Welcome to fast.ai’s 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing… it’s totally free!

This service uses linguistic analysis to detect and interpret emotions, social tendencies, and language style cues found in text.

Hawkins and Ahmad now say they know what’s going on. Their new idea is that distal and proximal synapses play entirely different roles in the process of learning. Proximal synapses play the conventional role of triggering the cell to fire when certain patterns of connections crop up.

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But distal synapses do something else. They also recognize when certain patterns are present, but do not trigger firing. Instead, they influence the electric state of the cell in a way that makes firing more likely if another specific pattern occurs. So distal synapses prepare the cell for the arrival of other patterns. Or, as Hawkins and Ahmad put it, these synapses help the cell predict what the next pattern sensed by the proximal synapses will be.

Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Come and get the latest news on Data Analytics! We will talk about Deep Learning, Machine Learning, the next-generation distribution of Apache Hadoop for "normal people", and other new technology you want to know about. This is a half day seminar event.

Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.

The informatics researcher began his experiment by selecting a straightforward task for the chip to complete: he decided that it must reliably differentiate between two particular audio tones. A traditional sound processor with its hundreds of thousands of pre-programmed logic blocks would have no trouble filling such a request, but Thompson wanted to ensure that his hardware evolved a novel solution. To that end, he employed a chip only ten cells wide and ten cells across— a mere 100 logic gates. He also strayed from convention by omitting the system clock, thereby stripping the chip of its ability to synchronize its digital resources in the traditional way.

One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana (see related work in [1], [2], [3], [4]). By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.

BayesDB, a Bayesian database table, lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries.

K-tree is a tree structured clustering algorithm. It is also refered to as a Tree Structured Vector Quantizer (TSVQ). The goal of cluster analysis is to group objects based on similarity. Each object in a K-tree is represented by an n-dimensional vector. All vectors in the tree must have the same number of dimensions.