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Articles by Axel Thevenot
Optimization & Eye Pleasure: 78 Benchmark Test Functions for Single Objective Optimization
If you only are here for eye pleasure you can go to the Benchmark part. 😜 I was looking for a benchmark of test functions to challenge a single objective optimization. I found two great websites with MATLAB and R implementations you can find on the sources. Yet I wanted to have this implementation in python. So I implemented these 78 functions in python in an homogeneous way to provide you an easy manner of working with them.
Predict Gold Prices With Scikit-Learn.
Advanced techniques with simple Python code By the end of this story you will understand how to use the popular Python library Scikit-Learn to predict gold prices. We will compare a linear regression model with a train / test split to a model trained using a rolling out-of-sample dataset. TABLE OF CONTENTS: Use the Pandas library to read our csv dataset. Import ‘Linear Regression’ from Scikit-Learn. Create ‘Return’ and ‘Lagged Return’ columns. Plot USD value of Gold.
Image Augmentation Mastering: 15+ Techniques and Useful Functions with Python Codes
Maybe at this point, you don’t see how simple the setup is. And yet it is. All we have to do is define a list of the transformations we want to do on our sample and that’s it. We do not touch anything else afterward. Note that the order of the transformations will have its importance. It’s up to you. We can now dive into the purpose of the article and see the image augmentation techniques.
12 Main Dropout Methods: Mathematical and Visual Explanation for DNNs, CNNs, and RNNs
Deep Dive into DNNs, CNNs, and RNNs Dropout Methods for Regularization, Monte Carlo Uncertainty, and Model Compression One of the major challenges when training a model in (Deep) Machine Learning is co-adaptation. This means that the neurons are very dependent on each other. They influence each other considerably and are not independent enough regarding their inputs. It is also common to find cases where some neurons have a predictive capacity that is more significant than others.
Overfitting and Underfitting: Visually Explained Like You’re Five
Everything we need to know about Detection and Prevention of Overfitting and Underfitting in Machine Learning and Deep Learning Sources: [1] [2] [3] [4] [5] — The Noun Project Every week, a bunch of flowers grows in my garden. You can think of it as a dataset. It was a good way to playfully introduce my child to complex concepts. In this article we will highlight through this flowery garden: the importance of understanding your dataset.
Build Your Fully Configurable Interface to Create MNIST-like Dataset with Python in 10 Minutes
Step by step user-friendly python interface to write a dataset from a JSON configuration file (with code) For a personal project and maybe a paper (fingers crossed), I need to create a MNIST-like dataset. I thought it could be useful for others. I will, therefore, explain how I coded this interface with Python. (github repository) The purpose of this article is to be able to design its own interface as flexible and fast as possible.
When Science and Philosophy meet Randomness, Determinism, and Chaos
What is the theory behind Randomness? Does that really exist? Source: Pxhere Sometimes a source of fear, sometimes a source of dreams. Randomness is in everyone’s mind a source of emotion. As a programmer, it fascinates me. If you have ever tried your luck at the casino, it’s because you are probably thrilled by it. Unlucky randomness can also make you cry or make you live the most difficult time of your life. “The world is governed by chance. Randomness stalks us every day of our lives.” Paul Auster.
Can super-parrots ever achieve language understanding?
By David Ferrucci — AI scientist and entrepreneur. Founder and CEO of Elemental Cognition. Parrot image credit: Teguh Mujiono/Shutterstock Computers that claim to “understand” language still lack world models — faithful representations of how objects and events relate to each other. As I established in my last post, you can reliably trip up today’s systems by asking about when events occur, where things are located, and why it’s all happening.
Understand and Visualize Color Spaces to Improve Your Machine Learning and Deep Learning Models
Explain, analyze and experiment 14 popular color spaces and their consequences on the accuracy of our models. “Why do we use the RGB color space as a standard in our training models? Sure, it’s the simplest color space because it’s the default color space. But are there other color spaces that may be more suitable? And can it improve our models?” These questions came to my mind and I absolutely had to find the answers. So I investigated and did some experiments.
Regular Expressions (RegEx): Plainly Explained with Cheat Sheet to Appreciate Them
Regular expressions (or regex) are tools used to represent string patterns. They are used to detect, examine, modify, manipulate strings. Basically, if you want to find all the proper names in a text, you can use a regex. We know that a name contains only letters and starts with an uppercase letter. Regex allows us to transcribe the natural human phrase into a computer representation. Experienced with Python or Java and even Excel, they can be very useful for engineers, marketers or anyone else.
Conv2d: Finally Understand What Happens in the Forward Pass
A visual and mathematical explanation of the 2D convolution layer and its arguments Deep Learning’s libraries and platforms such as Tensorflow, Keras, Pytorch, Caffe or Theano help us in our daily lives so that every day new applications make us think “Wow!”. We all have our favorite framework, but what they all have in common is that they make things easy for us with functions that are easy to use that can be configured as needed.
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