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This page contains interesting papers we collected throught the Web and are used for our coaching preparations and our specific consultancy activities. Download them for free and read them where you want!!!
This paper is a cheat sheet talking about Keras. Keras is a powerful and easy-to-use deep learning library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
This paper is a cheat sheet talking about Scikit-learn. Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.
This paper is a cheat sheet talking about SciPy. The SciPy library is one of the core packages for scientific computing that provides mathematical algorithms and convenience functions built on the NumPy extension of Python
Linear regression is one of the fundamental statistical and machine learning technique. This paper talk about linear regression with Python: definition, packages, examples, how to implement
This paper, written by Afshine Amidi & Shervine Amidi from Massachusetts Institute of Technology, is a short python summary on some useful fonctionnality applied to data manipulation: file management, filtering, datetime conversion, concatenation, aggregation and windows function
This pdf is a Machine Learning tutorial (167p) created by Tutorials Point and published on LinkedIn. In this book you will learn the basics of Python and Pandas and the main ML algorithms (Regression, Decision tree, clustering, and so on). The book provides examples and explain step by step why they are using the codes.
This pdf is a turorial (150p) published by packt and found on LinkedIn. If you read this e-book you will learn how to use SciPy for linear algebra, numerical analysis, signal processing, data mining and computational geometry. You need a good mathematical background and couple of coffee mugs.
This pdf explain, in short, the theory behind each ML algorithms. They provide the definition, mathematical formula , graphics, and so on. It's not orianted to a specific programming language, just theory. Very useful to use before an interview or an exam or just to remember how it works.
K-nearest neighbors is a supervised classification and regression machine learning algorithm. This e-book of 24 pages explains you the theory behind the algorithm, discuss how it works, pros and cons and make the difference beween Manhattan, Euclidian, Minkowski distance
This 250 p pdf is an e-book edited by Manning and found on LinkedIn. They EXPLAIN basic and advanced ML algorithms with Python and provide a practical workflow and real life examples (eg: tipping behavior of NYC taxis, employee business rules, and so on). This books also explain topics on NLP, image and time series analysis.
This 137 p e-book found on LinkedIn provides you an easy to read summary on statistical concepts and tests like Normal curve, Z test, T test, Paired T test and discuss what we have to do with unequal variance.
This pdf is a cheat sheet summary in 5 pages on the main ML algorithms. For each of them, there are explanation, graphical representation and some formula. Well done !
TensorFlow is an open-source software library for high performance numerical computation. This pdf is a cheat sheet
This e-book is the official Python Data Science Handbook of Oreilly. I found it on linkedin. It explain Numpy, Data manipulation with Pandas, Visualization with Matplotlib and some ML algorithms