AI - What powers it?

Now that I’ve mapped out how companies actually use AI, I’m shifting gears to learn the hard backend side: the stuff that powers models, not just prompts.

I’m giving myself one month to build real foundations in PyTorch, TensorFlow, and deep learning, with hands-on work every week.

Here’s the plan: Week 1 – Foundations Python for data science, NumPy, pandas, ML basics, and just enough linear algebra and statistics to understand what the models are doing.

Week 2 – Deep Learning Basics Intro to TensorFlow and PyTorch. Tensors, datasets, training loops, loss functions, and building first neural networks (think MNIST-level, but done right).

Week 3 – Model Engineering CNNs, sequence models, transfer learning, data pipelines, metrics, and debugging. Focus on making models train reliably, not magically.

Week 4 – Performance & Project GPU basics, fine-tuning pretrained models, saving/loading models, inference pipelines, and a small end-to-end project to tie it all together.

All learning is based on free, official resources and hands-on coding.

No shortcuts. No certificates. Just understanding how the systems actually work.

The goal isn’t to become a research scientist in a month, but to stop treating models like black boxes.

Time to get uncomfortable again.