• Learn how Harry’s uses AWS to simplify infrastructure operations
• Learn how AWS Fargate makes it simple to run containers
• Learn how to use AWS services like CloudFormation, Auto Scaling, and Load Balancing with AWS Fargate
This post is adapted from a presentation at nginx.conf 2016 by Yichun Zhang, Founder and CEO of OpenResty, Inc. This is the first of two parts of the adaptation. In this part, Yichun describes OpenResty’s capabilities and goes over web application use cases built atop OpenResty. In Part 2, Yichun looks at what a domain-specific language is in more detail.
You can view the complete presentation on YouTube.
Bayesian inference is a way to get sharper predictions from your data. It’s particularly useful when you don’t have as much data as you would like and want to juice every last bit of predictive strength from it.
Although it is sometimes described with reverence, Bayesian inference isn’t magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.
Bayesian inference is based on the ideas of Thomas Bayes, a nonconformist Presbyterian minister in London about 300 years ago. He wrote two books, one on theology, and one on probability. His work included his now famous Bayes Theorem in raw form, which has since been applied to the problem of inference, the technical term for educated guessing. The popularity of Bayes’ ideas was aided immeasurably by another minister, Richard Price. He saw their significance, refined them and published them. It would be more accurate and historically just to call Bayes’ Theorem the Bayes-Price Rule.
In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.
If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.
If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.
As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos.
Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.
This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
Generation of diagrams and flowcharts from text in a similar manner as markdown.
Ever wanted to simplify documentation and avoid heavy tools like Visio when explaining your code?