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
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.