Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences – Medium. Making Computers Translate So how do we program a computer to translate human language?
The simplest approach is to replace every word in a sentence with the translated word in the target language. Here’s a simple example of translating from Spanish to English word-by-word: This is easy to implement because all you need is a dictionary to look up each word’s translation. But the results are bad because it ignores grammar and context. So the next thing you might do is start adding language-specific rules to improve the results. That worked! This is how the earliest machine translation systems worked. Unfortunately this only worked for simple, plainly-structured documents like weather reports. The problem is that human language doesn’t follow a fixed set of rules. Making Computers Translate Better Using Statistics After the failure of rule-based systems, new translation approaches were developed using models based on probability and statistics instead of grammar rules. Coursera: Machine Learning. Metacademy is an open source platform designed to help you efficiently learn about any topic that you're interested in---it currently specializes in machine learning and artificial intelligence topics.
The idea is that you click on a concept that interests you, and Metacademy produces a "learning plan" that will help you learn the concept and all of its prerequisite concepts that you don't already know. Metacademy's learning experience revolves around two central components: You can tell Metacademy that you understand a [prerequisite] concept by clicking the checkmark next to the concept's title in the graph or list view. Furthermore, you can then click the "hide" button in the upper right to hide the concepts you understand (Metacademy remembers the concepts you've learned, so it'll automatically apply these in the future). Roadmaps. Concepts - Metacademy.
Roadmaps. Level-Up Your Machine Learning. Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?
Excellent question! My answer: consistently work your way through textbooks. I then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, "Oh, I watch what I eat and consistently exercise. " Progress requires consistent discipline, motivation, and an ability to work through challenges on your own. But why textbooks? In this brief roadmap, I list a few excellent textbooks for advancing your machine learning knowledge and capabilities.
Also, if you want alternative learning resources, Metacademy is at your disposal as are all of these textbooks. My sister, an artist and writer by trade, asked me how she could understand the basics of data science in a nontrivial way. Some programming experience [in R]some algebrabasic calculusa little bit of probability theory. Machine Learning/AI/Etc | Tech Topics. Large-Scale Machine Learning with Apache Spark. Machine Learning Video Library - Learning From Data (Abu-Mostafa) Machine Learning and Data Mining Books - A Baker's Dozen for Data Scientists.
Exclusive: Machine Learning Methods and Algorithms DebategraphA. I. HUB. Admiral Shovel and the Toilet Roll — dConstruct Audio Archive. Launch of the Kaggle Data Science Wiki. Our new Kaggle developer, Adam Kennedy, introduces the new Kaggle Wiki: The Kaggle Public Wiki launches today in Beta.
We have built it from the ground up to support the odd mix of science, math and code that makes our sport unique. Since arriving at Kaggle, my main task has been to put together a suitable long-term home for everything the Kaggle community knows about competitive data science. The Kaggle forums are full of great nuggets of advice for competitive data scientists, but they aren't as good at organizing this information and improving it over time. We want to make learning data science easier for new competitors, help our existing competitors with new techniques and tactics, and free up the forums to act as, well, a forum. We've based the wiki on the excellent Markdown format, to which we've added the same LaTeX support we use in the forums and extended code syntax highlighting to support MATLAB (with R highlighting on the way). Construct, contribute, play nice, be bold. How Khan Academy is using Machine Learning to Assess Student Mastery. See discussion on Hacker News and Reddit.
The Khan Academy is well known for its extensive library of over 2600 video lessons. It should also be known for its rapidly-growing set of now 225 exercises — outnumbering stitches on a baseball — with close to 2 million problems done each day. To determine when a student has finished a certain exercise, we award proficiency to a user who has answered at least 10 problems in a row correctly — known as a streak. Proficiency manifests itself as a gold star, a green patch on teachers’ dashboards, a requirement for some badges (eg. gain 3 proficiencies), and a bounty of “energy” points. Basically, it means we think you’ve mastered the concept and can move on in your quest to know everything. It turns out that the streak model has serious flaws.
First, if we define proficiency as your chance of getting the next problem correct being above a certain threshold, then the streak becomes a poor binary classifier. In Search of a Better Model to this: . . . . NPSML: Public Domain C Machine Learning Library with Almost No Dependencies : programming.