Intermediate · Short courseOnline · live classesOn campus
Deep Learning & Neural Networks
From your first neural network to CNNs, RNNs and Transformers — built in PyTorch, trained on real GPUs.
Enroll nowApproved & recognised by the University of Mysore
Level
Intermediate
Duration
12 weeks
Time commitment
8–10 hrs/week
Format
Live online or on campus
About this course
The course that takes you from classical ML into modern AI. You build neural networks from scratch to understand backpropagation, then move to PyTorch and train progressively bigger architectures: convolutional networks for images, recurrent networks for sequences, and the Transformer architecture behind today’s large language models. Training happens on cloud GPUs, the way real teams work.
Who it's for: Learners who know basic machine learning and Python, ready for the deep end of modern AI.
What you'll learn
Explain and implement backpropagation from scratch
Build and train networks in PyTorch
Design CNNs for image tasks and RNNs for sequences
Understand attention and the Transformer architecture
Use transfer learning and fine-tuning on real problems
Train on cloud GPUs with proper experiment tracking
Syllabus
Neural network foundations
Weeks 1–3- Perceptrons to MLPs
- Backpropagation by hand
- PyTorch fundamentals
Computer vision
Weeks 4–6- Convolutions and CNNs
- Data augmentation
- Transfer learning
Sequences & attention
Weeks 7–9- RNNs and LSTMs
- Attention mechanisms
- The Transformer, piece by piece
Capstone
Weeks 10–12- Fine-tune a pretrained model
- Experiment tracking and GPUs
- Demo day: defend your results
Tools you'll use
PyTorchGoogle ColabWeights & BiasesHugging Face
Certificate
Finish the course and its capstone project to earn a FuturAIse Academy Certificate of Completion — verifiable online, with the projects to back it up.