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?

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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.

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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.

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