D24
Data & AIAdvanced10 weeks
Deep Learning
Build a solid foundation in neural networks, then design, train, and deploy CNNs, sequence models, transformers, and generative architectures using PyTorch.
What you'll learn
Neural network fundamentals: backpropagation, optimizers, and regularization in PyTorch
Convolutional networks for image classification and transfer learning
Sequence models and transformer architectures for text and vision tasks
Training generative models (GANs, diffusion) and deploying models to production
Course details
Duration
10 weeks
Best for
Learners with Python and basic machine learning experience who want to build and deploy neural networks in PyTorch
On completion
Verified certificate & placement assistance
Course Curriculum
A module-by-module breakdown of what you'll cover in Deep Learning.
01
Math & PyTorch Foundations
Weeks 1–2- Linear algebra and calculus for deep learning
- Tensors, autograd, and manual backpropagation
- Building models with PyTorch's nn.Module
- GPU/CPU device management
02
Neural Network Fundamentals & Regularization
Week 3- Perceptrons, MLPs, and activation functions
- Loss functions and optimizers (SGD, Adam, RMSProp)
- Dropout, normalization, and early stopping
- Hyperparameter search and experiment tracking
03
Convolutional Networks & Computer Vision
Weeks 4–5- Convolution and pooling operations
- Transfer learning with ResNet/EfficientNet
- Data augmentation for vision tasks
- Visualizing model predictions with Grad-CAM
04
Sequence Models
Weeks 6–7- Vanilla RNNs and the vanishing-gradient problem
- LSTM and GRU gating mechanisms
- Sequence-to-sequence framing
- Padding, masking, and batching sequences
05
Transformers & Generative Models
Week 8- Self- and multi-head attention
- Transformer and vision-transformer architectures
- Fine-tuning pretrained transformers
- GAN and diffusion model fundamentals
06
Deploying & Scaling Deep Learning Models
Weeks 9–10- Model export to ONNX and quantization
- Serving models with TorchServe/TF Serving
- Distributed and multi-GPU training basics
- Latency, throughput, and model versioning
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
PyTPyTorch
HugHugging Face Transformers
DifDiffusers
WeiWeights & Biases
ONNONNX Runtime
TenTensorRT
RayRay Tune / Optuna
GitGitHub Copilot
Hands-on Projects
This is project-based learning — you'll build real, portfolio-ready work as you go, not just watch lectures.
Image classifier built with transfer learning and wrapped in a web demo
LSTM-based sequence forecaster benchmarked against a classical baseline
Fine-tuned transformer classifier with attention-map visualizations
Small diffusion or GAN image generator evaluated with an FID score
Real-time object detection app using a quantized, ONNX-exported model
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Student Reviews
“The PyTorch-first approach made backpropagation finally click, and the transformer module set me up to fine-tune models confidently at work.”
Arjun Mehta
Machine Learning Engineer
“Going from CNNs to diffusion models in one course felt ambitious, but the project-based structure kept everything grounded and practical.”
Sneha Reddy
Data Scientist
