcement Learning : Tabular Solution Methods
Multi-armed Bandits The multi-armed bandit problem serves as an introductory concept in reinforcement learning, much like how the house pricing problem acts as a starting point in machine learning. In the K-armed bandit problem, an agent is presented with a set of ‘k’ actions to choose from, and the agent’s decision results in a corresponding reward. Value of an action is the expected or mean reward given when that action is selected.
September 2, 2020
Reinforcement Learning : Fundamentals
Reinforcement learning is the process of discovering what to do in order to maximise a numerical reward signal. A reinforcement learning system’s primary sub-components are policy, reward signal, value function, agent, and environment.
September 1, 2020