Interesting and useful resources for data science, AI, neuroscience, statistics, and more

3 minute read

Published: March 18, 2022

A list of useful or interesting articles, courses, and blogs relating to data science, neuroscience, AI, and statistics (mostly).

Courses & Textbooks

Hands on Machine Learning with Scikit-Learn, Keras, and TensorFlow

I think this is the most popular intro to machine learning book for Python. I didn’t do the Keras/TensorFlow portions, but the first half is more than enough to get started in machine learning.

fast.ai

My favorite deep learning course. It covers deep learning across many domains, AI ethics, and why and how deep learning works so well.

Statistical Rethinking

My favorite (bayesian) statistics course. Taught in R and Stan, but it’s been translated to PyMC3, brms, & Julia.

The Missing Semester of Your CS Education

My favorite practical computer science course. I never took a formal CS class, and this covered a lot of useful material.

SQLZoo

My favorite SQL course. Uses interactive exercises to learn SQL commands step-by-step.

Handbook of Biological Statistics

A useful handbook for practical statistical testing in biology (and probably lots of fields).

Common Statistical Tests are Linear Models

Not really a course, but a cool breakdown of how most statistical tests are really just linear models.

StatQuest

Youtube channel with tons of statistics explanations and tutorials.

3Blue1Brown

Youtube channel with amazingly visual tutorials for lots of topics including linear algebra, calculus, neural networks, and more.

SentDex

Youtube channel with tutorials for seemingly anything you could want to do in Python.

Intro to Brain-like-AGI Safety

Fascinating textbook/series of articles on artificial general intelligence and what precautions we might want to make when it comes to that technology.

Blogs

yohanjohn.com

Yahan John’s blog. Research Assistant Professor at Boston University. Computational neuroscience, cognitive science, and philosophy.

gwern.net

Gwern Branwen’s blog. Psychology, statistics, and technology. Very long-form writing.

vickiboykis.com

Vicki Boykis’ blog. Machine learning engineer at Twitter. Technology, programming, and machine learning.

Columbia Statistic’s blog (mostly Andrew Gelman). Statistics, economics, social science.

guzey.com

Alexey Guzey’s blog. Executive director at New Science. Meta-science, biology, and philanthropy.

ryxcommar.com

Statistics, modelling, and data science.

Statistical Thinking

Frank E Harrell Jr’s blog. Professor of Biostatistics at Vanderbilt Medicine. Just a lot of statistics: Bayesian, NHST, RCT, machine learning, etc.

Data Science Career Advice

Crushed it! Landing a data science job

What candidates can and cannot control in their job hunt

Advice for Applying to Data Science Jobs

Advice for PhD Students Thinking about Data Science Internships

How to become a data scientist as a psychology/neuroscience PhD student

Posts & Articles

Why AI is Harder than We Think

What’s the deal with “brainwaves”?

Deep Neural Nets: 33 years ago and 33 years from now

Deep Learning is Hitting a Wall

We’re Killing Ourselves with Work

Heuristics That Almost Always Work

Lessons Learned Reproducing a Deep Reinforcement Learning Paper

Ergonomics

One Approach to Optimizing Ergonomic Keyboard Layouts

Awesome Split Keyboards (mostly DIY)

List of Assembled Ortholinear Split Keyboards

Another List of Assembled Ortholinear Split Keyboards

Calm Hands, help reduce nail-biting using computer vision and AI

3 minute read

Published: March 02, 2023

drawing Calm Hands helps the user reduce nail-biting during computer use. It provides realtime feedback about nail-biting habits using a deep neural net that monitors images from your webcam stream. This process is entirely local and images are never saved. Feedback is provided through audio and visual cues to alert you of when you are biting your nails. Realtime data visualization is provided as well. Check out the full repo here. Built with: Fastai, OpenCV, Tkinter, CustomTkinter, Matplotlib.

How I Made This

Step 1. Collect training and heldout test images

First I had to collect several hundred images of my biting my nails and not biting my nails. So I created camera.py and the Cam class. I call this in collect_training_data.ipynb. This allowed me to collect hundreds of photos in a variety of locations, lighting setups, and angles very easily.

from camera import Cam

cam = Cam()
# Collect 2 frames per second for 60 seconds.
cam.write_frame_stream(length = 60, wait = .5, path = 'frames/biting')

Step 2. Train the image classifier in Google Colab with fastai

I trained an edgenext_small model (imported from the timm library) using fastai. I used the proven method of finetuning a pretrained image classifier on this specific task.

learn = vision_learner(dls, 'edgenext_small', metrics=error_rate)
learn.fine_tune(3,base_lr = .001)

With ~1000 images and 3 cycles of training, I was at >90% accuracy. But I found there were specific positions and angles that the model was getting wrong.

Step 3. Collecting more data based on model mistakes

I created the realtime_model_preds.ipynb to collect more data quickly, based on the predictions of the existing model. First I made a couple of functions to make predictions and print them out.

def do_prediction(frame):
    with learn.no_bar(), learn.no_logging():
        return learn.predict(frame)

def print_pred(pred):

    conf = round(float(pred[2][pred[1]])*100,2)
    output = f'The model is {conf}% confident that you are {pred[0]}'
    print(output)

Then, I fed the functions a stream of frames from the webcam. This would print out the model predictions of each frame from my webcam.

    # If I press 1, save the frame to the 'good' folder.
    # If I press 2, save it to the biting folder
    key_press_dict = {49:'train/good',50:'train/bad'}
    path = cam.get_path_from_keypress(key_press_dict)
    cam.write_frame(frame,labelled_folder_path=path)

I moved around until I found positions that the model was incorrectly predicting. Then, I pressed either ‘1’ or ‘2’ on the keyboard to save that frame to the correct folder and add it to the training set. After collecting several hundred more photos, I retrained the model. Now it was performing much better on the edge cases.

Step 4. Creating the app

I used tkinter and customtkinter to create the GUI for the app. It displays the webcam feed on one side, and displays an matplotlib plot of the predictions of the model. It also provides instant auditory feedback if I’m biting my nails. Creating the GUI was much more complicated than I thought. I’ve never really created a user interface before, and getting all of the positioning and callbacks working correctly took a while. Also, getting the plot to update smoothly and correctly took a lot of effort. But I think the final product looks and functions well! drawing

Read more

Advice for becoming a data scientist as a psychology/neuroscience PhD student

2 minute read

Published: March 03, 2022

Here’s some advice if you’re considering moving into data science during a psychology PhD. I tried to make it as practical as possible.

1. Learn fundamentals of programming, statistics, and data science.

Learn Python or R, and probably not Matlab. I see way fewer positions that mention Matlab compared to R and especially Python. Take online courses in machine learning and statistics. I highly recommend The Missing Semester of Your CS Education if you have no CS background, like me. Don’t start with deep learning and neural networks. Make sure you understand data science and machine learning fundamentals very well before moving onto deep learning. If you do, I like the fastai course. And check out some of my other recommended Courses & Textbooks.

2. Do data science.

This obviously happens in parallel to step 1. Most neuroscience/psychology research already involves lots of data science elements. You’re probably already a better data scientist than you realize. Building and cleaning data sets, data visualization, statistical testing, modelling, predictions, writing, science communication, etc. Emphasize these aspects of your research. Projects and publications will help show your skills on your resume and during interviews. Make a website to showcase your research and side projects. I use the Minimal Mistakes GitHub Page theme.

3. Communicate early and often.

Tell your PI/advisor that you’re considering doing a summer internship. Some advisors might not let you. I was lucky enough to have a supportive advisor. Tell you’re lab mates, fellow grad students, and collaborators. When people know you’re interested, they are more likely to tell you about interesting positions they know of.

4. Apply to internships.

Aim to do at least 1 summer internship during your PhD. 2 even is better. Ideally between your 3rd & 4th, and 4th & 5th years. Apply to lots of internships. I think I applied to over 50. Use job posting sites like Indeed or Google Jobs. Check Twitter (search “PhD Internship”, “PhD Data Internship”, etc.). Ask friends and collaborators who you know went into industry. Connections might be extremely important because, honestly, it can be difficult to convince recruiters that a psychology PhD student would make a data scientist.

5. Do your internships.

Do a data science internship! You’ll hopefully learn a lot about putting models into production, collaboration, and more structured development. Some internships turn into full-time offers. Once you have one or two internships under your belt, you’ll be in a much better position when applying for full-time jobs upon graduation.

Additional reading.

Advice for PhD Students Thinking about Data Science Internships
Crushed it! Landing a data science job
What candidates can and cannot control in their job hunt
Advice for Applying to Data Science Jobs Read more

Competitive fighting game with heuristic-based AI

6 minute read

Published: January 04, 2022

I created a 1v1 fighting game in Python using PyGame. Included is a 1-player option that allows you to play against a challenging AI opponent. It can make fast decisions based on many different factors of the current game state. I personally lose to it almost half of the time. Read more

Visualizing and Analyzing Bicycle Infrastructure using OSMnx