Many endurance sports require the participant to follow a pre-set course and achieve specific control points by a specific time. Confirmation of a participants adherence to the pre-set course and control guidelines has been accomplished through the use of paper systems, photographs and manned control points.

The use of a participants GPS file, generated from the participants bike computer, sport watch or smartphone is gaining adoption. However that requires the event coordinator to manually gather, compare and manage the participants GPS file for verification purposes. …

- Language
- Strings
- Symbols
- Sets
- Operations
- Properties
- Rules
- Classes of Languages

Previously, we talked about how languages are studied using the notion of a formal language. Formal language is a mathematical construction that uses sets to describe a language and understand its properties.

We introduced the notion of a string, which is a word or sequence of characters, symbols or letters. Then we formally defined the alphabet, which is a set of symbols. The alphabet often goes hand in hand with the language because we define a formal language as a set of strings over a unique alphabet.

Then we explored…

This article is meant to be a gentle introduction to NLTK. As with everything, we will try to balance mathematical rigor, programmatic ease of use with concrete examples that have linguistically motivated examples.

In many ways, this article is the programmatic introduction to computational linguistics, and is a mirror to this article.

NLTK is a leading platform for building Python programs to work with human language data. …

- Goal
- Input & Output
- Encoding & Decoding
- Architecture
- Regularization
- Validation

- Import
- Model Definition
- K-Fold Validation
- Validation
- Final Model

We will try to predict the median house price given 13 different parameters. The parameters are attributes such as crime rate, property tax rate, square footage etc.

You can learn more about the data set here and here.

The input variables in order are:

- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0…

**DEFINITION 1. FORWARD PROPAGATION**

Normally, when we use a neural network we input some vector x and the network produces an output y. The input vector goes through each hidden layer, one by one, until the output layer. Flow in this direction, is called **forward propagation**.

During the training stage, the input gets carried forward and at the end produces a scalar cost J(θ).

**DEFINITION 2. BACK PROPAGATION ALGORITHM**

The back-prop algorithm then goes back into the network and adjusts the weights to compute the gradient. To be continued…

This article is meant to be a gentle introduction into formal language theory. As with everything, we will try to balance mathematical rigour with concrete examples that have linguistically motivated examples.

Topics to touch on:

- Elementary formal language theory
- Regular languages
- Regular expressions
- Languages
- Finite state automata
- Regular relations

**Formal languages** are defined to have a certain, specific **alphabet**. The **alphabet** may be denoted by ∑.

- Goal
- Input & output
- Training
- Regularization
- Predicting

- Deeper layers
- Wider layers
- Narrower layers

Moore’s law means a lot of things to lots of different people. It predicted the doubling of transistors on a chip every two years. This law roughly translated to doubling of computing power to business analysts, and it was the signal that stated computing industry was still the pony to bet on. For the engineers, this meant that given a problem, often the solution was to throw more computing power on it or wait a few until the processing power caught up to it. In recent years, it’s been a worry that this law may be slipping due to product…

Neural networks with n layers will have n-1 hidden units and 1 output unit. We have studies hidden units and output units. For example, we know from 1 to n-1 we have hidden layers, in other words, every layer except the last one. Whereas the last one is called the output layer. We studied what these layers consist of, units. As well as what these units consist of, weights and activation functions.

But what else is variable in a neural network? The other feature we can vary in the neural net is the overall structure of the network. …

It was 483 BCE, and the city of Athens was still rising to power. Athens had just overthrown a particularly painful monarch and for the first time in history established democracy in 508 BCE. Art, culture, political thought, and philosophy were all thriving in this city-state. Due to this fact, Athens had previously supported the Ionian revolt against the Persian King Xerxes rule. To set an example the Persian Empire was amassing an enormous army and navy to invade Athens and Sparta by land and the Aegean Sea. …

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