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Open Source Projects for Machine Learning Enthusiasts

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Open source refers to something people can modify and share because they are accessible to everyone. You can use the work in new ways, integrate it into a larger project, or find a new work based on the original. Open source promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. It helps you to write cleaner code. That can be of any choice.


8 Alternatives to TensorFlow Serving

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TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. Open-source platform Cortex makes execution of real-time inference at scale seamless. It is designed to deploy trained machine learning models directly as a web service in production.


CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing

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The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently. These successes, in conjunction with the proliferating mixed language interactions on social media have boosted interest in modeling code-mixed texts. The library consists of tools to develop and benchmark versatile model architectures that are tailored for mixed texts, methods to expand training sets, techniques to quantify mixing styles, and fine-tuned state-of-the-art models for 7 tasks in Hinglish. We believe this work has a potential to foster a distributed yet collaborative and sustainable ecosystem in an otherwise dispersed space of code-mixing research. The toolkit is designed to be simple, easily extensible, and resourceful to both researchers as well as practitioners.


Computer Vision with Python

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Welcome to the ultimate online course on Python for Computer Vision! This course is your best resource for learning how to use the Python programming language for Computer Vision. We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data. The most popular platforms in the world are generating never before seen amounts of image and video data. Now more than ever it's necessary for developers to gain the necessary skills to work with image and video data using computer vision.


NLP Basic Course For Beginner

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Welcome to the best Natural Language Processing course on the Udemy! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.


PyTorch Becomes Facebook's Default AI Framework

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Last week, Facebook said it would migrate all its AI systems to PyTorch. Facebook's AI models currently perform trillions of inference operations every day for the billions of people that use its technology. Its AI tools and frameworks help fast track research work at Facebook, educational institutions and businesses globally. Big tech companies including Google (TensorFlow) and Microsoft (ML.NET), have been betting big on open-source machine learning (ML) and artificial intelligence (AI) frameworks and libraries. Predominantly, Facebook has been using two distinct but synergistic frameworks for deep learning: PyTorch and Caffe2.


250+ Exercises - Data Science Bootcamp in Python

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The course consists of 250 exercises (exercises solutions) in data science with Python. This is a great test for people who are learning the Python language and are looking for new challenges. The course is designed for people who already have basic knowledge in Python and knowledge about data science libraries. Exercises are also a good test before the interview. Many popular topics were covered in this course.


Quantum Machine learning: data science's next big thing. – Quantum News

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Although topics like qubit scalability, error correction and the race to quantum supremacy highlight the current state of quantum computing, recently there has been a lot of discussion around use cases and applications of the available NISQ systems. The technology has reached a point of maturity where early adopters are looking into the possibility of squeezing out some quantum advantage, now or in the very near future. One direction that has been getting a lot of attention is quantum-machine learning or QML. This is not very surprising considering the massive strides made in machine learning over just the past few years. From breakthroughs in predicting protein folding, to deep fakes and the famous GTP3, these systems are sophisticated, impressively versatile and expected to, or already have, revolutionize many fields and industries.


Getting Started with Quantum Machine Learning

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Getting Started with Quantum Machine Learning The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix ... In this 2-hour long project-based course, you will learn basic principles of how machine learning can benefit from work, and how this can be implemented in Python using the Pennylane library by Xanadu. Quantum Computing represents a completely new paradigm in the computing realm, posed to revolutionize entire industries and bring amazing new innovations as they are used for purposes such as material design, pharmaceutical design, genetic and molecular simulations, and weather simulations. The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix multiplications and process massive amounts of data very quickly, and thus may represent a paradigm shift in AI and ML.


How TensorFlow Lite Fits In The TinyML Ecosystem

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TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems. In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers. "Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices." Most machine learning applications are resource-intensive, and expensive to deploy and maintain.