PyTorch Deep Learning Journey
Building production-ready deep learning systems with PyTorch. From neural network fundamentals to advanced architectures, model optimization, and real-world deployment strategies.
3-Course Certificate
From PyTorch fundamentals to advanced architectures and production deployment
Hands-on Projects
Real-world applications in computer vision, NLP, and model optimization
Production MLOps
Model deployment with ONNX, MLflow, pruning, and quantization techniques
Modern Architectures
Transformers, attention mechanisms, and generative models with PyTorch
Professional Certificate Structure
A 3-course professional certificate focused on building production-ready deep learning systems with PyTorch.
Course 1: PyTorch Exploration
Building foundational neural networks and image classification models with PyTorch
- •Introduction to PyTorch tensors and autograd
- •Building neural networks with nn.Module
- •Training loops and optimization
- •Image classification projects (MNIST, CIFAR, Nature)
Course 2: Techniques and Ecosystem Tools
Advanced model optimization and MLOps ecosystem tools
- •Optimizers and learning rate scheduling
- •Hyperparameter tuning with Optuna
- •Model profiling and performance optimization
- •Transfer learning and fine-tuning pretrained models
Course 3: Advanced Architectures and Deployment
Modern architectures and production deployment strategies
- •Transformer architectures and attention mechanisms
- •Generative models and diffusion techniques
- •Model export with ONNX
- •MLflow experiment tracking and deployment
- •Pruning and quantization for edge devices
Key Learning Outcomes
- Building and training deep neural networks with PyTorch
- Implementing computer vision models with TorchVision
- Natural language processing with Hugging Face and PyTorch
- Model optimization through pruning, quantization, and profiling
- Production deployment with ONNX and MLflow