Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

Posted on lun. 13 novembre 2017 in Deep Learning • Tagged with Deep learning, Convolutional Neural Networks, image classification, Keras, Tensorflow, AWS, GPU, Python, KaggleLeave a comment


Convolutional Neural Networks (CNNs) are nowadays the standard go-to technology when it comes to analyzing image data. These are special neural network architectures that perform extremely well on image classification. They are widely used in the computer vision industry and are shipped in different products: self driving cars, photo tagging systems, face detection security cameras, etc.
The theory behind convnets is beautiful. It attempts to explain and reverse-engineer the vision process. In this article, I'll go through it and explain what CNNs are all about. I'll try to go over the hype you see on the mass media and provide a detailed explanation with code snippets and interpretations.
This is also a hands-on guide to setup a deep learning dedicated machine on AWS and develop an end-to-end CNN model from scratch using Keras and Tensorflow.
By the end of this post you should have the global picture about CNNs: How do they work? and How to put them in practice?


Continue reading

Sentiment analysis on Twitter using word2vec and keras

Posted on jeu. 20 avril 2017 in NLP • Tagged with NLP, word2vec, doc2vec, deep learning, keras, neural network, TwitterLeave a comment


The focus of this post is sentiment analysis. This is a Natural Language Processing (NLP) application I find challenging but enjoyable. It aims at identifying emotional states, reactions and subjective information. It tries to determine the attitude of a speaker with respect to some topic.
If done automatically with high precision and on a large scale, sentiment analysis could be a goldmine for marketers or politicians who want to measure the public opinion through social networks.
In this post I'll show you how I built a machine learning model that classifies tweets with respect to their polarity. Tweets are short and yet capture lots of subjective information. That's why we'll be playing with them.
Some words for those who are ready to dive in the code: I'll be using python, gensim, the word2vec model and Keras.


Continue reading

How to mine newsfeed data and extract interactive insights in Python

Posted on mer. 15 mars 2017 in NLP • Tagged with Data science, Python, tf-idf, LDA, Kmeans, Newsapi.org, NLP, Topic mining, Text Clustering, BokehLeave a comment


In this tutorial we'll dive in Topic Mining. We'll analyze a dataset of newsfeed extracted from more than 60 sources. We'll show how to process it, analyze it and extract visual clusters from it. We'll be using great python tools for interactive visualization, topic mining and text analytics.
All the code is available to you to run and test. No bullshit.


Continue reading

How to score 0.8134 in Titanic Kaggle Challenge

Posted on mer. 10 août 2016 in Kaggle • Tagged with Kaggle, Titanic, Data science, Python, Solution, TutorialLeave a comment


The Titanic challenge on Kaggle is a competition in which the task is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat.
I have been playing with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0.8134 on the public leaderboard.
As I'm writing this post, I am ranked among the top 9% of all Kagglers: More than 4540 teams are currently competing.
This post is the opportunity to share my solution with you.


Continue reading

Welcome !

Posted on sam. 05 mars 2016 in Random, Data Science • Tagged with Machine Learning, Data Science, NLP, Dataviz, AnalyticsLeave a comment




Welcome to my blog!


My name is Ahmed. I'm a junior data scientist living in France. This blog is a personal project I'm embarking on to present some work I'm developing independently. It'll be about data analytics with a focus on machine learning.

In future posts, I'll write about some data science use-cases I'll make, some tools I enjoy using or some reading material I find worth sharing.

I do not pretend expertise. I'm writing about things I just learnt. So if you spot any mistake please don't hesitate to point it out.

Also if you have any recommendation that could improve this blog, I'm all ears and the comment section is yours.

Hope to see you around.


Continue reading