AI News, gianlucabertani/MAChineLearning

gianlucabertani/MAChineLearning

Setting up a network is a matter of two lines: These lines create a single layer perceptron with 3 inputs and 1 output, with step activation function, and randomize its initial weights.

In this case, you can make use of macros defined in MLReal.h, to avoid changing function names in case you later move from single to double precision: Once the input buffer is filled, computing the output is simple: If the output is not satisfactory, you can set the expected output in its specific buffer and ask the network to backpropagate the error.

Once your training batch is complete, update weights in the following way: During training, you typically feed the full sample set to the network multiple times, so that it increases its predictive capabilities.

typical training loop is the following: While a training loop with batch updates is the following: The network enforces the correct calling sequence by using a simple state machine.

It is based on an vector of numbers where each element represents a word in the text, and (in its simplest form) is either set to 1 o 0 if that word occurs or not in the text to be represented.

Given a text, it is then split in separate words (a process called tokenization) and, for every word, they are looked up in the dictionary and their corresponding element on the array is set accordingly.

number of improvements may be applied to this process, including the removal of frequently words (called stop words), more or less sophisticated tokenization, normalization of the bag of words vector, et.c The Bag of Words toolkit in MAChineLearning currently supports: With MAChineLearning, the dictionary for the Bag of Words is built progressively as texts are tokenized, you just need to fix its maximum size from the beginning.

For language guessing there are two utility methods available, employing either the macOS integrated linguistic tagger or an alternative algorithm that counts occurrences of stop words: The language is expresses as a ISO-639-1 code, such as 'en' for English, 'fr' for French, etc.

The most extended version of the MLBagOfWords factory method includes parameters to specify both the tokenizer and its tokenization options: Default configurations are the following: You may need to experiment a bit to find the correct configuration for your task.

In fact, Word Vectors can be summed and subtracted to form new meanings, such as the following well know examples: The Word Vectors toolkit in MAChineLearning supports loading pre-computed word vector dictionaries of the following models: Note: While a tentative at building a Word Vectors dictionary from a text corpus has been made, using the neural networks of MAChineLearning, it resulted impractically slow.

Computing Word Vectors from scratch, in fact, requires code specifically optimized for the task, since each text is a sparse vector (a Bag of Words, actually) and a general purpose neural network wastes lots of time computing values for zeroed elements.

Each vector provides methods to sum and subtract to/from other vectors, and the dictionary provides methods to search for the nearest word to a vector: Each Word Vector exposes its full vector as a C buffer (array), ready to be feeded to a neural network: The framework contains some unit tests that show how to use it, see WordVectorTests.m.

Neural networks [10.4] : Natural language processing - word representations

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