### using information theory for black hole discovery

We recently published a paper in the journal ** Nature** about an "

**An intermediate-mass black hole in the centre of the globular cluster 47 Tucanae**". For the first time, we are effectively combining information obtained from N-body simulations, pulsar observations and use information theory to probe whether there is a black hole in centers of globular clusters.

Click below for our paper:

### applications to astrophysical problems

- Come and listen to my talk at the upcoming "Detecting the Unexpected: Discovery in the Era of Astronomically Big Data" conference on "
**Pushing the Frontiers of Astronomical Discovery with Deep Learning**" to be held in Baltimore, STScI, between 27 February-2 March 2017.

### applied statistics

- asymmetric error bars: the asymmetric nature of error bars are often ignored, and the errors are typically approximated with Gaussian models. Take a look at our paper on the "
**Neutron Star Mass Distribution**". We developed a novel approach which is generically applicable for data with asymmetric error bars. For technical detail see the*Appendix*, subsection on*Model Formulation*. Don't ignore the value of information that may be 'hidden' in the errors!

### data science

- Data Science Bowl: the world’s premier data science for social good competition
- Kaggle: data science challenges
- Kaggle Past Solutions: inspiration for data science problems
- Data Science Central: blogs with lots of useful tips

### tools, articles, and tutorials I find useful

- scikit-learn: a useful tool for machine learning in python. Particularly powerful for classification, regression, and model selection.
- Top 8 Free Must-Read Books on Deep Learning
- aetros: an artificial platform for everyone.
- inFERENCe: posts on machine learning, statistics. Particularly recommended reading on Deep Learning and infoGANs.
- an interesting post by the MIT Technology review on the relation of deep learning and the universe based on article by Henry Lin (Harvard) and Max Tegmark (MIT). This might not convince the AI community and some astronomers (!), but has a nice ring to it!
- Deep Learning Adversarial Examples – Clarifying Misconceptions.
- interactive tutorial on numerical optimization.
- online 3D data- tensorflow playgroud.
- online deep learning - tensorflow playgroud.
- compare Deep Learning Frameworks, by Nvidia.
- chainer: a Powerful, Flexible, and Intuitive Framework for Neural Network.
- PyTorch: Check this out if you are interested in dynamic neural networks with GPU acceleration.
- Andrej Karpathy's blog on "The Unreasonable Effectiveness of Recurrent Neural Networks".
- great blog on neural networks by cloah.
- make neural networks intuitive with TensorBoard.
- TFLearn: a wrapper optimized for Tensorflow that has the modularity of Keras.
- master machine learning repo.
- microsoft's machine learning cheat sheet.
- github repo for deep learning+ cheat sheets.
- a nice guide to deep learning.
- another great online tutorial on neural networks
- marching neural network: visualizing level surfaces of neural networks
- comprehensive deep learning tutorial as a python notebook on using PyTorch
- pyro: deep universal probabilistic programming
- CapsNet and Capsule networks. What are they?
- good advice on how to build your own Deep Learning box
- A curated list of awesome Deep Learning tutorials, projects and communities.
- How to setup Nvidia Titan XP for deep learning on a MacBook Pro with Akitio Node + Tensorflow + Keras
- An Attempt at Demystifying Bayesian Deep Learning
- Course on the Application of Deep Neural Networks
- 2018 Data Science Bowl
- The hdbscan Clustering Library
- Start Here With Machine Learning
- Finland offers free online Artificial Intelligence course to anyone, anywhere
- Machine Learning, Data Science, Big Data, Analytics, AI
- easy-tensorflow · GitHub
- Data Driven NYC
- A curated list of awesome machine learning interpretability resources.
- List of Free Must-Read Machine Learning Books – Towards Data Science
- I failed my effing coding Interview!? – Noteworthy - The Journal Blog
- Keras or PyTorch as your first deep learning framework | deepsense.ai
- Deep Learning Tips and Tricks
- fast.ai Machine Learning Course Notes
- Sizing the potential value of AI and advanced analytics | McKinsey & Company
- Introduction to Apache Spark
- Cracking the Machine Learning Interview – Subhrajit Roy – Medium
- Deep Learning | Kaggle
- Machine Learning | Kaggle
- Pandas | Kaggle
- A gallery of interesting Jupyter Notebooks · jupyter/jupyter Wiki · GitHub
- TDA To Rule Them All: ToMATo Clustering – Towards Data Science
- Bayesian Deep Learning on a Quantum Computer – Arxiv Vanity
- Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) – Arxiv Vanity
- Foundations of Machine Learning
- Forecasting in Python with Prophet | Data Visualization Gallery - Mode Analytics
- The 5 Clustering Algorithms Data Scientists Need to Know
- How to Prepare for a Machine Learning Interview - Semantic Bits
- Data Science Central
- Bayesian Methods for Hackers
- Fraud Detection Using Autoencoders in Keras with a TensorFlow Backend
- Start With Gradient Boosting, Results from Comparing 13 Algorithms on 165 Datasets
- CS229: Machine Learning
- Where machine learning professionals come to learn, share knowledge, and build their careers
- Time Series Forecasting with the Long Short-Term Memory Network in Python
- MIT 6.S191: Introduction to Deep Learning
- The Most Important Machine Learning Algorithms - Semantic Bits
- GAN Lab: Play with Generative Adversarial Networks in Your Browser!
- Seeing Theory
- Uniform Manifold Approximation and Projection
- How Do Artificial Neural Networks Learn? – Towards Data Science
- How Autoencoders Work: Intro and UseCases | Kaggle
- Don’t Use Dropout in Convolutional Networks. – Towards Data Science
- MIT 6.S191: Introduction to Deep Learning
- Jupyter Notebook Viewer
- A Brief History of Machine Learning Models Explainability
- Convolutional Neural Networks for Beginners: Practical Guide with Python and Keras
- How to Develop an Autoregression Forecast Model for Household Electricity Consumption
- Papers with code. Sorted by stars. Updated weekly
- Deep Misconceptions About Deep Learning – Towards Data Science
- Colaboratory Notebooks for pix2pix - Colaboratory
- Lectures for INFO8006 - Introduction to Artificial Intelligence
- The "Python Machine Learning (2nd edition)" book code repository and info resource
- Why Deep Learning Works: ICSI UC Berkeley 2018 - YouTube
- Introduction to Learning to Trade with Reinforcement Learning – WildML
- Outlier Detection (Anomaly Detection)
- Deep Learning with TensorFlow 2 and Keras
- Deploy your machine learning models with Kubernetes
- GAN playground - explore generative adversarial nets in your browser
- The hdbscan Clustering Library: w/ useful and nice comparative visualization
- For probabilistic neural networks: Edward, TensorFlow Probability, and Pyro