Imagine teaching a child to understand the world. You do not just show them a picture of a dog and […]
BLIP Model Explained: How It’s Revolutionizing Vision-Language Models in AI Read More »
Batch normalization was introduced in 2015. By normalizing layer inputs, batch normalization helps to stabilize and accelerate the training process,
What is Batch Normalization and Why is it Important? Read More »
ModernBERT emerges as a groundbreaking successor to the iconic BERT model, marking a significant leap forward in the domain of
ModernBERT: A Leap Forward in Encoder-Only Models Read More »
The Qwen2.5-1M series are the first open-source Qwen models capable of processing up to 1 million tokens. This leap in
Qwen2.5-1M: Million-Token Context Language Model Read More »
DeepSeek-R1 represents a significant advancement in the field of LLMs, particularly in enhancing reasoning capabilities through reinforcement learning (RL). This
DeepSeek-R1: How Reinforcement Learning is Driving LLM Innovation Read More »
For a long time, the focus in LLM development was on pre-training. This involved scaling up compute, dataset sizes and
Inference Time Scaling Laws: A New Frontier in AI Read More »
Clustering is an unsupervised ML that aims to categorize a set of objects into groups based on similarity. The core
ML Clustering: A Simple Guide Read More »
Anomaly detection, also known as outlier detection, aims at identifying instances that deviate significantly from the norm within a dataset.
Anomaly Detection: A Comprehensive Overview Read More »
Time series forecasting is a statistical technique used to predict future values based on previously observed values, specifically in a
Time Series Forecasting: An Overview of Basic Concepts and Mechanisms Read More »
Generative Pre-trained Transformer (GPT) models have pushed the boundaries of NLP, enabling machines to understand and generate human-like text with
What Is GPT? A Beginner’s Guide To Generative Pre-trained Transformers Read More »
The exponential growth of data in diverse formats—text, images, video, audio, and more—has necessitated the development of AI models capable
Multi-modal Transformers: Bridging the Gap Between Vision, Language, and Beyond Read More »
As machine learning models grow in complexity and size, deploying them on resource-constrained devices like mobile phones, embedded systems, and
ML Model Quantization: Smaller, Faster, Better Read More »
