Hi, all

I have recently written a blog post on medium describing why I feel that there has to be room for science in data science and how one approach that helps solve this problem is proper experiment management.

I would love to hear your thoughts on the subject.

What are the best practices in your organizations/teams and how do they differ from classical software development approaches?

So I had chosen a masters of applied statistics instead of data science or computer science.

After reading many books I’ve gained a deep respect and admiration for developers of ML and artificial intelligence programs. I would like to start self-learning this.

Does having a math/stats background help with self-learning ML/AI ?

Thanks!

What is it?

Lightning defers training and validation loop logic to you. It guarantees correct, modern best practices for the core training logic.

Why do I want to use lightning?

When starting a new project the last thing you want to do is recode a training loop, model loading/saving, distributed training, when to validate, etc… You’re likely to spend a long time ironing out all the bugs without even getting to the core of your research.

With lightning, you guarantee those parts of your code work so you can focus on what the meat of the research: Data and training, validation loop logic. Don’t worry about multiple gpus or speeding up your code, lightning will do that for you!

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https://github.com/williamFalcon/pytorch-lightning

If you want to learn Machine Learning but you’re worried you don’t have the math or the software background to master it, or you don’t know where to begin, this blog could be “one-stop shopping” for you: (it’s written in Google Colaboratory):

Why did I write this humorous, comprehensive blog? Because I have been where you are now. As a Humanities major (who once worked for “Saturday Night Live”), I suffered through two years of hell as I taught myself ML with online courses and blogs, and it was like drinking from a fire hose–too much information from too many experts with too many conflicting approaches, and my head was filled with confusion and self-doubt. Could I really learn this stuff?

IMO, today’s AI books and online courses suffer from “Expert Blindness.” Most of the experts have been experts for so long, and so deeply, that they forgot how a beginner sees the material. My blog skips no steps as I use analogies, pictures, examples and humor to break the concepts down into bite-size, user-friendly pieces, with minimal expert blindness. And every phrase has been double-checked by my mentor, who is a Stanford PhD in aerospace engineering.

It would make me happy to know I helped other folks to avoid the hell I went through. Please pass this blog on to any ML rookies, and I welcome all constructive comments to improve this as a resource for all future ML engineers!

Warmly,

David Code (yes, that really is my last name–what are the odds, right? 🙂

I understand that the multiple workers do gradient update to the global network is done asynchronously in A3C ( https://arxiv.org/abs/1602.01783 ).

But how do the workers ensure that they won’t retrieve the same parameters from the global network they just updated?

Thank you.

Hey, I am currently working on the sequential feature prediction using LSTM (the sequential feature can be extracted by a pretrained VGG or the latent space from an autoencoder), so basically the extracted feature is considered as “ground truth”, and I am using LSTM to predict the future features.

The feature space is always not constrained to a specific interval, sometimes the values vary between [-10,50], but the tanh activation layer in LSTM will constrain the output between [-1,1] (If I understand correct?). I have also played with adding fc layer with leakyrelu or deconv layers after the LSTM layer, but the output is still in a much smaller interval, so the scale mismatch between my ground truth feature and predicted latent space make it really hard to train this model. I am wondering has anyone faced this issue before? And do you have some ideas about how to solve it?

Thanks in advance!!

I had a really good response to my video over at /r/learnmachinelearning (https://www.reddit.com/r/learnmachinelearning/comments/ckoy0k/want_to_build_your_own_machine_learning_robot/) so I thought I would share it here. Its a video guide that will help you build your first machine learning robot. It starts from basic breadboard prototyping all the way to PCB design, surface mount soldering, and finally programming. It uses an ARM microcontroller and an Arduino bootloader. The finished robot is designed to avoid light and a Neural Network is what controls the motors. It also fits in the palm of your hand (great for classrooms!).

https://youtu.be/wtNaPLmpy1I

I have a few more ML projects coming out over the next couple of months as well. Not as extensive as this, but I think really interesting topics.

edit: Forgot to mention its all open source, and all of the resources can be found here: https://github.com/IdleHandsProject/makennbot

Hi Everyone,

Context: Our previous [N]ews post

Since the response was good, I thought you will have something to say about the first Medium post about TRAINS

Specifically, I am looking for more feedback and feature requests from actual or would-be users.

PS: I am aware we do not have a comparison matrix set up yet, in the meantime you can go over the feature list in the medium post, they are quite comprehensive…
PS/2: And as before, only two lines of integration code 🙂

For me it seems e-greedy with a high alpha, as in reading lots of random abstracts or wikis or blogs, feels the most appropriate since I am getting a very low reward signal for my long term goals.

Has anyone put much thought into their process of acquiring new information and how deeply they need to go on a topic before they can rate its relative importance? And further, the risk of bias causing a dismissal of fruitful lines of inquiry?

The meta aspect of learning is fascinating to me and I am curious which modes of thought work best for people.