Building AI: 3 theorems you need to know – DXC Blogs

Building #AI: 3 theorems you need to know #MachineLearning

  • The mathematical theorem proving this is the so-called “no-free-lunch theorem” It tells us that if a learning algorithm works well with one kind of data, it will work poorly with other types of data.
  • In a way, a machine learning algorithm projects its own knowledge onto data.
  • In machine learning, overfitting occurs when your model performs well on training data, but the performance becomes horrible when switched to test data.
  • Any learning algorithm must also be a good model of the data; if it learns one type of data effectively, it will necessarily be a poor model — and a poor student – of some other types of data.
  • Good regulator theorem also tells us that determining if inductive bias will be beneficial or detrimental for modeling certain data depends on whether the equations defining the bias constitute a good or poor model of the data.

Editor’s note: This is a series of blog posts on the topic of “Demystifying the creation of intelligent machines: How does one create AI?” You are now reading part 3. For the list of all, see here: 1, 2, 3, 4, 5, 6, 7.
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Practical Deep Learning For Coders—18 hours of lessons for free

Practical #DeepLearning For Coders—18 hours of lessons for free

  • After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
  • I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
  • Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
  • But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

Continue reading “Practical Deep Learning For Coders—18 hours of lessons for free”

Practical Deep Learning For Coders—18 hours of lessons for free

Welcome to a 7 week course, Practical Deep Learning For Coders, Part 1,

  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
  • If you can code, you can do deep learning
  • It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately.
  • If you are looking to venture into the Deep learning field, look no further and take this course.
  • I now have the tools to apply deep learning models to real world problems.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

Continue reading “Practical Deep Learning For Coders—18 hours of lessons for free”

Practical Deep Learning For Coders—18 hours of lessons for free

Practical Deep Learning For Coders

  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
  • If you can code, you can do deep learning
  • It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately.
  • I now have the tools to apply deep learning models to real world problems.
  • If you are looking to venture into the Deep learning field, look no further and take this course.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

Continue reading “Practical Deep Learning For Coders—18 hours of lessons for free”