Resources

DeepLearning.AI's resource center to help you get started and level up your skills as an AI practitioner or Machine Learning Engineer | eBooks, Guides, Course Slides, AI Notes, and more.

Guides

A Complete Guide to Natural Language Processing

This comprehensive guide covers multiple questions including; What is Natural Language Processing? Why does NLP matter? What is NLP used for? Top NLP techniques, six important NLP Models and more

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eBooks

Machine Learning Yearning

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.

Machine Learning Yearning

Course Slides

Machine Learning Specialization — Course Slides

[Download] the annotated version of the course slides for the popular Machine Learning Specialization by Andrew Ng & Stanford Online

Deep Learning Specialization — Course Slides

Download the annotated version of the course slides for Deep Learning Specialization — Our foundational online course by machine learning pioneer Andrew Ng.

Natural Language Processing (NLP) Specialization — Course Slides

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.

AI for Everyone — Course Slides

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.

AI Notes

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.

Initializing neural networks

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.

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Parameter optimization in neural networks

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.

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Other Resources

MLOps: From Model-centric to Data-centric AI by Andrew Ng

In these slides, Andrew Ng shares the skills he sees as fundamental to the next generation of machine learning practitioners.