Learn how Gradient Descent helps neural networks improve predictions through gradual optimization of weights and biases. Discover the core mechanics of machine learning.

This article breaks down the key mathematical concepts behind neural networks, including weights, biases, and activations, with an example of handwritten digit recognition.

AWS Step Functions vs Azure Logic Apps vs Azure Durable Functions vs Temporal

Take a detour before diving into transformers and explore sub-word tokenization techniques like Byte-Pair Encoding, WordPiece, and Unigram models. Learn how they handle rare words, reduce vocabulary size, and make models more efficient!

Decoding N-Grams: The Heart of Large Language Models

Why am I writing these AI blogs? Discover my journey into AI and LLMs!

Emulates and resolves load balancing problems with gRPC