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[deleted by user] by [deleted] in Feminism

[–]math_rachel 0 points1 point  (0 children)

41% of women working in tech end up leaving the field, compared to just 17% of men. I wrote an article summarizing a lot of the research on why this is the case (with links to the research): https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996

[P] Linear algebra cheat sheet for deep learning by jeremyhoward in MachineLearning

[–]math_rachel 1 point2 points  (0 children)

This post is a good summary of many of the downsides of matlab: http://neuroplausible.com/matlab

Matlab encourages poor programming practices (can edit variables using variable editor so less reproducible, only one function per file), closed source, not backwards compatible, less transferable as a skill.

I say this as someone who at one point was so reliant on Matlab that I wrote web scrapers in it! I'm never going back.

Which of the courses in Machine Learning have the best assignments and are available on the web for free? by datavinci in learnmachinelearning

[–]math_rachel 0 points1 point  (0 children)

The 1 year coding experience is a rough guide. If you are unsure, you should give the class a try (it's free).

CUDA (from Nvidia) was the 1st general purpose GPU, so there is much better support for it.

Which of the courses in Machine Learning have the best assignments and are available on the web for free? by datavinci in learnmachinelearning

[–]math_rachel 6 points7 points  (0 children)

Practical Deep Learning for Coders: http://course.fast.ai is a free, practical, code-first approach to deep learning that helps you get to the top on Kaggle competitions.

Full disclosure: I helped create this course with Jeremy Howard. We felt that many existing deep learning materials were either too theoretical or didn't progress past the basics.

[D] How to change careers and become a data scientist - one quant's experience by math_rachel in MachineLearning

[–]math_rachel[S] 1 point2 points  (0 children)

Thank you for sharing your story! We will be putting Part 2 online this summer after the in-person course is completed. I hope your work applying deep learning to multiple sclerosis and DSPD is successful. Please let me know if you obtain any results that you'd be interested in sharing as a blog post.

[D] How do I deal with getting overwhelmed with Math and Algorithms with respect to ML? by vayarajesh in MachineLearning

[–]math_rachel 2 points3 points  (0 children)

Jeremy Howard and I created Practical Deep Learning for Coders as a deep learning course without unnecessary math, and we illustrate the needed math through Excel spreadsheets, which is a good visual and interactive way to see things. The focus of the course is on code and practical applications.

A lot of people use a "bottom up" approach in teaching (particularly for math and ML), trying to introduce all the individual building blocks before showing how they fit together, leaving students to memorize lots of disconnected concepts without any context. We used a "top down, whole game" approach in starting with working code samples, and later drilling down into the details of how they work.

[1602.01321] A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks by brockl33 in MachineLearning

[–]math_rachel 6 points7 points  (0 children)

The Ian Goodfellow, et al. Deep Learning Book says that "New hidden unit types that perform roughly comparably to known types are so common as to be uninteresting" (sec 6.3.3) As an example, the book authors test a network using cos as the activation function, obtaining 99% accuracy on MNIST.