Explore neural networks, transformers, large language models, computer vision,
generative AI, diffusion models, reinforcement learning, and the latest
breakthroughs shaping the future of Artificial Intelligence research.
Self-attention, positional embeddings, and multi-head attention -
a ground-up walkthrough of how transformer networks power ChatGPT,
Gemini, Claude, and every major large language model in production today.
We cover architecture variants, scaling laws, and what comes next.
Step-by-step implementation of ViT - patch embeddings, positional encodings, and the classification head - trained on CIFAR-10 with a detailed ablation study.
An analysis of compute-optimal training, Chinchilla's findings, and how model size and data volume interact - with implications for the next generation of frontier models.
How denoising diffusion probabilistic models work, why they outperform GANs on image generation, and a walkthrough of the latent diffusion architecture behind Stable Diffusion.
Suchibrata Patra
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MLflow, Weights & Biases, and containerised serving - a complete production checklist.
6 Jun 2026 · 8 min read
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About Deep Learning
Deep learning is a subfield of machine learning that uses multi-layered
artificial neural networks to learn representations directly from raw data -
images, text, audio, and beyond. Unlike traditional ML pipelines that depend
on hand-crafted features, deep networks learn hierarchical features
automatically, making them extraordinarily powerful for perception tasks.
Transformers and Large Language Models
The transformer architecture, introduced in Attention Is All You Need
(Vaswani et al., 2017), replaced recurrent networks as the dominant paradigm
in NLP. Today, GPT-4, Gemini, Claude, and Llama 3 are all built on
transformer variants. Research on scaling laws (Hoffmann et al., 2022) showed
that both data and model size must grow together for efficient training -
a finding that reshaped how frontier labs budget compute.
Generative AI and Diffusion Models
Diffusion models - DALL·E 3, Stable Diffusion, Midjourney - have
supplanted GANs for image synthesis. Their training stability and sample
diversity make them the go-to choice for multimodal generation. Recent work
extends diffusion to video (Sora), audio, and protein structure prediction.
Reinforcement Learning from Human Feedback
RLHF and its variants (DPO, ORPO) are the techniques behind the alignment
of large language models. Combining supervised fine-tuning with a reward
model trained on human preferences produces the conversational assistants
widely used today.
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