A.I, Data and Software Engineering

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Recurrent neural network – time-series data- part 1

RNN block

If you are human and curious about your future, then the recurrent neural network (RNN) is definitely a tool to consider. Part 1 will demonstrate some simple RNNs using TensorFlow 2.0 and Keras functional API. What is RNN An RNN is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence (time series). This...

Advanced Keras – Custom loss functions

Petamind A.I

When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often...

Word2vec with TensorFlow 2.0 – a simple CBOW implementation

Petamind A.I

In TensorFlow website, there is a good example of word embedding implementation with Keras. Nevertheless, we are curious to see how it looks like when implementing word2vec with PURE TensorFlow 2.0. What is CBOW In the previous article, we introduced Word2vec (w2v) with Gensim library. Word2vec consists of two-layer neural networks that are trained to reconstruct linguistic contexts of words. The...

Save, restore, visualise Graph with TensorFlow v2.0 & KERAS

Petamind A.I

TensorFlow 2.0 is coming really soon. Therefore, we quickly show some useful features, i.e., save and load a pre-trained model, with v.2 syntax. To make it more intuitive, we will also visualise the graph of the neural network model. Benefits of saving a model Quick answer: to save time, easy-share, and fast deploy. A SavedModel contains a complete TensorFlow program, including weights and...

A.I, Data and Software Engineering

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