Deep Learning.

Neural networks beyond the hype cycle - architectures, training dynamics, and when deep learning actually beats the boosted-tree baseline (and when it does not).

01Recurrent Neural Networks Explained for Sequential DataLearn how recurrent neural networks process sequences, preserve context through hidden states, train with backpropagation through time, and compare LSTMs with GRUs.29 min read 02Weights in LLMs: what they are and how they workWeights are the learned parameters that map token embeddings to logits. Learn how training updates them, what they store, and why they matter.22 min read 03Understanding Transformer Models in AIExplore the power of transformer models in AI, their architecture, applications, and impact across industries.12 min read 04LLM (Large Language Models): An Introduction1. Introduction In the realm of artificial intelligence, Large Language Models (LLMs) have emerged as a groundbreaking force. These models, which are capable of understanding and generating human-like text, are reshaping industries, from business to healthcare. But what exactly a6 min read 05GPT: How, What, Why?GPT, or Generative Pretrained Transformer, is an AI model that uses deep learning to generate text. It has revolutionized natural language processing and has applications in various fields. In this article, we will explore how GPT works and its impact on the world of AI. What is 5 min read