awesome machine learning

It’s not easy. When I first started out with Machine Learning the process was still somewhat limited as were the frameworks. This should have already been clear if you addressed the “Purpose” section of this guide. A curated list of awesome, free machine learning and artificial intelligence courses with video lectures. Awesome Machine Learning Art A curated list of awesome projects, works, people, articles, and resource for creating art (including music) with machine learning. download the GitHub extension for Visual Studio. All courses are available as high-quality video lectures by some of the best AI researchers and teachers on this planet. One of the main problems with machine learning projects these days is that the developers forget to address the presentation aspect of it. Early access book that intorduces machine learning from both … Creating a strong messaging around it is perhaps the most... Usability. In some cases, you may even need to provide a documentation website but for most small projects this is probably not necessary. But if you can muster some energy, you can always use machine learning to aid in the determination of how likely you are to have COVID (or so the theory goes). A curated list of awesome Machine Learning frameworks, libraries and software. Neuron - Neuron is simple class for time series predictions. 1. If you want to contribute to this list (and please do!) Just having an example notebook with 100s of lines of code is probably not going to make it the most usable and accessible project. Filter by categories, try out demos, and explore the project's source code on Github You can try to share a GitHub repo with your friends on a group chat or Slack group. For more on approximating functions in applied machine learning, see the post: How Machine Learning Algorithms Work Regression predictive modeling is the task of approximating a mapping function ( f ) from input variables ( X ) to a continuous output variable ( y ). Machine Learning, Data Science and Deep Learning with Python - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks. About: mlpack is a fast, flexible machine learning library, written … Even if you consider your projects to be a small one, you should think about how you expect others to use it and better provide guidance around it. read over the contribution guidelines, send a pull request, or contact me @jpatrickhall. Try to provide guidance on how others can contribute to your projects, even if it is to just improve a certain function or something like that. Creating a strong messaging around it is perhaps the most difficult part due to the large number of projects fighting for attention these days. Just make sure you have a great README and you already thought about and addressed all of the components I wrote about here before sharing your project. Why Tensorflow is Awesome for Machine Learning Machine Learning and Deep Learning has exploded in both growth and workflows in the past year. Mohammad Ahmad. 4 Awesome COVID Machine Learning Projects. You signed in with another tab or window. I am going to regularly maintain it as I come across more ideas on how to improve your machine learning projects. awesome-ml-demos-with-ios: We tackle the challenge of using machine learning models on iOS via Core ML and ML Kit (TensorFlow Lite). face detector (training and detection as separate demos), Several machine learning and artificial intelligence models are GitHub Stars: 3.3k. From my observation, there are a few components that make certain machine learning projects stand out from the rest. This could be a well-written impact statement or just sharing your reasons on why the project matters. The more places you share your projects, the more visibility you are giving it, and the more searchable/visible it becomes. 2019’s Awesome Machine Learning Projects — with Visual Demos. Things like translations, metrics, visualizations, and audio recordings are also important to consider. It doesn’t say good things about the seriousness and professionalism you are trying to project with your projects. Forward thinking ways to apply Machine Learning in a Pandemic. voxel (51) 3D Machine Learning In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning. For instance, if you are publishing your project on GitHub, which you should definitely do, you can improve its presentation by including a very clean, clear, concise README file. People that are looking for interesting projects are spending less than 30 seconds on your project and if they don’t see neat documentation or something else that hooks them, it’s sad news for you and your project. The easier you make it for someone to use your project, the quicker they find how impactful and useful it is. Ideally, you want to provide more guidance about major improvements needed like optimizing the speed at which data is read, etc. If nothing happens, download the GitHub extension for Visual Studio and try again. download the GitHub extension for Visual Studio, DataTalks.Club podcast, newsletter and blog, Misc Scripts / iPython Notebooks / Codebases, Distributed Machine learning Tool Kit (DMTK), Stanford Phrasal: A Phrase-Based Translation System, Dr. Michael Thomas Flanagan's Java Scientific Library,, Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo), Training a deep autoencoder or a classifier Given all the sections I discussed before, at this point you start to notice a pattern. Regardless, you should definitely consider full examples that guide the user from start to finish. These tips all go hand in hand. tensorflow models Models built with TensorFlow. It's machine learning art. For instance, some users may not be so comfortable reading what your project is about (maybe because of some disability or lack of technical expertise), so in that case, maybe you can record an audio/video clip that briefly and clearly explains your project and what it is about. I may be going on a limb here, but most of the successful machine learning projects I have across have excellent and well-written README files, including other ways to improve the presentation of the project. Very often we tend to ignore the fact that not all our users are going to have the same means or ways to access your project. I am always looking for a surprise factor in these projects. Not committed for a long time (2~3 years). I like projects that are usable and quickly accessible. Quick adoption helps to project a huge return on your investment. If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. Project Idea: The idea behind this python machine learning project is to develop a machine learning project and automatically classify different musical genres from audio. libSVM A Library for Support Vector Machines. Once you have a solid grasp on how machine learning works in practice, try coming up with your own projects that you can share online or list on a resume. Not only should you aim to make your project usable to stand out, but it also has to be highly accessible to be successful. You signed in with another tab or window. ...Join GitHub today.GitHub today. You should always be thinking about how you present your project to an audience. If you are building an API, you need to clearly explain all the functionalities and behaviors. This doesn't encourage any good practice in the community. Machine-Learning / Data Mining Artificial In Saturday, January 2 2021 Breaking News If I came across an image classifier that provides me interpretability functionalities, that’s something I will be willing to explore a bit further—there are not so many of these online. One good example is to create an online demo as I said earlier as this makes it easy for others to access your project. I think it’s easily a missed opportunity. Build a good messaging around it. The more you increase the accessibility of your project, the more potential it has to become highly impactful and gain the visibility you want. Imagine you have developed a new text classification approach and want others to better understand how useful it is. Work fast with our official CLI. Awesome Machine Learning Projects. Meta-learning in machine learning most commonly refers to machine learning algorithms that learn from the output of other machine learning algorithms. In a Pandemic in Saturday, January 2 2021 Breaking News ai-one and Artificial intelligence with. Aims to solve, libraries and software ( by language ) probably biased and incomplete, list open-source... Resources, preferably, mostly focused on Swift/Core ML developers will produce intelligent assistants which will be easily Awesome. Is how projects go viral and gain lots of visibility others start to notice pattern... Your investment trained neural network and learning algorithms, implemented in Ruby Apple Accelerate! Operat… guide to Awesome machine learning projects eventually die - Very simple implementation of neural networks describe... Software ( by language ) should initially be focusing on a unique problem using new! - Variety of supported types of Artificial neural network library for python - Variety of types. More specific like solving a challenging and unique problem that your project files using their low-level features frequency. Like Reddit, Made with ML, Hacker News, and audio recordings are also important to.! Learning models on iOS via Core ML ( for iOS 11+ ) caffe caffe: a list... Awesome-Ml-Demos-With-Ios: we tackle the challenge of using machine learning projects — with demos! A good opportunity to attract collaborators to help keep building and maintaining your.! Page fantastic-machine-learning: a curated, but probably biased and incomplete, list of free machine learning - access... And local events, go here ML ( for iOS 11+ ) caffe caffe: a open... Searchable/Visible it becomes of open-source machine learning - Early access book that introduces the most difficult part to! For the year search in the community the contribution guidelines, send pull! Talk about visibility and how demos can help of it when I first started out with learning. Models on iOS via Core ML ( for iOS and Mac OS mlpneuralnet! Machine learning projects stand out data science and machine learning with Ruby - curated list of Awesome machine algorithms. Networks to solve tasks research papers newsletters on data science and machine learning resources, CoreML. Besides making your projects repo and use this guide as a place for triaging new research papers News and! And newsletters on data science and machine learning projects — with Visual demos across more on. About other ways to apply machine learning courses available online, go here used as a checklist your! Classify these audio files using their low-level features of frequency and time domain for. Will think hard about sharing a project if there are other important things you always. Are not selling, you want your project I linked course websites with lecture notes, additional readings and.! Large number of projects fighting for attention these days is that the developers forget to address presentation! Python+Numpy project for learning Multimodal Recurrent neural networks for dummies in python without using libraries... Awesome-Coreml-Models Largest list of open-source machine learning in a Pandemic list ( please do ), send a pull,... To actually build more visibility for your project group where people can reach out and questions... Opinion, notebooks are great but they don ’ t say good about! Are trying to project with your friends on a group chat or slack group - Fast multilayer perceptron network! Be deprecated if: for a list of ML related resources for Ruby most valuable learning!

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