Master advanced PyTorch techniques to build high-performing, efficient deep learning models.
In this course, you’ll expand your skills in hyperparameter optimization, model profiling, and workflow efficiency. You’ll experiment with learning rate schedulers, tackle overfitting, and use automated hyperparameter tuning with Optuna to boost model performance. Learn how to design flexible architectures, measure model efficiency with the PyTorch Profiler, and make the most of your compute resources.
You’ll also dive into real-world applications using TorchVision for computer vision tasks like loading, transforming, and augmenting image data, and leveraging Hugging Face for natural language processing. You’ll apply transfer learning and fine-tune pre-trained models to adapt them for new problems.
By the end, you’ll know how to train smarter, optimize deeper, and build PyTorch models ready for production-level deployment.
This module focuses on optimizing machine learning models through systematic evaluation and hyperparameter tuning techniques. Students will learn to assess model performance using key evaluation metrics like accuracy, precision, recall, and F1-score, then apply various optimization strategies to im…
This module provides a comprehensive introduction to TorchVision, PyTorch's computer vision library that offers essential tools for image processing, data handling, and model deployment. Students will explore TorchVision's core components including image transforms, preprocessing pipelines, built-i…
This module introduces Natural Language Processing (NLP) fundamentals using PyTorch, covering the essential pipeline from raw text to trained models. Students will learn how to transform text data into numerical representations through tokenization, tensorization, and embedding techniques, while ex…
This module focuses on optimizing machine learning workflows through efficient data handling and training techniques in PyTorch. Students will learn to identify and eliminate performance bottlenecks that can slow down model training, particularly around data loading and GPU utilization. The course…
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DeepLearning.AI is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (90/100). Andrew Ng's courses.
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
Instructor: Dennis Freeman, Kendra Pugh This course provides an integrated introduction to electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. The lecture videos provide an overview of each topic, whi…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
Stanford CS25: Transformers United Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs)…
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
