Deep Learning Foundations for AI Engineers
Learn how neural networks learn, why you fine-tune a pre-trained model instead of training from scratch, and how CNNs and Transformers fit in.
What You'll Learn
Understanding Why Deep Learning Exists
Every week, acme-shop's grocery team receives about four thousand photos from sellers: crates of beans, tomatoes, chillies.
Understanding How a Neural Network Is Built
A neural network is a stack of layers, and each layer multiplies its inputs by learned numbers, adds another learned number, and bends the result...
Understanding Forward and Backward Propagation
A network learns by guessing, measuring how wrong the guess was, and nudging every weight in the direction that makes the guess less wrong.
Understanding Transfer Learning
Transfer learning means starting from a model already trained on a large general dataset and adapting it to your task, instead of training from...
Understanding Convolutional Neural Networks
A Convolutional Neural Network (CNN) slides small learnable filters across an image so that it detects patterns by looking at local patches, not at...
Understanding Sequence Models: RNNs, LSTMs, and GRUs
Text, audio, and time series are sequences where order matters, so they need models that carry context forward from earlier items.
Skills You'll Master
Curriculum Index9 topics
Understanding Why Deep Learning Exists
Every week, acme-shop's grocery team receives about four thousand photos from sellers: crates of beans, tomatoes...
Understanding How a Neural Network Is Built
A neural network is a stack of layers, and each layer multiplies its inputs by learned numbers, adds another learned...
Understanding Forward and Backward Propagation
A network learns by guessing, measuring how wrong the guess was, and nudging every weight in the direction that makes...
Understanding Transfer Learning
Transfer learning means starting from a model already trained on a large general dataset and adapting it to your task...
Understanding Convolutional Neural Networks
A Convolutional Neural Network (CNN) slides small learnable filters across an image so that it detects patterns by...
Understanding Sequence Models: RNNs, LSTMs, and GRUs
Text, audio, and time series are sequences where order matters, so they need models that carry context forward from...
Understanding the Transformer and Attention
The Transformer replaced recurrence with attention, so every token in a sequence can look directly at every other token...
Building and Fine-Tuning a Model Hands-On
You will train a small network from scratch, fine-tune a pre-trained ResNet18 on real bean leaf photos, and then...
Quick Reference and Common Mistakes
Quick reference Common mistakes Reaching for deep learning on every problem happens because it gets the headlines.
Career Impact
Roles that use the skills in this module.
AI Engineer
MLOps Engineer
Platform Engineer
Next Modules
Related Guides
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
No. The lab in this module runs on a laptop CPU, just slowly for the image model. Free Google Colab gives you a GPU if you want the fine-tuning step to finish faster.
Fine-tune a pre-trained model unless no suitable one exists. A pre-trained model has already learned general features from millions of examples, so you need far less data and compute to adapt it.
You need the idea, not the derivation. PyTorch computes gradients for you, and the idea is enough to reason about why training is stuck, diverging, or too slow.
Learn what they do and why they struggled, because it explains why Transformers won. You will rarely build one today, since Transformers replaced them for most text and many sequence tasks.