The PyTorch for Deep Learning Professional Certificate teaches you how to build, train, optimize, and deploy deep learning models using the PyTorch framework. Through three progressively structured courses, you’ll move from the fundamentals of PyTorch and neural networks to advanced architectures and model deployment techniques used in real-world AI systems.
You’ll start by learning about tensors, neural networks, and machine learning pipelines, which power deep learning models. Then, you’ll apply these concepts to computer vision and natural language processing by using and improving models found in TorchVision and Hugging Face. In the final course, you’ll explore architectures like Siamese networks, ResNet, DenseNet, and Transformers, and learn how to prepare, export, and optimize models for deployment using ONNX, MLflow, pruning, and quantization.
By the end, you’ll have the practical skills to develop, evaluate, and deploy PyTorch models for a wide range of AI applications.
Applied Learning Project
Throughout this Professional Certificate, you’ll apply your PyTorch skills to build and refine deep learning models. You’ll be working with computer vision and natural language processing models, which you will build from scratch, and optimize using hyperparameter tuning. You will also use pre-trained models and learn how to improve and adapt them via transfer learning and fine tuning. Later, you’ll work on advanced projects such as text classification and image segmentation, and finish by preparing your trained models for deployment using tools like MLflow and ONNX.
