Introduction to Deep Learning
Explore artificial neural networks, CNNs, RNNs, and the techniques behind modern AI breakthroughs.
Course overview
Students dive deep into artificial neural networks — learning how structures like CNNs and RNNs power modern breakthroughs in computer vision and natural language processing.
Core curriculum
Four themed modules. Each module is a working block of lessons and labs.
Neural Networks
Deep learning concepts, architecture, forward propagation, and training mechanics.
Computer Vision
Master Convolutional Neural Networks (CNNs) for image-based detection and classification projects.
Sequence & Text
Apply Recurrent Neural Networks (RNNs) and LSTMs to time-series forecasting and NLP analysis.
Generative AI
Introduction to GANs for realistic data creation and Transfer Learning using pre-trained models.
What you'll gain
- Build, train, and evaluate neural networks from scratch
- Image classification with CNNs on real datasets
- Sequence modeling with RNNs / LSTMs for text and time series
- Hands-on intro to transfer learning and generative models
Available classes
Open classes you can enroll in directly. Each class shows its instructor and weekly schedule.
Not sure which class fits? Reach us from the Contact page.
Ready to build with AI?
Schedule a free consultation. We'll help you choose the right track — Builder, Portfolio, Scholar, or Innovation — based on your goals and experience.