Originally posted on searchenterpriseai.techtarget As companies realize the power of data, they’re tasked with finding data science practitioners with AI and ML skill sets to help them use the data to make better business decisions. Advanced analytics techniques, big data infrastructure and powerful algorithms are providing organizations with the ability to utilize their data for...
Originally posted on analyticsindiamag. When does a deployed Machine Learning model fail? There could be some good answers. One among them is when a model is deployed to make predictions on unforeseen data whose pattern is entirely different from training data. This issue is common in dynamic scenarios where non-stationary data streams in continuously. Continual...
Originally posted on thenextweb. In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning...
Originally posted on thenewstack. We have seen an explosion in developer tools and platforms related to machine learning and artificial intelligence during the last few years. From cloud-based cognitive APIs to libraries to frameworks to pre-trained models, developers make many choices to infuse AI into their applications. AI engineers and researchers choose a framework to...
Originally posted on analyticsindiamag. The job of compilers is to translate programming languages written by humans into binary executable by computer hardware. Compilers run on large complex, heterogeneous, non-deterministic, and constantly changing systems. Optimising compilers is difficult because the number of possible optimisations is huge. Designing heuristics that take all of these considerations into account ultimately becomes...
Originally posted on medium. Lockdown in most countries has been adopted in lieu of the pandemic. Perhaps this could be an ideal time to read through some of the best AI articles online. Perhaps staying home during COVID-19 is not at all a bad idea. Although everything has been impacted due to the pandemic, we...
Originally posted on Github Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not...
Tools to make ML easier Making Machine Learning easier is more possible than you think. Today we have a great variety of free open-source tools for any tastes and demands. Exploiting these tools will definitely help you to manage complex tasks and acquire new skills faster. Besides, you can always visit various online communities connected...
For Octave/MatLab version of this repository please check machine-learning-octave project. This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained. Each algorithm has interactive Jupyter Notebook demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions right in your browser. In most cases the explanations...
On Monday I went to the 2019 Turing talk, hosted by the British Computer Society (BCS) and the Institute of Engineering and Technology (IET). It’s a free talk that they organise every year. This year they invited Dr Krishna Gummadi to talk about bias in AI: Machine (data-driven learning-based algorithmic) decision making is increasingly being used to assist...
As a beginner in machine learning, it might be easy for anyone to get enough resources about all the algorithms for machine learning and deep learning but when I started to look for references to deploy ML model to production I did not find really any good resources which could help me to deploy my...
Machine learning is a branch of computer science that uses statistical methods to give computers the ability to self-improve without direct human supervision. Machine learning frameworks have changed the way web development companies utilize data. Machine learning algorithms can process large volumes of unstructured information, and turn them into actionable insights and predictions. Companies such as...
The plethora of knowledge involved in Machine Learning is the most fabulous thing about the subject. The theoretical and coding balance requires a steady and disciplined approach. In this five series tutorial, we saw CNNs, where we saw various approaches to different scenarios, and then worked on word embeddings, which was our gateway to Natural...
It’s not entirely clear what level of mathematics is necessary to get started in machine learning, especially for those who didn’t study math or statistics in school. In this piece, my goal is to suggest the mathematical background necessary to build products or conduct academic research in machine learning. These suggestions are derived from conversations...
The artificial intelligence (AI) industry has been leading the headlines consistently, and for good reason. It has already transformed industries across the globe, and companies are racing to understand how to integrate this emerging technology. Artificial intelligence is not a new concept. The technology has been with us for a long time, but what has...
The best way to learn data science is by doing it, and there’s no other alternative . From this post, I am going to reflect my learning on how I developed a machine learning model, which can classify movies reviews as positive or negative , and how I embed this model to a Python Flask web application. The...
