Machine learning is an expanding field that has the potential to transform many industries. GitHub hosts a wide range of machine-learning projects that are open-source. The GitHub repository is the.git/ directory within a project. This repository keeps track of all files modified in your project. It also records the building’s history.
Both academia and industry are hot topics for machine learning and deep learning. There are thousands upon thousands of GitHub repositories with source code, documentation, as well as other useful information that can be used to support a variety of projects. GitHub is simple to use, supports both private and public repositories, and is free for small-scale projects. Git allows you to manage multiple versions of source codes and then transfer them to Git files. GitHub can also be used for uploading Git repository copies.
Top 10 GitHub Repositories to Learn Machine Learning
Let’s take a look at the GitHub repositories that allow you to acquire technical and theoretical knowledge about Machine Learning.
TensorFlow is an open-source platform for machine learning. TensorFlow can manage all aspects of a machine-learning system. However, this class is focused on the development and training of machine learning models using a specific TensorFlow interface. TensorFlow APIs are organized in a hierarchical fashion, with higher-level APIs built upon lower-level APIs. Machine-learning researchers use low-level APIs to develop and test new machine-learning algorithms.
Also read: Top 10 AI And ML APIs for Developers
Scikit-learn is an open-source Python machine-learning library built on SciPy and NumPy. It is easy to use and compatible with many other libraries and frameworks. Scikit-learn can be used to help with classification, regression, clustering, and other tasks.
Google’s Keras API is a high-level, deep-learning API that can be used to implement neural networks. It’s written in Python and allows for easy implementation of neural networks. It allows you to calculate multiple backend neural networks. Keras is easy to use and learn because it offers a high-level Python frontend with the option of multiple back-ends for computation.
PyTorch is free and open-source machine learning (ML), framework. It was created using the Torch library and Python. Torch is an open-source ML library written using the Lua scripting language and used to create deep neural networks. It is a popular platform for deep learning research. This framework was designed to reduce the time it takes for research prototypes to be deployed.
SciPy is a popular Python program for Machine Learning enthusiasts. It includes modules for optimization and linear algebra as well as integration and statistics. The SciPy library is different from the SciPy stack. SciPy is an essential package that makes up the SciPy stack. SciPy can also manipulate images.
Theano is a Python library that allows us to quickly evaluate mathematical operations, such as multi-dimensional arrays. It is used mainly in the development and maintenance of Deep Learning Projects. It runs faster on a graphics processor unit (GPU) than it does on a CPU. Theano can handle large data loads at speeds that rival C implementations. Theano can use GPUs to perform better than C under certain conditions.
Caffe is a C++ Python-based open-source deep learning framework. It is fast and efficient and It is used frequently for feature extraction and image classification. Caffe is home to a large active developer and user community and it is often used with other libraries and frameworks such as TensorFlow or PyTorch.
Also read: Top 10 MLOps Tools for Machine Learning Lifecycle Management
Shogun is an open-source C++ machine-learning software library. It offers a variety of algorithms and data structures to solve machine-learning problems. SWIG is used to provide interfaces to Octave and Python, R, Java Lua, Ruby, C#, Java, Lua, Ruby, Lua, Ruby, Java, Lua, Ruby, and R.
TensorFlowTM is an open-source software program that performs numerical computations using data flow graphs. The graph’s edges represent mathematical operations. The graph’s edges are multidimensional data arrays (tensors), that are communicated between them. The adaptable architecture enables allows you to use one API to send computations to multiple CPUs or GPUs on a desktop, server, or mobile device.
CatBoost is a gradient-boosting algorithm that boosts decision trees. Yandex engineers and researchers created it. It is used to search, recommendation systems, and personal assistants. Weather prediction and many other tasks at Yandex as well as other companies like CERN, Cloudflare, and Careem taxi. It is open-source, and anyone can use it.