KNN (K-Nearest Neighbors) is Dead! | by Marie Stephen Leo | Towards AI | Dec, 2020 | Medium

KNN (K-Nearest Neighbors) is Dead! | by Marie Stephen Leo | Towards AI | Dec, 2020 | Medium

Learning how to apply some of the algorithms mentioned in this article would likely improve students’ and teachers’ ability to locate CALI resources and allow us to build a useful recommender system.

AWS Launches New Deep Learning AMIs for Machine Learning Practitioners

The Conda-based AMI comes pre-installed with Python environments for deep learning created using Conda. Each Conda-based Python environment is configured to include the official pip package of a popular deep learning framework, and its dependencies. Think of it as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Our step-by-step guide provides instructions on how to activate an environment with the deep learning framework of your choice or swap between environments using simple one-line commands.

But the benefits of the AMI don’t stop there. The environments on the AMI operate as mutually-isolated, self-contained sandboxes. This means when you run your deep learning code inside the sandbox, you get full visibility and control of its run-time environment. You can install a new software package, upgrade an existing package or change an environment variable—all without worrying about interrupting other deep learning environments on the AMI. This level of flexibility and fine-grained control over your execution environment also means you can now run tests, and benchmark the performance of your deep learning models in a manner that is consistent and reproducible over time.

Finally, the AMI provides a visual interface that plugs straight into your Jupyter notebooks so you can switch in and out of environments, launch a notebook in an environment of your choice, and even reconfigure your environment—all with a single click, right from your Jupyter notebook browser. Our step-by-step guide walks you through these integrations and other Jupyter notebooks and tutorials.

New AWS Deep Learning AMIs for Machine Learning Practitioners | AWS AI Blog

Get started with machine learning using Python |

From self-driving cars to stock market predictions to online learning, machine learning is used in almost every field that utilizes prediction as a way to improve itself. Due to its practical usage, it is one of the most in-demand skills right now in the job market. Also, getting started with Python and machine learning is easy as there are plenty of online resources and lots of Python machine learning libraries available.

Source: Get started with machine learning using Python |

Torch is a scientific computing framework with wide support for machine learning algorithms. Torch | Scientific computing for LuaJIT.