Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.

Considering how much important Artificial intelligence is, especially when it comes to transforming raw data, organizations are relying heavily on it. Artificial intelligence is one of those excellent ways to work smarter and not harder. On that note, have a look at top Google AI tools that everybody should learn in 2022. ML Kit is one of the best tools that mobile app creators can ask for. Storage, coding skills, etc. are something that need not be bothered about.

Google Colaboratory is a platform built on top of the Jupyter Notebook environment which runs entirely on Google Cloud Platform (GCP). This platform provides GPU which is free of cost and supports Python 2 and 3 versions. With the help of Colab, one can not only improve machine learning coding skills but also learn to develop deep learning applications. You can also learn to work with popular deep learning libraries such as Keras, TensorFlow, OpenCV and others. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

GPU acceleration is nowadays becoming more and more important. As a demonstration for this shift, an increasing number of online data science platforms is now adding GPU enabled solutions. Some examples are: Kaggle, Google Colaboratory, Microsoft Azure and Amazon Web Services (AWS). In this article, I will first introduce you to the NVIDIA open-source Python RAPIDS libraries and I will then offer you a practical demonstration of how RAPIDS can speed up Data Analysis up to 50 times. All the code used for this article is available on my GitHub and Google Colaboratory for you to play with.

AI and Machine Learning have already stormed the industry with interesting use cases. By the time we realized the immense uses for machine learning, we are hearing about deep learning. So, what is deep learning? Is it just more advanced machine learning or something else? Deep learning is a subset of Machine Learning that mimes the working of a human brain using neurons. With Deep Learning the focus is on building Artificial Neural Networks (ANN) using several hidden layers.

This article will cover a brief introduction to these topics and show how to implement them, using Google Colaboratory to do automated machine learning on the cloud in Python. Originally, all computing was done on a mainframe. You logged in via a terminal, and connected to a central machine where users simultaneously shared a single large computer. Then, along came microprocessors and the personal computer revolution and everyone got their own machine. Laptops and desktops work fine for routine tasks, but with the recent increase in size of datasets and computing power needed to run machine learning models, taking advantage of cloud resources is a necessity for data science.

Last year Google partnered with the Raspberry Pi Foundation to survey users on what would be most helpful in bringing Google's artificial intelligence and machine learning tools to the Raspberry Pi. Now those efforts are paying off. Thanks to Colaboratory – a new open-source project from Google – engineers, researchers, and makers can now build and run machine learning applications on a simple single-board computer. Google has officially opened up its machine learning and data science workflow – making learning about machine learning or data analytics as easy as using a notebook and a Raspberry Pi. Google's Colaboratory is a research and education tool that can easily be shared via Google's Chrome web browser.

There is always something new to learn on any fast-evolving topic, and machine learning is no exception. This post will point out 5 things to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten. Note that the title of this post is not "The 5 Most Important Things..." or "Top 5 Things..." to know about machine learning; it's just "5 Things." It's not authoritative or exhaustive, but rather a collection of 5 things that may be of use. It's fairly well-discussed that data preparation takes a disproportionate amount of time in a machine learning task.