As a deep learning student, one of the most vital things you can do is to stay updated with the current developments in your field. To help you in that regard, we’ve prepared the following list of deep learning projects in Github as Github is the best place to start.
The following projects have the most stars and commit on Github at the time of writing this article. However, some of these projects weren’t in the top but are highly relevant because of their applicability. Let’s get started:
Top Deep Learning Projects in Github
At the time of writing this article, Keras is at the top of deep learning projects in Github. It has around 49,000 stars and 18.4 forks. Keras is a deep learning API, which runs on top of TensorFlow, a popular machine learning platform. Keras is written in Python and helps you in working on deep learning projects with much ease. It primarily helps you in conducting research fast and deploying faster.
As it is built on top of TensorFlow, it provides you with the advantages of that platform too. This means you can deploy your Keras models through any embedded device. It has excellent accessibility and has an industry-level framework to scale vast clusters of GPUs.
Learning about Keras is essential for any deep learning student. Make sure that you’re familiar with TensorFlow and its applications before you start learning about Keras because, without the former, the latter would be too difficult to master.
This Github project shares the most cited deep learning papers that were published between the years 2012 and 2016. It doesn’t have any articles published after 2016 because researchers and academics have released many documents since then, and it got too overwhelming for the project creators.
Even though its papers might seem outdated, you should give them a read because they are among the ‘classics’ of deep learning papers. Many articles present in this project have 200+ citations. Some of those papers have more than 800 citations so you can understand their popularity and applicability.
Caffe is an open framework for deep learning. It is a product of Berkeley AI Research/ The Berkeley Vision and Learning Center. While Caffe’s creators wanted it to focus on computer vision, it has become a general-purpose library for deep learning. Caffe has a thriving community of academic researchers as well as professional users so you can easily find help while working with it.
It is excellent for deploying convolutional networks and working on speech, vision, and other deep learning projects. It can process more than 60 million images in a day, so it’s undoubtedly a suitable tool for computer vision projects as well.
One of the most popular deep learning projects in Github, Machine Learning Notebooks is a project that helps you learn the basics of machine learning in Python. It has a sample code with solutions to exercises as well. It has multiple Jupyter notebooks that show you machine learning (and deep learning) fundamentals in Python through TensorFlow and Scikit-Learn.
Jupyter notebooks are interactive and help you try out the code within the notebook. They are a product of jupyter.org and are open source tools.
Most of the exercises present in this project are available in the book Hands-on Machine Learning with Scikit-Learn and TensorFlow. Before you begin learning from this project, make sure that you know about TensorFlow and Scikit-Learn. They are both widely popular tools among deep learning professionals, and you must be familiar with them before you begin working on this one.
You might find it a little challenging to work on some problems shared in this project, so it would be better to get a machine learning course and get a personalized learning experience.
MXNet is an open-source deep learning framework that helps you in performing research prototyping fast and flexibility for fast production. It allows scalable distributed training as it has Horovod and Parameter Server support. It is integrated into Python and supports multiple prominent languages such as C++, Julia, Clojure, Perl, R, and Scala.
Another reason for its vast popularity is its extensive ecosystem of libraries and tools for time series, natural language processing, computer vision, and others. Some of those tools and libraries are GluconTS, GluconCV, D2L.ai, and GluconNLP. All of these tools and libraries help you in specific domains of deep learning. For example, GluconCV is a toolkit for computer vision which allows you in pose estimation as well as to object detection. It has an Apache-2.0 license and is one of the must-haves for any deep learning professionals, so be sure to get familiar with it while studying the same.
Checkout: Top Machine Learning Projects in Github
fastai is a library for simplified and fast training of neural networks. It has out of the box support for tabular, vision, collaborative filtering, and text models. This is among the deep learning projects in Github because it offers tutorials and guides on using fast.ai as well.
Being familiar with prevalent libraries and frameworks will help you as deep learning professional. So you must be familiar with them. This project also has examples of fast.ai’s implementations and use cases so you can easily understand how to apply this library in real-world projects.
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for deep learning applications. You can combine different prominent model types and realize them quickly. Some of those model types include recurrent networks, feed-forward DNNs as well as CNNs. It applies SGD learning (stochastic gradient descent) with automatic parallelization and differentiation among servers.
It has an open-source license and one of the best communities. CNTK finds applications in many industries, and you might have to be familiar with it to work on some projects. So it would be best if you learn about it before you enter the market. The Microsoft Cognitive Toolkit gets constant updates too so you wouldn’t find any outdated tools or implementations in it.
Also Read: Top Python Projects in Github
Learn More About Deep Learning
Studying deep learning and related topics can take a lot of effort. We suggest you read more on this topic to understand deep learning with more effectiveness.
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