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Most popular programming language frameworks and tools for machine learning

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If you're wondering which of the growing suite of programming language libraries and tools are a good choice for implementing machine-learning models then help is at hand. More than 1,300 people mainly working in the tech, finance and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey. The list is a mix of software frameworks and libraries for data science favorite Python, big data platforms, and cloud-based services that handle each stage of the machine-learning pipeline. Most firms are still at the evaluation stage when it comes to using machine learning, or AI as the report refers to it, and the most common tools being implemented were those for'model visualization' and'automated model search and hyperparameter tuning'. Unsurprisingly, the most common form of ML being used was supervised learning, where a machine-learning model is trained using large amounts of labelled data.


GitHub: The top 10 programming languages for machine learning

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While you might think that machine learning is reserved for developers well-versed in languages like R and Python, you'd be wrong. Online code repository GitHub has pulled together the 10 most popular programming languages used for machine learning hosted on its service, and, while Python tops the list, there's a few surprises on there. Web-scripting language turned jack-of-all trades JavaScript finds its way to number three on the list, data science-focused newcomer and Python rival Julia makes number six, Shell scripts are bundled together at number seven, and big-data favorite Scala is at number 10. The rankings are based on the primary languages used in code repositories tagged as related to machine-learning, according to GitHub. They almost certainly don't reflect which languages are most commonly used for machine learning.


13 frameworks for mastering machine learning

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These open source tools do the heavy lifting for you By Serdar Yegulalp, Senior Writer, InfoWorld Aug 16, 2017 W.Rebel via Wikimedia 13 frameworks for mastering machine learning Over the past year, machine learning has gone mainstream with a bang. The "sudden" arrival of machine learning isn't fueled by cheap cloud environments and ever more powerful GPU hardware alone. It is also due to an explosion of open source frameworks designed to abstract away the hardest parts of machine learning and make its techniques available to a broad class of developers. Here is a baker's dozen of machine learning frameworks, either freshly minted or newly revised within the past year. These tools caught our attention for their provenance, for bringing a novel simplicity to their problem domain, for addressing a specific challenge associated with machine learning, or for all of the above.


Google and Udacity launch free course to help you master machine learning

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Google and online learning hub Udacity have launched a free course designed to make it simpler for software developers to grasp the fundamentals of machine learning. The "Intro to TensorFlow for Deep Learning" course is designed to be more accessible to developers than previous machine-learning courses offered by Udacity. "Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math," says Mat Leonard, head of the School of AI at Udacity. "If you can code, you can build AI with TensorFlow. You'll get hands-on experience using TensorFlow to implement state-of-the-art image classifiers and other deep learning models. You'll also learn how to deploy your models to various environments including browsers, phones, and the cloud."


A bot lingua franca does not exist: Your machine-learning options for walking the talk

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So, you want to create a hugely successful machine-learning startup? Or you've been asked to start investigating ML for your firm? Well, you'd better get programming – but what language should you use? No languages have been designed specifically with ML in mind, but some do lend themselves to the task. Developers experimenting with machine learning will spend most of their time processing data sets, running them against a machine-learning algorithm, and then classifying them again until the results seem right.