article neural networks from ANN to Tranformers

article-neural-networks/README.md

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+# ANN, CNN, RNN, and Transformers — A Simple Guide
+
+This is a small article I wrote to explain how deep learning models grew over time, in a simple way that anyone can understand.
+
+The idea is simple: every new model was created because the old one had a real problem. Once you see this chain of problems and fixes, picking the right model for your task becomes much easier.
+
+## What's inside
+
+The article walks through the main neural network families step by step:
+
+- **Activation functions** → why they matter, and why ReLU and GELU changed everything
+- **CNNs** → how they made image learning possible
+- **RNNs** → how they added memory for sequences (and why they forget too fast)
+- **LSTM and GRU** → how gates fixed the memory problem
+- **Transformers** → why "attention" changed the whole field
+- **BERT** → a quick look at how big pretrained models work today
+
+You also get:
+
+- 2 comparison tables (activations + architectures) so you can pick a model fast
+- 3 small code examples (PyTorch + HuggingFace) showing how each idea looks in real production code
+- 7 images to help visualize the concepts
+
+## Who is this for
+
+- Engineers who want to understand *why* each model exists, not just memorize names
+- Students who are just starting with deep learning
+- Anyone who wants a clean, simple overview before diving into papers or courses
+
+You don't need a math background. The article uses simple words, short sentences, and real examples.
+
+
+## Main takeaway
+
+Deep learning didn't appear all at once. Each model fixed a real weakness of the one before it:
+
+- ANNs were too heavy for images → CNNs
+- CNNs ignored order → RNNs
+- RNNs forgot too fast → LSTM and GRU
+- LSTMs were too slow → Transformers
+
+If you keep this chain in mind, you'll always know which model fits your task — and why.
+
+You can also read my Medium article on this topic here: 
+
+[Open Medium Article](https://medium.com/p/6ad62a95f98f?postPublishedType=initial)