A comprehensive deep learning journey from neural network fundamentals to cutting-edge architectures. Build, train, and deploy CNNs, RNNs, Transformers, GANs, and diffusion models with PyTorch.
Deep dive into neural network design. Activation functions, loss functions, optimisers (SGD, Adam, AdamW), batch normalisation, dropout, and architectural design principles.
Convolutional neural networks from scratch. Feature maps, pooling, modern architectures (ResNet, EfficientNet), image classification, and transfer learning with pretrained models.
Recurrent neural networks for sequential data. Vanishing gradient problem, LSTM and GRU cells, bidirectional networks, and sequence-to-sequence models.
Build the Transformer architecture from the ground up. Self-attention, multi-head attention, positional encoding, encoder-decoder design, and why Transformers revolutionised AI.
Leverage pretrained models to solve your specific problems with minimal data. Fine-tuning strategies, feature extraction, domain adaptation, and efficient transfer techniques.
Master PyTorch for deep learning research and production. Custom datasets, data loaders, training loops, mixed precision, distributed training, and TorchScript for deployment.
Generative models: GANs (DCGAN, StyleGAN), Variational Autoencoders, and Diffusion Models (DDPM). Understand how Stable Diffusion and DALL-E work under the hood.
Quantisation, pruning, knowledge distillation, ONNX export, TorchServe, and deploying models efficiently in production with proper latency and throughput requirements.
Python developers with basic ML knowledge who want to master deep learning and neural networks
Data scientists looking to add deep learning to their toolkit for computer vision and NLP tasks
Engineers preparing for roles in AI research, applied ML, or deep learning engineering
Python and basic ML knowledge required. You should be comfortable with Python, understand basic ML concepts (training, testing, evaluation), and have some familiarity with NumPy. Our ML Engineering Bootcamp or Python for AI course provides ideal preparation.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
We provide cloud GPU access for all lab sessions through Google Colab Pro or similar platforms. You do not need to own a GPU, but having one will let you experiment more freely outside class.
We use PyTorch exclusively. It is the dominant framework in research and increasingly in industry. The concepts transfer easily to other frameworks if needed.
You should be comfortable with basic linear algebra (matrices, vectors) and calculus (derivatives). We review the essential math as we go, but some foundation helps. We provide a math refresher module.
This course gives you a strong deep learning foundation. Combined with portfolio projects and our advanced courses (Computer Vision, NLP, LLM Fine-Tuning), you will be well-prepared for DL engineering roles.