A Study on CoVe, Context2Vec, ELMo, ULMFiT and BERT

I did some research on some of the revolutionary models that had a very powerful impact on Natural Language Processing (NLP) and Natural Language Understanding (NLU) and some of its challenging tasks including Question Answering, Sentiment Analysis, and Text Entailment. These models aim to have a better understanding of the language using the Transfer Learning …

An End-to-End Note About FCIS Graduation Project(GP)

This post is inspired by a recent post that is written by my friend Mustafa Saad. It is a great post that I recommend you to read and think about the topics and suggested projects that are mentioned on his post. Actually, I don't completely agree with him, but Mustafa is an experienced guy whose ideas …

[Kaggle] SMS Spam Collection

I've just made some exploration on a dataset provided by Kaggle for SMS Spams Detection. I've managed to get a loss of 0.1 on the testing set and approx. accuracy of 95%. You can check my exploration and classification source code on the Python Notebook https://github.com/AhmedHani/Kaggle-Machine-Learning-Competitions/blob/master/Dataset%20Exploration/SMS%20Spam%20Collection%20Dataset/sms_spam_detection.ipynb

Feedback Sequence-to-Sequence Model – Gonna Reverse Them All!

This tutorial assumes that you have a pretty good understanding about the basics of Recursive Neural Networks and Backpropagation Through Time (BPTT) and how these models actually work Terminologies One-to-one: Problems that are concerned into getting a direct relation between an input word and output word. For example, the relation (like, love) is considered to …

[Thesis Tutorials II] Understanding Word2vec for Word Embedding II

Previously, we talked about Word2vec model and its Skip-gram and Continuous Bag of Words (CBOW) neural networks. Regularly, when we train any of Word2vec models, we need huge size of data. The number of words in the corpus could be millions, as you know, we want from Word2vec to build vectors representation to the words …

[Thesis Tutorials I] Understanding Word2vec for Word Embedding I

Vector Space Models (VSMs): A conceptual term in Natural Language Processing. It represents words as set of vectors, these vectors are considered to be the new identifiers of the words. These vectors are used in the mathematical and statistical models for classification and regression tasks. Also, they shall be unique to be able to distinguish between …

Hello, Valor!

Being a researcher and a programmer can make the life so boring. Actually, I have been feeling boring for 2 months. My life is full of same routine processes, going to work, back to home and study, or code some freelancing tasks, waiting Thursday to meet my friends and hangout. This routine really made my …

Generative Adversarial Networks [2] – Camouflage your Predator!

You can imagine the situation by learning without any prior knowledge about something, you just do mistakes and you let others correct them to you. Continuing this process will lead you to learn how to do that thing, although you may still don't know what are you doing?! This was a comment from my professional …

Overview: Generative Adversarial Networks – When Deep Learning Meets Game Theory

Before going into the main topic of this article, which is about a new neural network model architecture called Generative Adversarial Networks (GANs), we need to illustrate some definitions and models in Machine Learning and Artificial Intelligence in general. Discriminative Models: Models that predict a hidden observation (called class) given some evidence (called features). In …

Another LSTM Tutorial

The figures are taken from this great blog post by Christopher Olah Recurrent Neural Networks Recurrent Neural Networks (RNN) is a type of Neural Networks (NN) that is commonly used in problems that depend on sequential data. In sequential data, we should assume that the data is dependent to each other. For example, if we have …