Brandon Rohrer

Book Project: DIY Networking

• Chapter 1: Host your own web server
• Chapter 2: Set up a domain name for your website
• Chapter 3: Make your webserver secure
• Chapter 4: Caring for your webserver
• Chapter 5: Traffic control
• Chapter 6: Automating traffic control
• Chapter 7: Bomb-proof your website with mirroring
• Chapter 8: Moving your code from prototype to production
• Chapter 9: How to start publishing an RSS feed
• Chapter 10: Setting up an ssh server Notes from my home hackery.
• Chapter 11: Writing a bare-bones HTTP client in Python
• Chapter 12: Writing a bare-bones HTTP server in Python
• Appendix A: The word "server" can mean several things
• Appendix B: Addition App, frontend code, backend code
• Appendix C: Graffiti Wall App
  • frontend code
  • backend code
  • architecture walkthrough
  • frontend walkthrough
  • backend walkthrough

Book project: How to Train Your Robot

How to Train Your Robot is a book-in-progress about applied robotics, machine learning, and software engineering.

• Chapter 1: Can't AI Already Do That?
• Chapter 2: Keeping Time with Python
• Chapter 3: Getting Processes to Talk to Each Other
• Chapter 4: Making Animations with Matplotlib
• Chapter 5: Simulating the Physical World
• Chapter 6: Making Your Python Code Run Faster
• [In progress] Chapter 7: Would You Like to Play a Game?
• [In progress] Chapter 8: Deconstructing Sound
• Chapter 9: Ziptie: Learning Useful Features
• [In progress] Chapter 10: Reconstructing Sound
• Chapter 11: Naive Cartographer: A Markov Decision Process Learner
• Chapter 12: Myrtle: A Real-time Reinforcement Learning Workbench
• Chapter 13: A Dead Simple Message Queue
• [In progress] Chapter 14: DIY webserver
• [In progress] Chapter 15: Animations in Javascript
• [In progress] Chapter 16: Testing a distributed system
• [In progress] Chapter 17: BucketTree, an automated discretizer
• Chapter 18: Putting it all together to invert a pendulum
• Chapter 19: Getting started with Arduino
• Chapter 20: Interrogating an Arduino sensor

Book project: Small Tech

Small Tech is a book about doing analytics, data, and machine learning in small and medium-sized bootstrapped companies

• Chapter 1: Build with AI or traditional ML?
• Appendix A: Data engineering for the absolute beginner

Book project: Under the Hood of Machine Learning

• Chapter 1: Choosing between models
• Chapter 2: Separating signal from noise
• Chapter 3: Choosing a loss function
• Chapter 4: Splitting the data
• Chapter 5: Navigating assumptions
• Chapter 6: Optimization methods
• Chapter 7: Optimization with a central tendency model
• Chapter 8: Optimization with a linear model
• Chapter 9: Optimization with a complex model
• Chapter 10: How backpropagation works
• Chapter 11: Decision trees
• Chapter 12: Support vector machines
• Chapter 13: Autocorrelation
• Chapter 14: Convolution in one dimension
• Chapter 15: Convolution in two dimensions
• Chapter 16: Bayesian inference
• Chapter 17: Fully connected neural networks
• Chapter 18: Regularization
• Chapter 19: Softmax
• Chapter 20: Batch normalization
• Chapter 21: What neural networks can learn
• Chapter 22: Convolutional neural networks
• Chapter 23: Convolutional neural networks, in depth
• Chapter 24: Recurrent neural networks and LSTM
• Chapter 25: Transformers
• Chapter 26: Controlling a pendulum with reinforcement learning
• Chapter 27: Approaching human intelligence through robotics
• Chapter 28: Electrocardiogram case study
• Chapter 29: Compressing images from the Mars rover
• Chapter 30: MNIST digits case study
• Chapter 31: CIFAR-10 images case study
• Appendix A: What questions can machine learning answer

Book project: Data Munging

• Chapter 1: What is data
• Chapter 2: How to get good quality data
• Chapter 3: Data types
• Chapter 4: How to use datetime
• Chapter 5: Reading and writing data files
• Chapter 6: Play and record sounds
• Chapter 7: Converting audio between formats
• Chapter 8: Turn images to videos and back
• Chapter 9: How to slice and index pandas DataFrames
• Chapter 10: Create your first database in SQLite
• Chapter 11: Navigating the awkwardness of databases
• Chapter 12: Converting a csv to a database
• Chapter 13: Create your first database in Postgres
• Chapter 14: Data science for beginners
• Chapter 15: How data science works
• Chapter 16: There is more to data science than machine learning
• Chapter 17: Data science archetypes

Book project: Signal Processing Tricks

• Chapter 1: Rate of change
• Chapter 2: How to normalize a signal by mean and variance
• Chapter 3: How to normalize a signal by its minimum and maximum
• Chapter 4: Exponential smoothing
• Chapter 5: 1D convolution
• Chapter 6: 2D convolution
• Chapter 7: How to convert RGB color images to grayscale
• Chapter 8: How to turn a picture into numbers
• Chapter 9: Turn images to videos and back
• Chapter 10: Play and record sounds

Python packages

• buckettree, an automated discretizer
• cottonwood, a teaching-focused neural network framework
• dsmq, a dead simple message queue
• myrtle, a workbench for real-time reinforcement learning
• pacemaker, a steady time keeper for wall-clock sensitive functions
• sqlogging, a SQLite-backed logging package that mimics Python's logging package

Algorithms and Methods

• Evolutionary Powell's method for hyperparameter tuning
• k-nearest neighbors adaptive feature weighting
• k-nearest neightbors data reduction
• k-sparse neural network layer
• Naive Cartographer , a Markov Decision Process learner
• oknn, an online variant of kNN
• Pathfinder, a many-to-many shortest path algorithm
• Sharpened Cosine Similarity, an alternative to convolution for learning 2D features in neural networks
• Ziptie, an unsupervised feature creation algorithm

Matplotlib Guides

• I. Quick start guide
• II. Three important ideas
• Colors and colormaps
• Layout, background, and multiple plots
• Lines and curves
• Manual animations
• Patches
• Scatterplots and points
• Text, axis labels, and annotation
• Ticks, tick labels, and grids

Making Python Faster

• Make your code run faster
• Multiprocessing for Parallelization
• Multiprocessing for Real-Time Applications
• Threading
• Make your own personal Python toolbox

Unsolicited Advice

• Build a strong distributed data science team
• Choose your professional path
• Decide and commit
• Get to know your new company
• How to be an influencer
• How to choose a project
• How to choose your tools
• How to get a job you like
• How to get hired as a data scientist
• How to solve a hard problem
• Imposter syndrome
• On microsuffering
• Oversimplify your communication
• What I learned running online courses
• What to do when a leader does something wrong

Resources

• Calculus
• C++
• Git
• Linear Algebra
• Machine learning
• Mental Focus
• NumPy
• Python
• SQL
• Statistics
• uv cheatsheet for pip users

Everything Else

• Artisanal Language Models
• Build a custom tokenizer [In progress]
• A love letter to Empirical Design in Reinforcement Learning
• Handling stochastic non-integer delay in reinforcement learning
• Customizing syntax highlighting in vim on markdown files
• Data Revolution Podcast
• Machine Learning Street Talk ML from nuts and bolts
• What I learned building an online school

About me

• Codeberg
• GitHub
• GitLab
• LinkedIn
• Mastodon
• Publications
• RSS feed
• YouTube