DEEP LEARNING For Beginners

Watched a dozen deep learning tutorials, can define “neural network,” and still couldn’t build one without copy-pasting code you don’t understand?

That’s what happens when courses skip straight to Keras one-liners or bury you in math with no intuition behind it. You end up able to run someone else’s model but unable to debug your own, explain backpropagation, or fix a network that simply won’t learn. This book takes the opposite path: building a neural network from a single neuron up, by hand, before ever touching a framework — so every PyTorch line you write later actually means something.

Inside, you’ll learn:

• Just enough math to matter: vectors, matrices, derivatives, and gradients, explained as tools rather than abstract theory
• How a network actually learns — loss functions, backpropagation, and gradient descent — traced by hand before you see them in code
• How to build and train real networks in PyTorch, from your first tensors to a complete, working training loop
• Why training goes wrong (overfitting, underfitting) and how to fix it with regularization and by actually reading PyTorch error messages
• Why architectures like CNNs, recurrent networks, and Transformers exist, and how to apply transfer learning instead of starting from zero

By the end, you’ll know how to prepare real datasets, use data augmentation, and design a project properly before building it — then apply all of it in your capstone: a complete, working image classifier.

Stop running code you can’t explain. Get your copy today and start building deep learning models you actually understand.