PyTorch for Deep Learning Professional Certificate
Learn to use PyTorch to build, optimize, and deploy deep learning models. Start with PyTorch fundamentals to build and train neural networks, progress through optimization and data-handling techniques, explore advanced architectures for computer vision and natural language processing, apply transfer learning and fine-tuning, and then finish by preparing efficient, portable models ready for deployment.
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Build deep learning systems step-by-step: Code neural networks from the ground up in PyTorch. Work directly with tensors, neural networks, and training loops to create and evaluate your first image classifier.
Apply your skills to vision and natural language tasks: Use TorchVision and Hugging Face models, fine-tune pretrained models, compare architectures, and boost performance through hyperparameter tuning.
Advance to modern architectures and deployment: Explore structures such as Siamese networks, ResNets, and Transformers. Interpret model behavior, and prepare your models for deployment using tools and techniques such as ONNX, MLflow, pruning, and quantization.
Why Learn PyTorch
PyTorch is one of the most widely used frameworks in AI research and production. Its Python-based design makes it easy to experiment, debug, and scale models, from simple prototypes to deployed systems. Through this Professional Certificate, you’ll learn how PyTorch powers the full deep learning workflow. Build and train neural networks from scratch, develop deep learning models for computer vision and natural language processing, apply transfer learning and fine-tuning, plus design advanced architectures used in modern AI applications. You’ll also learn to prepare and deploy models for real-world use.
About this Professional Certificate
Building practical deep learning systems means going beyond theory. The PyTorch for Deep Learning Professional Certificate teaches you to build and train the deep learning models that power real AI applications, using PyTorch — one of the most widely adopted frameworks in research and industry — to design efficient, reliable systems.
In this 3-course professional certificate, you’ll learn through hands-on projects that mirror the challenges faced by deep learning engineers: designing efficient architectures, applying transfer learning and fine-tuning to pretrained models, using interpretability techniques to understand model behavior, and preparing optimized, portable models with ONNX and experiment tracking tools like MLflow. Along the way, you’ll gain experience with techniques used across modern AI applications, including pruning and quantization.
Whether you’re strengthening your career in machine learning, expanding into applied AI, or building your own projects, this certificate gives you the skills and the confidence to turn ideas into working PyTorch models.
Start building deep learning systems that make an impact!
Upon completing the PyTorch for Deep Learning Professional Certificate, you’ll earn credentials demonstrating your ability to build and deploy deep learning systems using PyTorch, the most widely adopted DL framework of today
Instructor
Laurence Moroney
Instructor Laurence Moroney has extensive experience working as an AI developer and evangelist at the world’s biggest software companies, including Google and Microsoft, in addition to teaching several of the highest-rated courses with DeepLearning.AI.
- 3 courses
- Self-paced
- Intermediate
Hands-on projects for a better learning experience
Across three courses, you’ll complete hands-on programming labs that take you from coding your first neural network to preparing efficient models ready for real-world deployment with PyTorch. You’ll learn how to diagnose pneumonia from chest X-rays, classify text into categories, build a translation system, and much more. Through guided projects, you’ll:
Begin by building your first neural networks in PyTorch, then take on image classification as you teach a model to tell apart categories like flowers, insects, and animals while learning how neural networks learn from data.
Improve model performance and efficiency on real datasets. Experiment with optimizers and learning-rate schedulers, automate hyperparameter tuning with Optuna, and use profiling tools to identify bottlenecks in your training pipeline.
Apply PyTorch to computer vision projects using TorchVision to load, transform, and augment image data; fine-tune pretrained networks like ResNet and MobileNet; and visualize predictions with saliency maps and class activation maps.
Build and fine-tune language models using Hugging Face and PyTorch. Implement text preprocessing, compare embeddings (GloVe, FastText, DistilBERT), and train classifiers that can analyze real text datasets.
Explore advanced architectures in PyTorch by building transformer models from core components like multi-head attention, and get an introduction to modern generative approaches such as diffusion models.
Prepare models for real-world deployment by exporting them with ONNX, tracking experiments in MLflow, and applying pruning, quantization, and quantization-aware training to reduce model size and latency.
Course Syllabus
Recommended Background
Familiarity with Python and foundational deep learning concepts, such as those covered in the Deep Learning Specialization by DeepLearning.AI, will help you get the most from this professional certificate.
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