Just sharing my sources of Deep learning, if anyone finds this post helpful, please share it. The content will be updated with the new techniques and information.
AI, Machine Learning and Deep Learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:
- Should you collect more training data?
Should you use end-to-end deep learning?
How do you deal with your training set not matching your test set?
and many more.
Historically, the only way to learn how to make these “strategy” decisions has been a multi-year apprenticeship in a graduate program or company. I am writing a book to help you quickly gain this skill so that you can become better at building AI systems.
The book will be around 100 pages and contain many easy-to-read 1-2 page chapters. If you would like to receive a draft of each chapter as it is finished, please sign up for the mailing list.
An article begins with the Title, Abstract, and Keywords.
The article text follows the IMRAD format, which responses to the question below:
- Introduction: What did you/other do? Why did you do it?
- Methods: How did you do it?
- Results: What did you find?
- Discussion: What does it all mean?
The biggest mistake that you can make is to believe that you are working for somebody else. Job security is gone. The driving force of a career must come from the individual. Remember: Jobs are owned by the company, you own your career!
— Earl Nightingale
Asking a question in a seminar is like a box of chocolates, you never know what you gonna get.
“All of the humanity’s problems stem from man’s inability to sit quietly in a room alone.”
— Blaise Pascal, Pensées
The best thing to being Grad student is … “the free food” on a seminar.
The learning problem and the principles before building a model.
This blog is mainly based on the book and lecture notes by Professor Yaser S. Abu-Mostafa from Caltech on Learning from data , you could benefit a lot from the lecture and videos.
“In God we trust, and others bring data”.
If you show a picture to a three-year-old and ask if there is a tree in it, you will likely get the correct answer. But if you ask a thirty-year-old what the definition of a tree is, you will likely get an inconclusive answer.
We didn’t learn what a tree is by studying the mathematical definition of a tree. We learned it by looking at trees. In other words, we learn from ‘Data’.