Faster Training of Large Language Models with Parallelization
Implementing Faster Training of Large Language Models with Parallelization. Learn about key parallelism techniques for GPUs and TPUs to accelerate training.
Tag Archives: Deep Learning
Key Evaluation Techniques for LLMs
Large Language Models, or LLMs for short, have attracted a lot of attention in past few years due to their amazing capabilities. As a result, there is a growing demand to integrate LLMs in production applications. Before putting them into production, it is critical to design proper evaluation strategies to ensure their positive business impact. This article “Key… Read More »
A Gentle Introduction to Large Language Models
This article, “A Gentle Introduction to Large Language Models“, uncovers the high level science and intuition behind the very popular ‘Large Language Models’ along with their key real-world applications. This article covers the following key topics: Check out my article on “Beginner Friendly Introduction to GenAI and Its Applications“ Let’s learn about these topics in more details. 1.… Read More »
Beginner Friendly Introduction to GenAI and Its Applications
This article, “Beginner Friendly Introduction to GenAI and Its Applications“, aims at giving the readers a high level introduction to GenAI. This article covers the following important topics about GenAI and its applications in the real world applications. Let’s now learn about these topics one by one. 1. What is GenAI? GenAI, more specifically Generative AI, refers to… Read More »
Understanding Adversarial Examples and Defence Mechanisms
Adversarial Examples and Defence Mechanisms Adversarial examples are inputs to Machine Learning (ML) models that are intentionally designed to fool the model. These examples are quite easy to generate and can be created by performing intentional feature perturbation on the inputs. And, as a result they can make the ML models do false predictions. In this article, we… Read More »
Best Practices for training stable GANs
Training stable GANs Generative Adversarial Networks, or GANs for short, are quite difficult to train in practice. This is due to the nature of GAN training where two networks compete with each other in a zero-sum game. This means that one model improves at the cost of degradation in the performance of the other model. This contest makes… Read More »
Understanding Failure Modes of GAN Training
Understanding Failure Modes of GAN Training The idea of two competing neural networks is no doubt interesting; where, at each step one of them attempts to defeat the other one and in the process, both networks keep getting better at their job. But building such a dynamic training system is not always feasible. Generative Adversarial Networks, or GANs,… Read More »
Image Synthesis using Pixel CNN based Autoregressive Generative Model
Image Synthesis using Pixel CNN based Autoregressive Generative Models Recent advances in the field of deep learning have led to the development of complex generative models that are capable of generating high quality content in the form of text, audio, pictures, videos and so on. Generative models that make use of deep learning architectures to tackle the task… Read More »
What are Autoregressive Generative Models
What are Autoregressive Generative Models? The term ‘autoregressive’ is taken from the field of time-series forecasting frameworks. Where, In order to make a future prediction, a model considers all the past observations in a timely manner. Autoregressive generative models are also quite similar in nature. They also take help from all their past predictions in order to decide… Read More »
How does a Generative Learning Model Work?
Generative Learning refers to a special class of statistical models that are capable ofgenerating content that is very hard to distinguish from the reality (or fake content thatlooks real). The generated content could be poems, images, music, songs, videos, 3Dobjects or content from some other domain we could imagine. A domain is nothing but a fancy word for… Read More »