TensorFlow and deep learning are things that corporations must now embrace. The coming flood of audio, video, and image data and their applications are key to success.
If you've ever tried to hire anyone, you know how difficult it can be to pour through hundreds of resumes and find the right one. AI can take the pain out of the process!
See how to get started with writing stream processing algorithms using Apache Flink. by reading a stream of Wikipedia edits and getting some meaningful data out of it.
If you've been following software development news recently you probably heard about the new project called Apache Flink. I've already written about it a bit...
We've seen an explosion of interest in machine learning in the past few years. But where did machine learning come from and why is there so much interest in it now?
If you have often wondered to yourself about the difference between machine learning and deep learning, read on to get a detailed comparison in simple layman language.
No more coding for different models, noting down the results, and selecting the best model — AutoML is going to do all of these for you while you brew a cuppa!
Learn about the different cluster management modes that you can run in your Spark application - standalone, Mesos, Yarn, and Kubernetes - and how to manage them.
GPUs can accelerate the training of machine learning models. In this post, explore the setup of a GPU-enabled AWS instance to train a neural network in TensorFlow.
Many articles define decision trees, clustering, and linear regression, as well as the differences between them — but they often neglect to discuss where to use them.
If you're looking to start an AI project but don't know where to start, check out this article. We've listed the top 12 AI tools, libraries, and platforms, what they are typically used for, what pros and cons they come with, and more!
The goal of someone learning ML should be to use it to improve everyday tasks—whether work-related or personal. To do this, it's important to first understand algorithms.
How did Spark become so efficient in data processing compared to MapReduce? Learn about Spark's powerful stack of libraries and big data processing functionalities.
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