Course · Interview foundations
Deep Learning for Data Engineers
Free deep learning course: how neural networks work and learn, backpropagation, learning rates, CNNs, RNNs, transformers and PyTorch basics, explained simply.
- Lessons
- 4
- Interview questions
- 0
- Projects & case studies
- 0
- Reading time
- ~1 h
Your progress
Saved in this browser onlyCourse structure
- 1 lessonStart hereThe complete overview of the course in one read.
- 1 lessonBeginnerCore concepts you will use every day.
- 2 lessonsIntermediatePatterns used in production pipelines.
Practise
Lessons
Work through the lessons in order. Completed lessons show a tick; lessons you have opened are outlined.
Start here
The complete overview of the course in one read.
Beginner
Core concepts you will use every day.
Intermediate
Patterns used in production pipelines.
- How Neural Networks Learn: Loss, Gradient Descent and BackpropagationHow neural networks learn, in plain words: loss, gradient descent, backpropagation and the learning rate, with a NumPy experiment comparing learning rates.
- CNNs, RNNs and Transformers: The Main Neural Network DesignsThe main neural network designs in plain words: CNNs for images, RNNs for sequences and transformers, with a convolution you can run in NumPy.
Resources
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