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: Artificial Neural Networks
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 »
Variational AutoEncoders and Image Generation with Keras
This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders ..etc), Variational Autoencoders (VAEs) are generative models like GANs (Generative Adversarial Networks). This article is primarily focused on the Variational Autoencoders and… Read More »
Sentiment Classification with Deep Learning: RNN, LSTM, and CNN
Sentiment classification is a common task in Natural Language Processing(NLP). There are various ways to do sentiment classification in Machine Learning (ML). In this article, we talk about how to perform sentiment classification with Deep Learning (Artificial Neural Networks). In my previous two articles, We have already talked about how to perform sentiment analysis using different traditional machine… Read More »
Optimizers explained for training Neural Networks
Overview Training a Deep Learning model (or any machine learning model in fact) is all about bringing the model predictions (model output) close to the real output(Ground truth) for a given set of input-output pairs. Once the model’s results are close to the real results our job is done. To understand how close model predictions are with respect… Read More »