Neural Networks and Deep Learning
40 hoursIntermediate7 modules
4 of 7 modules with a published syllabus
0 of 7 modules recorded
Syllabus
Goes from visual intuition to implementation: gradients and applied linear algebra, a neural network written from scratch with no framework, the same network rewritten in PyTorch with autograd and GPU, and the discipline of evaluation and experimentation. The two area modules add CNNs — shared with Computer Vision — and regularisation/normalisation.
Modules of this programme
The same objects as the full curriculum. A module opened from any programme that contains it is the same module, with the same syllabus and the same status.
| Code | Module | Content | Hands-on | Status | Price |
|---|---|---|---|---|---|
| DL-101 | Visual intuition for neural networks | Detailed syllabus not written yet | — | Syllabus published | |
| CORE-201 | Essential mathematics II | Gradients, optimisation, applied linear algebra | Gradient descent from scratch in NumPy | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| CORE-202 | Neural networks from scratch | Perceptron, backpropagation, activation functions | Implement a neural network with no framework | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| CORE-203 | PyTorch in practice | Tensors, autograd, training loops, GPU | Rewrite the CORE-202 network in PyTorch | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| CORE-204 | Evaluation and experimentation | Overfitting, cross-validation, experiment tracking (W&B/MLflow) | Run and compare 10 experiments | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| DL-201 | CNNs (shared with Computer Vision) | Detailed syllabus not written yet | — | Syllabus published | |
| DL-203 | Regularisation and normalisation | Detailed syllabus not written yet | — | Syllabus published |
- DL-101Syllabus published
Visual intuition for neural networks
Detailed syllabus not written yet
- CORE-201Syllabus published
Essential mathematics II
- Content
- Gradients, optimisation, applied linear algebra
- Hands-on
- Gradient descent from scratch in NumPy
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- CORE-202Syllabus published
Neural networks from scratch
- Content
- Perceptron, backpropagation, activation functions
- Hands-on
- Implement a neural network with no framework
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- CORE-203Syllabus published
PyTorch in practice
- Content
- Tensors, autograd, training loops, GPU
- Hands-on
- Rewrite the CORE-202 network in PyTorch
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- CORE-204Syllabus published
Evaluation and experimentation
- Content
- Overfitting, cross-validation, experiment tracking (W&B/MLflow)
- Hands-on
- Run and compare 10 experiments
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- DL-201Syllabus published
CNNs (shared with Computer Vision)
Detailed syllabus not written yet
- DL-203Syllabus published
Regularisation and normalisation
Detailed syllabus not written yet
Not everything fits into 40 hours. State of the art, distributed training, interpretability, edge deployment and the five integrative projects exist only inside the complete tracks: Tracks