Design and Train Neural Networks
This course enters the 'engine room' of deep learning. Move from abstract concepts to the concrete mechanics of training neural networks, including supervised learning, classification, sentiment analysis, and Named Entity Recognition.
Key Topics
- Multilayer Perceptrons (MLPs): The building blocks of deep learning
- The Training Loop: Forward pass, loss calculation, backpropagation
- Optimization: Gradient descent algorithms
- Diagnostics: Identifying overfitting and underfitting
Learning Outcomes
- Understand the mechanics of training a neural network
- Learn to spot and mitigate common training issues
- Evaluate model performance on classification tasks
- Implement MLPs from scratch or using frameworks
- Apply backpropagation theory to code
Practical Projects
- Implement and evaluate an MLP for classification tasks
- Social Impact Analysis: Anticipate potential risks of trained models
- Train a Bi-LSTM-CRF model for NER in African languages
Recommended Datasets
12 datasets are aligned with this course for hands-on exercises and projects.
Malaria Cell Images
Parasitized vs Uninfected Blood Cell Images (NIH)
MasakhaNEWS
News Topic Classification for 16 African Languages
KaraAgro AI Maize
Annotated Maize Disease Images from Ghana
MasakhaNER 2.0
Named Entity Recognition for 20 African Languages
AfriSenti
Sentiment Analysis for 14 African Languages
AfriHate
Hate Speech Detection for 15 African Languages
Tunizi
Tunisian Arabizi Sentiment Analysis
South Africa Crop Type
Fused Sentinel-1/2 Imagery for Crop Mapping
Ghana Croplands Agronomy
Biophysical & Yield Measurements for Northern Ghana Maize
African Medical Records (OCR)
Nigerian Handwritten Clinical Records for OCR
Nigerian Colposcopy Images
Cervical Cancer Screening Images from Nigeria
Snapshot Serengeti
Camera-Trap Wildlife Images from Tanzania
About This Course
This course covers the fundamentals of neural networks for NLP. You’ll learn to design, train, and optimize neural networks for text classification, sequence labeling, and other language tasks.
Key Topics
- Network Architecture: Feedforward, recurrent, and convolutional networks
- Backpropagation: Understanding gradient flow
- Optimization: SGD, Adam, and learning rate scheduling
- Regularization: Dropout, weight decay, and early stopping
- Practical Training: Debugging and improving model performance
Prerequisites
- Completion of Courses 01-02 or equivalent knowledge
- Calculus basics (derivatives, chain rule)
- Python with PyTorch or TensorFlow