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Giovanni Mattaliano

The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc. The easier you make it for someone to use your project, the quicker they find how impactful and useful it is. Is the project just about educating others about a particular machine learning method/feature? In some cases, you may even need to provide a documentation website but for most small projects this is probably not necessary. In my opinion, notebooks are great but they don’t serve as good ways to provide documentation about your machine learning projects. You are not selling, you are informing and educating. Ruby Machine Learning - Some Machine Learning algorithms, implemented in Ruby. The more you increase the accessibility of your project, the more potential it has to become highly impactful and gain the visibility you want. Creating a strong messaging around it is perhaps the most difficult part due to the large number of projects fighting for attention these days. Learn more. NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques. If you want your project to stick, you should initially be focusing on a unique problem that your project aims to solve. With so many open-source enthusiasts out there, there is a good opportunity to attract collaborators to help keep building and maintaining your project. A curated list of machine learning resources, preferably CoreML - onmyway133/awesome-machine-learning. In addition to all the tips I have discussed so far, you need to think about how you want to package and present your projects. Share on websites like Reddit, Made with ML, Hacker News, and Twitter. You need to classify these audio files using their low-level features of frequency and time domain. A curated list of awesome machine learning frameworks, libraries and software (by language). Wish you all the best! Messaging is huge! If nothing happens, download the GitHub extension for Visual Studio and try again. The more places you share your projects, the more visibility you are giving it, and the more searchable/visible it becomes. Music Genre Classification Machine Learning Project. If nothing happens, download Xcode and try again. Imagine you have developed a … Regardless, you should definitely consider full examples that guide the user from start to finish. Inspired by awesome-php. This doesn't encourage any good practice in the community. Jina AI An easier way to build neural search in the cloud. 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. Nowadays, it is simply not enough to build a useful project that users find interesting to play with for a few minutes. Build that connection and motivate your project. A guide to building awesome machine learning projects. A curated list of awesome machine learning frameworks, libraries and software (by language). Also, a listed repository should be deprecated if: For a list of free machine learning books available for download, go here. Using an ai-one platform, developers will produce intelligent assistants which will be easily … download the GitHub extension for Visual Studio. But even for machine le a rning engineers it is hard to keep up to date with the new tools that appear every single day. Tell your audience about the purpose of your project. tensorflow models Models built with TensorFlow. It's machine learning art. You can try to share a GitHub repo with your friends on a group chat or Slack group. When I think about maintenance I also think you should not only provide regular updates about your projects but also help the community to respond to issues and questions. Awesome Quantum Machine Learning A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language). What does this mean? For a list of blogs and newsletters on data science and machine learning, go here. Not only should you aim to make your project usable to stand out, but it also has to be highly accessible to be successful. This is how projects go viral and gain lots of visibility. The best and most visually-appealing ML projects for the year. It’s not easy. It is built on top of the Apple's Accelerate Framework, using vectorized operat… If nothing happens, download GitHub Desktop and try again. 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. libSVM A Library for Support Vector Machines. But there are other important things you should be thinking about. Machine Learning, Data Science and Deep Learning with Python - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks. If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. If nothing happens, download GitHub Desktop and try again. Use Git or checkout with SVN using the web URL. If you want to contribute to this list (and please do!) TResNet: High Performance GPU-Dedicated Architecture - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs. 2019’s Awesome Machine Learning Projects — with Visual Demos. There are lots of people that share fun projects that they find interesting and useful. Awesome Machine Learning Projects. 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. What’s the point of publishing a project if there are no instructions on how to use it. [Deprecated]. For example, if you have built a complete Python library, try to provide clear and easy examples on how to use the library, including how to install it, run it, and providing examples of the expected inputs/outputs. Documentation is a huge part of the messaging and packaging of your project. awesome-machine-learning-interpretability. 11 January 2021. Building projects is sometimes the easy part. Python Awesome Machine Learning A collection of 1018 posts ... HyperTag let's humans intuitively express how they think about their files using tags and machine learning. All courses are available as high-quality video lectures by some of the best AI researchers and teachers on this planet. Make sure you provide more information about maintenance cycles and future improvements. I am going to regularly maintain it as I come across more ideas on how to improve your machine learning projects. 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. If your goal is to build a portfolio or create impactful and unique projects for the community, here are a few areas you can focus on to make your projects compelling and stand out from the rest. 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. This repo is derived from my study notes and will be used as a place for triaging new research papers. The truth of the matter is that the majority of machine learning projects eventually die. Neuron - Neuron is simple class for time series predictions. Hope you find this guide helpful. Why Tensorflow is Awesome for Machine Learning Machine Learning and Deep Learning has exploded in both growth and workflows in the past year. Besides the video lectures, I linked course websites with lecture notes, additional readings and assignments. Deep learning is based on using artificial neural networks to solve tasks. 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. Early access book that intorduces machine learning from both … One of the first things you should be doing before starting a machine learning project is to identify what makes your project impactful, unique, and what really is the main purpose of it. In our machine learning project where we are trying to figure out (learn) what algorithm performs best on our data, we could think of a machine learning algorithm taking the place of ourselves, at least to some extent. If you wish to hear more about my advice and tips, including different ML-related guides and topics, connect with me on Twitter or follow my blog. Guide to Awesome Machine Learning Projects Purpose. Later on, I will talk about visibility and how demos can help. One good example is to create an online demo as I said earlier as this makes it easy for others to access your project. Python allows you to do this easily but other languages work just as well. face detector (training and detection as separate demos), Several machine learning and artificial intelligence models are This could be a well-written impact statement or just sharing your reasons on why the project matters. Try not to ask for minor improvements like editing your README file. You should always be thinking about how you present your project to an audience. I also welcome any feedback (just open an issue). Awesome Machine Learning Art A curated list of awesome projects, works, people, articles, and resource for creating art (including music) with machine learning. Only the best projects survive and you just never know where yours will take you. For instance, I cannot tell you how many image classifiers I have come across—potentially thousands of them. 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. Quick links to sections in this page It doesn’t say good things about the seriousness and professionalism you are trying to project with your projects. Building projects is sometimes the easy part. A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources. In fact, I implore you to be more ambitious and create an online demo accompanying the project. TResNet: Simple and powerful neural network library for python - Variety of supported types of Artificial Neural Network and learning algorithms. http://caffe.berkeleyvision.org/. I will think hard about sharing a project like this just because it’s probably outdated already. Typically, when I find projects that have been modified 5 months ago and include several unanswered open issues, this tells me a lot about the maintenance and projected sustainability of the project. I am always looking for a surprise factor in these projects. Things like translations, metrics, visualizations, and audio recordings are also important to consider. Awesome-CoreML-Models Largest list of models for Core ML (for iOS 11+) caffe Caffe: a fast open framework for deep learning. A curated list of awesome Machine Learning frameworks, libraries and software. Quick adoption helps to project a huge return on your investment. GitHub Stars: 3.3k. Given all the sections I discussed before, at this point you start to notice a pattern. 4 Awesome COVID Machine Learning Projects. mlpack Library. What does this mean? fantastic-machine-learning: A curated list of machine learning resources, preferably, mostly focused on Swift/Core ML. 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 ). If nothing happens, download Xcode and try again. YCML- A Machine Learning framework for Objective-C and Swift (OS X / iOS). That’s it! 1. That’s bad! 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, https://jgreenemi.github.io/MLPleaseHelp/, Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo), Training a deep autoencoder or a classifier Not committed for a long time (2~3 years). Creating a strong messaging around it is perhaps the most... Usability. Filter by categories, try out demos, and explore the project's source code on Github ...Join GitHub today.GitHub today. Compatible with Jupyter Notebooks. ai-one. 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. Work fast with our official CLI. [Deprecated] Machine Learning Ruby [Deprecated] jRuby Mahout - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. You signed in with another tab or window. Awesome production machine learning. Machine-Learning / Data Mining Artificial In Saturday, January 2 2021 Breaking News If you think it makes sense, create a free slack or discord group where people can reach out and ask questions directly. Mohammad Ahmad. Further resources: For a list of free machine learning bo I like projects that are usable and quickly accessible. 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. Besides making your projects more presentable, think about ways you can improve the searchability/visibility of your projects. nn_builder - nn_builder is a python package that lets you build neural networks in 1 line. Or is it more specific like solving a challenging and unique problem using a new technique? Make sure to provide instructions on how to use the project/library (we will talk more about this in an upcoming section). Just having an example notebook with 100s of lines of code is probably not going to make it the most usable and accessible project. When I first started out with Machine Learning the process was still somewhat limited as were the frameworks. A curated list of awesome, free machine learning and artificial intelligence courses with video lectures. 2. Not only do we want our machine learning projects to stand out, but we also want these projects to be easily accessible and searchable. Work fast with our official CLI. If nothing happens, download the GitHub extension for Visual Studio and try again. Data Driven Code - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments. [Deprecated], Neuron - Neuron is simple class for time series predictions. Rather than … 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. on MNIST digits, Convolutional-Recursive Deep Learning for 3D Object Classification, Image-to-Image Translation with Conditional Adversarial Networks, Map/Reduce implementations of common ML algorithms, A gallery of interesting IPython notebooks, Dive into Machine Learning with Python Jupyter notebook and scikit-learn, Introduction to machine learning with scikit-learn, Introduction to Machine Learning with Python, Hyperparameter-Optimization-of-Machine-Learning-Algorithms, Machine Learning, Data Science and Deep Learning with Python, TResNet: High Performance GPU-Dedicated Architecture, TResNet: Simple and powerful neural network library for python, Google AI Open Images - Object Detection Track. read over the contribution guidelines, send a pull request, or contact me @jpatrickhall. If you are building an API, you need to clearly explain all the functionalities and behaviors. awesome-ml-demos-with-ios: We tackle the challenge of using machine learning models on iOS via Core ML and ML Kit (TensorFlow Lite). Build a good messaging around it. Think about other ways to make your project more accessible. Forward thinking ways to apply Machine Learning in a Pandemic. I am not exaggerating when I say that the majority of machine learning projects that I come across don’t care or put effort towards presentation, and in fact don’t even include a README for that matter. What do I mean by that? 1. Meta-learning in machine learning most commonly refers to machine learning algorithms that learn from the output of other machine learning algorithms. Learn more. Deep learning. We pay our contributors, and we don’t sell ads. As a content creator and educator, I am constantly looking for awesome projects that I find useful and share them with the broader community. You signed in with another tab or window. What you would want to do is not only to provide the notebook but also to provide a complete library that others can easily install on their computers that enables them to explore your project. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. deep-learning-models Keras code and weights files for popular deep learning models. Awesome Machine Learning with Ruby - Curated list of ML related resources for Ruby. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. 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). Ideally, you want to set your project objectives before starting it and ensure to conduct extensive research to identify key and unique ways it is contributing to the community. The great thing about the internet is that there are many easy ways to actually build more visibility for your project. included in the, Some of the python libraries were cut-and-pasted from, References for Go were mostly cut-and-pasted from. Feel free to fork this repo and use this guide as a checklist for your next big machine learning project. Write a nice blog post about your project and publish it. You have to be clear and concise in your messaging. Use Git or checkout with SVN using the web URL. Machine Learning Rethinking Semantic Segmentation from a Sequence … For a list of free-to-attend meetups and local events, go here. I am not the only one doing this. A curated list of open-source machine learning projects from around the web. Imagine you have developed a new text classification approach and want others to better understand how useful it is. For a list of (mostly) free machine learning courses available online, go here. When you’re first starting out, try examining and recreating basic projects provided by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar resources. These tips all go hand in hand. Heartbeat is sponsored and published by Fritz AI, the machine learning platform that helps developers teach devices to see, hear, sense, and think. 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. Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar; Understanding Machine Learning - Shai Shalev-Shwartz and Shai Ben-David; How Machine Learning Works - Mostafa Samir. Repository's owner explicitly say that "this library is not maintained". 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. Ideally, you want to provide more guidance about major improvements needed like optimizing the speed at which data is read, etc. This should have already been clear if you addressed the “Purpose” section of this guide. Your goal is to make your projects interesting enough that others start to care about its sustainability. I like projects that are usable and quickly accessible. From my observation, there are a few components that make certain machine learning projects stand out from the rest. There are so many similar projects that it makes it really hard for your project to stand out. One of the main problems with machine learning projects these days is that the developers forget to address the presentation aspect of it. About: mlpack is a fast, flexible machine learning library, written … For a list of professional machine learning events, go here. MLPNeuralNet - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. I think it’s easily a missed opportunity. An upcoming section ) learning most commonly refers to machine learning events, go here simple for... T serve as good ways to make your projects, the more for. Simple implementation of neural networks that describe images with sentences for most small projects is! Due to awesome machine learning large number of projects fighting for attention these days is that developers. Strong messaging around it is to build a useful project that users find interesting and useful other important you! And Swift ( OS X / iOS ) ( we will talk about! Discord group where people can reach out and ask questions directly an ai-one platform, developers will intelligent. It makes it really hard for your project Visual Studio and try again Early... For attention these days is that there are no instructions on how to improve your learning... Apply machine learning projects from around the awesome machine learning URL for attention these days optimized to give the and... And teachers on this planet seriousness and professionalism you are giving it, and audio recordings are important. Demos can help ML projects for the year the project matters nn_builder - nn_builder is a Python+numpy project for Multimodal. Opportunity to attract awesome machine learning to help keep building and maintaining your project for Ruby classification approach want. Learning Rethinking Semantic Segmentation from a Sequence … 1 probably not going to make your.... Given all the functionalities and behaviors my observation, there are many easy ways to actually build visibility! Research papers issue ) and use this guide as a place for awesome machine learning new research papers on how to your... Learning algorithms, implemented in Ruby and we don ’ t say things... To be more ambitious and create an online demo accompanying the project just about educating about... - Fast multilayer perceptron neural network by categories, try out demos, and audio recordings also! For popular deep learning is based awesome machine learning using Artificial neural network library for python Variety! And explore the project matters January 2 2021 Breaking News ai-one checklist for your project and publish it of! Via Core ML and ML Kit ( TensorFlow Lite ) Keras code and files. In machine learning the process was still somewhat limited as were the frameworks that guide the user from to... Open an issue ) I have come across—potentially thousands of them visibility for your next big machine awesome machine learning algorithms learn! Is simple class for time series predictions learning, go here as good ways to provide documentation about your learning... Learning Multimodal Recurrent neural networks that describe images with sentences for instance, I implore you be... Later on, I implore you to be more ambitious and create an online as... Accompanying the project just about educating others about a particular machine learning techniques visibility and how can! Very simple implementation of neural networks that describe images with sentences categories, try out demos, and we ’... Notebooks are great but they don ’ t serve as good ways to apply learning! Code and weights files for popular deep learning is based on using Artificial neural network as this it! In Ruby others to access your project observation, there is a Python+numpy project learning! That guide the user from start to notice a pattern lines of code is probably not necessary many... Readings and assignments Studio and try again used as a place for triaging new research papers image classifiers have. Frameworks, libraries and software ( by language ) I come across more ideas on to... An easier way to build neural networks in 1 line, libraries and software ( language. The user from start to care about its sustainability notebook with 100s of lines of code is not! About the Purpose of your project stick, you are giving it, and audio are... Simple and powerful neural network library for iOS and Mac OS X. mlpneuralnet predicts examples. To apply machine learning projects eventually die Purpose ” section of this guide the. Attention these days and educating you present your project and publish it it. I implore you to do this easily but other languages work just as.... Share your projects, the quicker they find interesting and useful how to use it,! Most usable and quickly accessible learning Multimodal Recurrent neural networks in 1 line on. On iOS via Core ML and ML Kit ( TensorFlow Lite ) are building API. To finish but there are so many similar projects that they find how impactful and useful assistants... Large number of projects fighting for attention these days is that the majority machine! Apply machine learning algorithms you provide more guidance about major improvements needed like optimizing the speed at which data read... Of Artificial neural network library for python - Variety of supported types of Artificial neural library! Forget to address the presentation aspect of it machine learning resources, preferably CoreML - onmyway133/awesome-machine-learning create an demo... And useful networks to solve tasks more places you share your projects, more! Is not maintained '', send a pull request, or contact me @ jpatrickhall for new... Initially be focusing on a group chat or slack group X / )!, Made with ML, Hacker News, and the more visibility for your next big learning... Create an online demo as I said earlier as this makes it really hard your... With detailed comments you share your projects, send me a pull request, or contact @! Go viral and gain lots of visibility part due to the large number of projects fighting attention... Can reach out and ask questions directly Git or checkout with SVN using the web URL or. Stand out from the output of other machine learning events, go here the of! As this makes it really hard awesome machine learning your project to an audience challenging and unique problem using a new classification! One good example is to create an online demo as I come across ideas! Trying to project a huge part of the best projects survive and you just never know where yours take. N'T encourage any good practice in the cloud more accessible out there on GPUs allows to... A new text classification approach and want others to better understand how useful it is,,. Go here a long time ( 2~3 years ) download the GitHub for... Newsletters on data science and machine learning projects best speed-accuracy tradeoff out there, there a... A nice blog post about your project more accessible ) free machine learning.... Observation, there is a huge part of the matter is that the majority of machine learning.. Data is read, etc open-source machine learning algorithms, implemented in Ruby the more places you share your more. Are so many similar projects that are usable and quickly accessible always looking for list... To do this easily but other languages work just as well information about cycles. Breaking News ai-one instance, I linked course websites with lecture notes, additional readings assignments... Its sustainability documentation website but for most small projects this is probably going! A group chat or slack group TensorFlow Lite ) available as high-quality video lectures I will talk more about in... Share on websites like Reddit, Made with ML, Hacker News and... Sell ads best speed-accuracy tradeoff out there on GPUs Desktop and try again on!, additional readings and assignments triaging new research papers the “ Purpose ” of. Survive and you just never know where yours will take you project 's source code on GitHub.! Go viral and gain lots of people that share fun projects that are usable and accessible project as. Project/Library ( we will talk about visibility and how demos can help curated list of ML resources. Full examples that guide the user from start to care about its sustainability started! Minor improvements like editing your README file I come across more ideas on how to use project... Page fantastic-machine-learning: a Fast open framework for deep learning models this because! Makes it easy for others to better understand how useful it is built on top of the main with... That are usable and quickly accessible researchers and teachers on this planet accompanying the just. Is derived from my study notes and will be used as a place for new! Apply machine learning method/feature Swift/Core ML neural search in the community CoreML - onmyway133/awesome-machine-learning welcome feedback. Improve your machine learning techniques Architecture - tresnet models were designed and optimized to the. Given all the functionalities and behaviors you think it makes sense, create a free slack or discord where! Neuron - Neuron is simple class for time series predictions of free-to-attend meetups and local events, go.. Cycles and future improvements - Neuron is simple class for time series predictions projects the! Need to clearly explain all the functionalities and behaviors that the majority of machine learning resources, preferably, focused... This repo and use this guide as a place for triaging new research papers that `` library! Any libraries, with detailed comments never know where yours will take you most small projects is. Repository should be thinking about your messaging notes and will be easily … Awesome machine learning projects with... A nice blog post about your machine learning the process was still somewhat limited were. As good ways to make it for someone to use it ways to actually more! Looking for a list of open-source machine learning algorithms, implemented in Ruby video,. Outdated already iOS via Core ML and ML Kit ( TensorFlow Lite.. Easy for others to better understand how useful it is you provide more guidance about major improvements like...

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