PyTorch for Deep Learning

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

• Build and train deep learning models with PyTorch
• Apply transfer learning in computer vision and NLP tasks
• Optimize model accuracy and performance for deployment

• Basic familiarity with the topic and terminology
• Willingness to practice with applied exercises or case studies