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This book delivers insights from AI pioneer Andrew Ng about learning foundational skills, working on projects, finding jobs, and joining the machine learning community. A practical roadmap to building your career in AI.
This is an introductory book about developing ML algorithms. You will learn to diagnose errors in an ML project, prioritize the most promising directions, work within complex settings like mismatched training/test sets, and know when and how to apply various techniques.
[Download] the annotated version of the course slides for the popular Machine Learning Specialization by Andrew Ng & Stanford Online
Download the annotated version of the course slides for Deep Learning Specialization — Our foundational online course by machine learning pioneer Andrew Ng.
Download the course slides for the Mathematics For Machine Learning & Data Science Specialization. A specialization that teaches you the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
Download the course slides for the Generative Adversarial Network (GANs) specialization. A specialization that teaches you image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach.
Download the course slides for the Natural Language Processing (NLP) specialization. A specialization that teaches you how to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarize text, and even build chatbots.
Download the course slides for AI for Everyone — a non-technical course that helps you understand AI technologies and spot opportunities to apply AI to problems in your own organization.
This is a series of long-form tutorials that supplement what you learned in the Deep Learning Specialization. With interactive visualizations, these tutorials will help you build intuition about foundational deep learning concepts like initializing neural networks and parameter optimization.
Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes have been found to accelerate training, but they require some care to avoid common pitfalls. In this post, we'll explain how to initialize neural network parameters effectively.
Training a machine learning model is a matter of closing the gap between the model's predictions and the observed training data labels. But optimizing the model parameters isn't so straightforward. Through interactive visualizations, we'll help you develop your intuition for setting up and solving this optimization problem.
In these slides, Andrew Ng shares the skills he sees as fundamental to the next generation of machine learning practitioners.
