Training Optimization So by now we’ve learned how to build a deep neural network and how to train it to fit our data. Sometimes however, we go out there and train on ourselves and find out that nothing works as ...
Read moreThe 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) spots out some major trends in NLP. This post focus on one exciting and clever paper that motivate the more careful usage of cosine similarity in the word ...
Read moreIntroduction A hyper parameter is a variable that we need to set before applying a learning algorithm into a dataset. The challenge with hyper parameters is that there are no magic numbers that work everywhere. The best numbers depend on ...
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Introduction In this project, you’ll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, you’ll have a much better understanding of gradient …
Gradient Descent with Squared Errors We want to find the weights for our neural networks. Let’s start by thinking about the goal. The network needs to make predictions as close as possible to the real values. To measure this, we …
Introduction So let’s start with two questions, what is deep learning, and what is it used for? The answer to the second question is pretty much everywhere. Recent applications include things such as beating humans in games such as Go, …
What is a neural network? The term, Deep Learning, refers to training Neural Networks, sometimes very large Neural Networks. So what exactly is a Neural Network? In this video, let’s try to give you some of the basic intuitions. Let’s start to the …
Introduction Distributional semantics is a subfield of natural language processing predicated on the idea that word meaning is derived from its usage. The distributional hypothesis states that words used in similar contexts have similar meanings. That is, if two words …
Introduction Last few decades have witnessed substantial breakthroughs on several areas of speech and language understanding research, specifically for building human to machine conversational dialog systems. Dialog systems, also known as interactive conversational agents, virtual agents or sometimes chatbots, are …
Introduction automatic text summarization is the process of shortening a text while keeping the main content and ideas of the documents. With the large amount of text published online such as product’s reviews and the quick capture of these document’s …