Editor's Picks

• Everything I learned about accidentally running a successful tech conference
  On December 15, 2022, the first and only Normconf, the tech conference about all the stuff that matters in data and machine learning but doesn’t get the spotlight, happened...I do want to offer some takeaways for people who are thinking about how to run online conferences and communities and create vibrant and happy online spaces...

• What did you screw up this year? [Reddit Discussion]
  I will go first. Yesterday I deployed my spark script and it overwrote the table I been loading for a while...Just started a new project this week and this project use older version of Spark. It was supposed to only overwrite the partition but instead, the whole table was wiped...Who knows that dynamic partition overwrite only work after spark 2.3...Now I am spending part of the weekend rebuilding the table...

• What Building "Copilot for X" Really Takes
  This comes from the Codeium team, who I’ve had the good fortune of getting to know in the past year. This small team blew away my expectations by creating a complete Copilot clone in one month (not exaggerating - I have disabled GitHub Copilot completely to use them, in part to beta-test Hey Github, but also Codeium seems a bit faster!). Since “Copilot for X” is at the top of every AI product thinker’s wishlist, I invited them to share their learnings!...To build a “Copilot for X”, you must...

A Message from this week's Sponsor:

• Data Science on Kubernetes
• Hadoop replacement
• Apache Livy alternative
• Integration with Jupyter and Apache Zeppelin

Data Science Articles & Videos

• How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement Volume
  In this blog post, we will demonstrate how we improved Pinterest Homefeed engagement volume from a machine learning model design perspective — by leveraging realtime user action features in Homefeed recommender system...

• Will there be a replacement for Machine Learning Twitter? [Reddit Discussion]
  It seems like a lot of prominent ML researchers are pretty active on Twitter, and that it's a decent place to hear about new research and promote your own stuff. But, the inmates appear to have taken over the asylum over there. Will there be, for example, an ML Mastodon instance?...

• Why Business Data Science Irritates Me
  Data Science is a pretty solid career compared to most others where you can earn a lot of money. This post is about my gripes as an industry insider, not intended to discourage anyone from pursuing it as a career...I resonated with ryx's post so much that I felt compelled to write my reasons for being frustrated with industry data science, and what I think you can practically do about it if you feel the same way...

• Build a GPT-3 app: How I used GPT-3 to make gifting easier
  I built Gifthub...Gifthub is a gift ideas generator powered by GPT-3. Now, instead of asking someone for advice, I can answer a few questions regarding the person's interests, hobbies, gift type, and get a bunch of unique gift recommendations...

• ImageNet-X - Understanding model mistakes with human annotations of ImageNet
  ImageNet-X is a set of human annotations pinpointing failure types for the popular ImageNet dataset. ImageNet-X labels distinguishing object factors such as pose, size, color, lighting, occlusions, co-occurences, etc. for each image in the validation set and a random subset of 12,000 training samples...

• What I learned from NormConf 2022
  For busy people who didn't yet get the chance to watch all the excellent @normconf talks, here's a written summary of my lessons learned...

• Univariate Analysis — Intro and Implementation
  As a data scientist, what is the first step you do when you receive a new and unfamiliar set of data? Well, we start familiarizing ourselves with the data. This post focuses on answering that question by analyzing only one variable at a time, which is called a univariate analysis...

• Dimensionality Reduction for Linearly Inseparable Data
  Standard PCA works well with linearly separable data in which the different classes can be clearly separated by drawing a straight line (in the case of 2D data) or a hyperplane (in the case of 3D and higher dimensional data)...Standard PCA will not work well with linearly inseparable data in which the different classes...can only be separated by using a curved decision boundary...For nonlinear dimensionality reduction, we can use the kernel PCA which is the non-linear form of the standard PCA...

• Agree not to Disagree
  About a year ago, I stumbled upon the GoEmotions dataset. It's a dataset that contains user texts from Reddit with emotion annotations. Google attached its name to the dataset and published a paper about how the dataset got created...All of this effort might give you the impression that this is a dataset that's ready for a machine learning model. You'd be wrong, though...GoEmotions has been a great motivation for me to dive into techniques that find mislabeled examples...It's one thing to find bad annotations in ML, but it'd be better to learn how we might get good annotations instead...So in this blog post, I’ll explore this by diving into the dataset some more...

• Do Simpler Machine Learning Models Exist and How Can We Find Them
  The above is the title of a talk by computer scientist Cynthia Rudin...

• PufferLib
  You have an environment, a PyTorch model, and an RL framework that are designed to work together but don’t. PufferLib is a wrapper layer that provide better compatibility between Gym / PettingZoo environments and standard reinforcement learning frameworks. You write a native PyTorch network and a short binding for your environment; PufferLib takes care of the rest...

• AI is changing everything, and it's not just ChatGPT or self-driving cars [Twitter Thread]
  You'd be surprised at just how broadly AI is being applied across tons of industries...I’ve now invested in 50 AI startups, and here are some recent investments using AI in fascinating applications 👇...

Tool*

• built-in visualizations (like the image panel),
• custom project views, and
• your own python panels,

Tool*

Jobs

• Data Scientist / Machine Learning Engineer - Epsilon - NYC
  
  Epsilon Strategy and Insights, Data Sciences team is looking for a talented team player in a Data Scientist/Machine Learning Engineer role. You are an expert, mentor and advocate. You have strong machine learning and deep learning background and are passionate about transforming data into ml models. You welcome the challenge of data science and are proficient in Python, Spark MLLib, Tensorflow, Keras, ML algorithms and Deep Neural Networks, Big Data. You must be self-driven, take initiative and want to work in a dynamic, busy and innovative group...
   

Training & Resources

• Convert Jupyter Notebooks into Functions
  Parameterize notebooks so you can programmatically run them...

• A guide to using ggmap in R
  Learn how to work with ggmap, a cool R library to visualize data...

• sts-jax: Structural Time Series (STS) in JAX
  This library has a similar to design to tfp.sts, but is built entirely in JAX, and uses the Dynamax library for state-space models. We also include an implementation of the causal impact method. This has a similar to design to tfcausalimpact, but is built entirely in JAX...

Last Week's Newsletter's 3 Most Clicked Links

• TikTok’s Secret Sauce

• Failed ML Project - How bad is the real estate market getting?

• Building a synth with ChatGPT

Cutting Room Floor

• Biology Breakthroughs of 2022
• In order to understand why ChatGPT can't replace Google Search, it's useful to understand the early days of web search and the role that PageRank played...