Join Simple Machines and be part of our mission to help clients unlock the full potential of their data through the creation of cutting-edge business driven data platforms and software solutions. As we continue to expand our team across Australia and New Zealand, we are seeking Data Engineers who are unafraid to challenge the status quo and push boundaries. We are looking for a consultant-minded individual who is passionate about working closely with clients to solve complex problems and lead them on a journey from discovery to technical implementation. You have a knack for understanding client needs and delivering market-leading solutions. Simple Machines is not your average consultancy or software engineering firm.

Linear regression is a simple machine learning model and chances are you are already aware of it! Do you remember plotting the line y mx c in your introductory algebra class? This is an equation of a straight line where m is its gradient and c is the point where the line crosses the y-axis. Using this equation, you're able to estimate the value of y for any given value of x. Similarly, linear regression involves estimating the relationship between independent variables (x) and a dependent variable(y).

The idea of this article is to do a quick and easy build of a Docker container with a simple machine learning model and run it. Before reading this article, do not hesitate to read Why use Docker for Machine Learning and Quick Install and First Use of Docker. In order to start building a Docker container for a machine learning model, let's consider three files: Dockerfile, train.py, You can find all files on GitHub. The train.py is a python script that ingest and normalize EEG data in a csv file (train.csv)

Neural networks are powerful models that solve a variety of complex real-world problems. However, the stochastic nature of training and large number of parameters in a typical neural model makes them difficult to evaluate via inspection. Research shows this opacity can hide latent undesirable behavior, be it from poorly representative training data or via malicious intent to subvert the behavior of the network, and that this behavior is difficult to detect via traditional indirect evaluation criteria such as loss. Therefore, it is time to explore direct ways to evaluate a trained neural model via its structure and weights. In this paper we present MLDS, a new dataset consisting of thousands of trained neural networks with carefully controlled parameters and generated via a global volunteer-based distributed computing platform. This dataset enables new insights into both model-to-model and model-to-training-data relationships. We use this dataset to show clustering of models in weight-space with identical training data and meaningful divergence in weight-space with even a small change to the training data, suggesting that weight-space analysis is a viable and effective alternative to loss for evaluating neural networks.

Learn to develop a multivariate linear regression for any number of variables in Python from scratch. Linear regression is probably the most simple machine learning algorithm. It is very good for starters because it uses simple formulas. So, it is good for learning machine-learning concepts. In this article, I will try to explain the multivariate linear regression step by step.

In the seminal paper on AI, titled Computing Machinery and Intelligence, Alan Turing famously asked: "Can machines think?" -- or, more accurately, can machines successfully imitate thought? Turing clarifies that he's interested in machines that "are intended to carry out any operations which could be done by a human computer." In other words, he's interested in complex digital machines. Since the achievement of a thinking digital machine is a matter of the evolution of machines, it reasons to start at the beginning of machine history. A machine is a device that does work.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. At some point, every one of us has had the feeling that online applications like YouTube and Amazon and Spotify seem to know us better than ourselves, recommending content that we like even before we say it. At the heart of these platforms' success are artificial intelligence algorithms--or more precisely, machine learning models--that can find intricate patterns in huge sets of data. Corporations in different sectors leverage the power of machine learning along with the availability of big data and compute resources to bring remarkable enhancement to all sorts of operations, including content recommendation, inventory management, sales forecasting, and fraud detection. Yet, despite their seemingly magical behavior, current AI algorithms are very efficient statistical engines that can predict outcomes as long as they don't deviate too much from the norm.

When working on a Machine learning project flexibility and reusability are very important to make your life easier while developing the solution. Find the best way to structure your project files can be difficult when you are a beginner or when the project becomes big. Sometime you may end up duplicate or rewrite some part of your project which is not professional as a Data Scientist or Machine learning Engineer. A quick example is when running different Machine Learning experiments to find the best model for the problem you are trying to solve, most of the time people tend to change the values of the different parameters directly from the source code and run the experiment again and again. They repeat this process until they get the best results.

Build Machine Learning Model in Python In this video, I will show you how to build a simple machine learning model in Python. Particularly, we will be using the scikit-learn package in Python to build a simple classification model (for classifying Iris flowers) using the random forest algorithm. Build Machine Learning Model in Python In this video, I will show you how to build a simple machine learning model in Python. Particularly, we will be using the scikit-learn package in Python to build a simple classification model (for classifying Iris flowers) using the random forest algorithm.

Two robotic arms face two closed doors. Both reach forward and miss the door handles entirely. So they reach again, and this time, they hit the handles head-on, rattling the door frames. Finally, they grab the handles cleanly and pull the doors open, and after a few more hours of trial and error, they can repeat the trick every time. The two robots are somewhere inside Google, and though other machines have long been agile enough to pull a door handle, these are different: They learned to open those doors largely on their own.