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Data is the New Oil and Google DataSet Search is the Motherlode of Data –


Data is the new oil and now Google is providing Dataset Search. Datasets are needed to train Machine Learning and for other computer projects. Data is a vital resource for the modern age and now Datasets are easily published and discovered. Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets and find links to where the data is. You can now filter the results based on the types of dataset that you want (e.g., tables, images, text), or whether the dataset is available for free from the provider.

Neural architecture search


Neural architecture search (NAS)[1][2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures.[3][4] Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:[1] NAS is closely related to hyperparameter optimization and is a subfield of automated machine learning (AutoML).[5] Reinforcement learning (RL) can underpin a NAS search strategy. Zoph et al.[3] applied NAS with RL targeting the CIFAR-10 dataset and achieved a network architecture that rivals the best manually-designed architecture for accuracy, with an error rate of 3.65, 0.09 percent better and 1.05x faster than a related hand-designed model.

15 Machine Learning and Data Science Project Ideas with Datasets


In this article, we'll be discussing 15 machine learning and data science projects for beginners as well for intermediate level. Projects are some of the best investments of your time. You'll enjoy learning, stay motivated, and make faster progress. For machine learning or data science projects finding a dataset is a quite difficult task. And, to build accurate models, you need a huge amount of data.

Run image classification with Amazon SageMaker JumpStart


Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using SageMaker. These models can be quickly deployed and are pre-trained open-source models from PyTorch Hub and TensorFlow Hub. These models solve common ML tasks such as image classification, object detection, text classification, sentence pair classification, and question answering. The example notebooks show you how to use the 17 SageMaker built-in algorithms and other features of SageMaker.

How We Improved Data Discovery for Data Scientists at Spotify


Not only does this provide useful information to users in the moment, but it has also helped raise awareness and increase the adoption of Lexikon. Since launching the Lexikon Slack Bot, we've seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. You just listened to a track by a new artist on your Discover Weekly and you're hooked. You want to hear more and learn about the artist. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists.