# Data Machina #150

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**A ****Tour-de-force****on Causality & Machine Learning**: Right! so there is a lot of ‘*debate*’ recently on whether Causality/Causal Inference can help Machine Learning overcome some of its “ limitations” or not.

Fist, apologies: A link correction is needed. In the previous free version of Data Machina I wrote **On Causal Inference and *** The Book of Why*.

As I said I’m reading this fascinating book and also following this *intellectual match* between Judea Pearl, the *creator* of Bayesian Networks and Probabilistic AI, and Andrew Gelman, a* famous* Bayesian statistician. **Here is the correct link**: Pearl bashes statisticians and thinks AI needs causal inference, and Gelman has difficulty understanding the point of Pearl’s writing on causal inference. Enjoy the reading.

Second, the basics. Let me recommend you the following readings, books, courses, stuff on **Causal Inference**:

Start here:

*Causal Inference in Statistics: An Overview*by Judea PearlMIT’s Elements of Causal Inference: Foundations & Algorithms

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Harvard’s Causal Inference Book: Concepts of, and Methods for Causal Inference

Third, let me bombard you with some interesting stuff on **Machine Learning and Causality**:

In this post Judea Pearl writes about *The Seven Tools of Causal Inference and Reflections on Machine Learning*

Back in 2017, the *The Unofficial Google Data Science Team* published this great post: *Causality in Machine Learning*

In this interview, Judea Pearl argues that *To Build Truly Intelligent Machines, [we should] Teach Them Cause and Effect*

At this forum some *great minds* like Judea Pearl, Michael Jordan, Leon Bottou, Hal Varian… discuss *Drawing Causal Inference from Big Data*

Michiel -a PhD researcher in Machine Intelligence- explains what Machine Learning can and can’t do, and how *Climbing the Ladder of Causality* can help.

Yanir - a Kaggle Master- claims that the often overlooked topic of causality should be more relevant for data scientists than Deep Learning. He elaborates: *Why You Should Stop Worrying About Deep Learning And Deepen Your Understanding Of Causality Instead*

Here you can access all the course materials of: *Harvard’s Advances in Causality and Foundations of Machine Learning*

In this paper, the folks at IBM Research write about *Explaining Deep Learning Models using Causal Inference*

In this presentation, the team at Oxford Policy Management wonders *How can Machine Learning be Employed to Help with Causal Inference?*

In this video talk*, *Professor Bernhard Schölkopf @Max Planck Institute for Intelligent System talks bout *Statistical and Causal Approaches to Machine Learning*

And finally, the ever great Ferenc -it’s been ages! since we last met- writes about why he’s become a full-on *Causal Reasoning believer*, and why Causal Inference and Causal Diagrams complement Deep Learning. Read more here: *Machine Learning beyond Curve Fitting: An Intro to Causal Inference and do-Calculus*

**Postscript, etc**

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Tips? Suggestions? Feedback? Send email to Carlos

Curated by Carlos @ds_ldn in the middle of the night.

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**Data Machina #149 - Paid Subscription**

**On Deep Learning and ****New**** Programming Languages: **Deep Learning may need a new programming language to overcome Python’s weaknesses

**Swift for Tensorflow**: it is time to embrace Swift for Machine Learning

Swift for TensorFlow: The Next-Generation Machine Learning Framework

**Flux in Julia**: we need a language to write differentiable algorithms.

**Maybe Rust?**: A work-in-progress catalog on the state of Rust for Machine Learning

**Or Owl in OCaml?**: a State-of-the-Art, platform for functional scientific and numerical computing

**10 Link-o-Troned**

The World's 1st Immersive Linear Algebra Online Book

JupyterBooks - Inspirational Machine Learning Notebooks

Easily Transpile Trained ML Models into Native Python, C, Java

Taco Bell Programming: Hadoop Hell and The Unix Zen

Viewing Matrices & Probability as Graphs

Awesome Network, Graph Embeddings - A Curated List

Visual Exploration of Neural Nets with Activation Atlases

Ultrafast Geospatial DB for Geofencing & Location-based Apps

GPU Accelerated Javascript for Massive Parallel Computations

[free ebook] Harvard+Stanford Intro to Probability [609 pages, pdf]

**A Pythonista *Experience***

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Poincaré Embeddings for Learning Hierarchical Representations

Location Embeddings: Implementing Loc2Vec in PyTorch

**beCause of Dennis & Bjarne**

rec2c - A Fast, Open-source Tokenizer in C++

Shogun- Unified, Efficient Machine Learning

xtensor- C++ Tensor Algebra Library

**Scripting aRt**

Classifying News Content with R, bash & Vowpal Wabbit

Functional Data Analysis in R Course: Lectures & Codes

Feature Selection in R with mlr

**Love from Julia**

Probabilistic Programming with Programmable Inference

Google's Machine Learning Crash Course in Julia

Multiple Dispatch - An Example for Math Optimizers

**(Paren(th)ethical)**

Getting Started with Clojure and MXNet on AWS

justus.ai- a Clojure Wrapper for DeepLearning4J

You’re in a Maze of Deeply Nested Maps, All Alike

**ScalaTOR**

How to Deploy KubeFlow on Lightbend (9 Chapters)

*Category Theory for Programmers*, Milewski 3 March 2019DynaML -a Scala & JVM Machine Learning Toolbox

**data v-i-s-i-o-n-s**

A Visual Exploration of Exoplanets

A New Way to Visualise Interactions in Neural Nets

Google DataGIF Maker to Compare Data & Tell Stories

**Distributed de-Entangler**

[free ebook] Distributed Systems for Fun & Profit

Overview of Ozone: A Modern Object Store for Hadoop

All the Talks from Facebook Data@Scale Conference

**Blockchain Über Alles**

Decentralized, Self-Sovereign, and Blockchain Identity

The 1st Python Blockchain with Turing Complete Contracts

Why it is Impossible to Solve Blockchain Trilemma?

**IoTea - everyThing/anyThing**

Noise Mapping with KafkaSQL, RasPi & Software-Defined Radio

IoT & Fraud Detection with Kafka, Tensorflow & Google Cloud

Industrial IoT with Kafka, Flink and CrateDB

**Forschung!**

A Fast Multi-pattern Regex Matcher for Modern CPU

Agents that Learn to Follow Directions in Google Street View

Emotion-based Fake News Detection with word2vec & RNNs

**Algorithmic Potpourri**

Image-Based Airbnb Pricing Algorithm

Training Time Estimation for scikit-learn Algorithms

Interactive, Online: Path Finder Algorithms

**Robots & Cyborgs like <you>**

The Amazing MIT Mini Cheetah Robot

Learning to Walk via Deep Reinforcement Learning

Learning from Demos to Mimic Human Behaviour

**Deep & Other Learning Bits**

Deep Learning to Federated Learning in 10 Lines of Code

All the Projects from UC Berkley Deep Learning Spring2019

Integrating Domain Knowledge into Deep Learning [pdf]

**startups -> radar**

RaptorMaps - Machine Learning for Solar Panel Inspections

Orcam - Advanced Wearable AI Devices for the Blind

Freenome - AI Genomics for Cancer Detection

**ML Datasets & Stuff**

Common Voice - The Largest Human Voice Dataset

Who Links Whom? 1.78 Billion Links Graph Dataset

GrapAL- Knowledge Graph of 40 Million Academic Papers

**Postscript, etc**

Spread the word Share Data Machina with your friends

Tips? Suggestions? Feedback? Send email to Carlos

Curated by Carlos @ds_ldn in the middle of the night.