Machine Learning Yearning by Andrew Ng: Full Book PDF [Free]

Machine Learning Yearning by Andrew NG - Free Full Book PDF | Cognitive Quest

Machine Learning Yearning by Andrew Ng – Book Summary

Machine Learning Yearning is a deep exploration of the fundamental concepts and techniques in machine learning. 

Machine Learning Yearning by Andrew NG - Free Full Book PDF | Cognitive Quest

The book begins with an in-depth discussion on Basic Error Analysis, emphasizing the importance of understanding the nature of errors made by a model. It provides insights into how to examine these errors and create strategies to enhance the model’s performance.

The book then transitions into the critical concepts of Bias and Variance. It clarifies the balance between these two elements, essential for optimizing machine learning algorithms. 

Following this, Ng introduces the concept of Learning Curves. These curves are instrumental in visualizing the performance of a machine-learning model as it learns from an increasing amount of data over time. The book provides a detailed guide on how to interpret these curves and utilize them to improve the model’s performance.

Ng further delves into a more detailed Error Analysis, this time dissecting it into parts. This approach provides a better understanding of the model’s performance, pinpointing specific areas where the model might be underperforming and providing strategies to address these issues.

Ng concludes by reinforcing the importance of these concepts in machine learning. It encourages the reader to apply these principles in their machine-learning projects, emphasizing that a profound understanding of these concepts is critical to successfully implementing machine-learning algorithms and improving their performance. 

Who is Andrew Ng?

Andrew Ng
speaks onstage during TechCrunch Disrupt SF 2017 at Pier 48 on September 20, 2017 in San Francisco, California. Photo by Steve Jennings

Andrew Ng is a well-known computer scientist and entrepreneur recognized for his work in machine learning and artificial intelligence (AI). He co-founded Google Brain and was the Chief Scientist at Baidu, where he established the AI Group. He’s also an adjunct professor at Stanford University and co-founder of Coursera and DeepLearning.AI, contributing significantly to online education.

Born in the UK in 1976 to Hong Kong immigrants, Ng earned his undergraduate degree from Carnegie Mellon University, his master’s from MIT, and his Ph.D. from UC Berkeley.

Ng’s research focuses on machine learning, deep learning, computer vision, and natural language processing. He’s co-authored influential papers and significantly impacted AI, computer vision, and robotics. He also co-founded Coursera, offering free online courses, and launched the AI Fund in 2018, a $175-million fund for AI startups. He founded Landing AI, which offers AI-powered SaaS products.

What exactly is Machine Learning?

AIs are used in many areas of our lives, from recommending what movie to watch next on Netflix to powering the voice assistant. 

While intelligent machines have already been integrated into various aspects of our lives, their prominence has recently surged with the introduction of groundbreaking AI technologies like ChatGPT, Midjourney, and Stable Diffusion

These advancements have propelled artificial intelligence into the forefront, revolutionizing how we interact with technology. 

Many people use the terms “Machine Learning” and “Artificial Intelligence” (AI) interchangeably, but they actually refer to distinct concepts.

Machine learning is a specific technique that involves training computers to learn from data, similar to how humans learn from experiences. 

For example, feeding the computer a lot of data, such as pictures of cats, and telling it, “This is a cat.” Over time, the computer ‘learns’ to identify cats by finding patterns and features in the given data. The more data it learns, the better it gets at recognizing cats.

AI, on the other hand, is a broader concept. It’s about creating machines that can perform tasks that usually require human intelligence. This includes things like understanding language (the way we talk and write), recognizing patterns (identifying cats), making decisions (like in a game of chess), and more.

In general, machine learning and AI involve creating intelligent machines that can learn and make choices similar to humans. And as technology advances, we can expect a significant increase in these machines in our everyday lives.

About The Author

Leave a Reply