A Blog About Neural Networks and Code

Welcome to the blog where programming meets artificial intelligence. Learn, experiment, and get inspired with me.

Neural Network Graphic
AI Concept
Building Neural Networks from Scratch

Dive into the structure of modern neural network architectures — from linear regression to transformers. All code examples are in Python and PyTorch.

Regular articles, tutorials, and reviews of new approaches — subscribe to stay up to date with AI technologies.

What This Blog Is About

This blog is not just about theory. I share real-world experience in building AI projects, working with models, optimizing and deploying them.

  • Examples & Source Code

    Each post includes source code and explanations available on GitHub.

  • Framework Overviews

    I compare TensorFlow, PyTorch, Keras, and other deep learning tools.

  • From Idea to Production

    Explaining how to turn ML models into real-world applications.

Programming AI
Neural Nets from Scratch

Explaining how to build neural networks without libraries. Just Python and NumPy.

Working with PyTorch

Implementing modern architectures: CNN, RNN, GAN, ViT, LLM.

MLOps & Production

Integrating models into real-world apps: Docker, APIs, monitoring.

Fine-tuning LLMs

How to fine-tune large language models for your own tasks and data.

AI in Games

Creating bots and NPC behaviors using machine learning.

AI News Digest

Weekly digests of the latest research papers and tool releases.

A Blog About Neural Networks and Code

Welcome to the blog where programming meets artificial intelligence. Learn, experiment, and get inspired with me.

Read More
Neural Network Graphic
AI Concept
Building Neural Networks from Scratch

Dive into the structure of modern neural network architectures — from linear regression to transformers. All code examples are in Python and PyTorch.

Regular articles, tutorials, and reviews of new approaches — subscribe to stay up to date with AI technologies.

What This Blog Is About

This blog is not just about theory. I share real-world experience in building AI projects, working with models, optimizing and deploying them.

  • Examples & Source Code

    Each post includes source code and explanations available on GitHub.

  • Framework Overviews

    I compare TensorFlow, PyTorch, Keras, and other deep learning tools.

  • From Idea to Production

    Explaining how to turn ML models into real-world applications.

Programming AI
Neural Nets from Scratch

Explaining how to build neural networks without libraries. Just Python and NumPy.

Working with PyTorch

Implementing modern architectures: CNN, RNN, GAN, ViT, LLM.

MLOps & Production

Integrating models into real-world apps: Docker, APIs, monitoring.

Fine-tuning LLMs

How to fine-tune large language models for your own tasks and data.

AI in Games

Creating bots and NPC behaviors using machine learning.

AI News Digest

Weekly digests of the latest research papers and tool releases.

Frequently Asked Questions

If you're starting your journey with neural networks, here are answers to common questions my readers ask.

Where should I start learning neural networks?

I recommend starting with Python, the NumPy library, and linear algebra basics. Then move on to TensorFlow or PyTorch.

Don’t just read — replicate examples and build your own mini-projects.

Email: [email protected]
Do I need a powerful computer?

Not at the beginning. Many things can be run on CPU or in Google Colab. Later on, a GPU becomes useful.

Which courses would you recommend?

FastAI, DeepLearning.ai on Coursera, and Stanford's CS231n are excellent starting points. I also review them on this blog.

What’s the difference between LLMs and regular models?

LLMs (Large Language Models) are trained on massive text corpora and capable of handling a wide range of tasks: from translation to code generation.