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Are you prepared to take the next major step in your machine learning journey? Working with toy datasets and popular data science libraries and frameworks is a fantastic place to begin.
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Are you prepared to take the next major step in your machine learning journey? Working with toy datasets and popular data science libraries and frameworks is a fantastic place to begin. However, if you actually want to stand out from the crowd, you must take a risk and differentiate yourself. So, let's take a look at some of the data science GitHub projects that have been developed.
Machine Learning Projects pyforest - Importing all Python Data Science Libraries with a Single Line of Code • As the title implies, your standard data science libraries are imported using a single library - pyforest. Pyforest presently includes pandas, NumPy, matplotlib, and many other data science libraries.
Simply run pip install pyforest to install the library on your system, and you're ready to go. In addition, you may import all of the popular Python libraries for data science with a single line of code. HungaBunga - An Approach to Building Machine Learning Models using Sklearn How do you choose the best machine learning model from among many you've created? How do you confirm that the correct hyperparameter values are in use?
These are essential questions that a data scientist must address. And, unlike most data science libraries, the HungaBunga project will assist you in getting that answer faster. It iteratively runs through all of the sklearn models (yep, all!) with all of the various hyperparameters and ranks them using cross-validation.
Deep Learning Projects DeepMind's Behavior Suite for Reinforcement Learning (bsuite) • Deepmind has recently made headlines due to massive year-on-year losses. But, let's face it, the corporation is still well ahead in terms of reinforcement learning research. They have staked a lot of money on this field as the future of artificial intelligence.
So, here is their most recent open-source release - the bsuite. This project is a collection of experiments designed to uncover the fundamental capabilities of a reinforcement learning agent. A thorough explanation of how to use bsuite in your projects can be found in the GitHub repository.
RAdam • The inventors of RAdam demonstrate in their work that the convergence problem we face in deep learning techniques is caused by an unacceptably large variance in the adaptive learning rate during the early stages of model training.
RAdam is a novel Adam variation that corrects the variance of the adaptive learning rate. This release is a significant improvement over the vanilla Adam optimizer, which suffers from variance.
End Notes • The amount of research and thus achievements occurring in data science is astounding. Whatever era or standard you use to compare it to, the rate of advancement is mind-boggling. • Which data science project piqued your curiosity the most? Will you be attempting anything soon?
Please let me know in the comments section below, and we'll brainstorm some ideas! We at Tutort Academy offersArtificial Intelligence Certification Course Onlineand Machine Learning and Ai Courses Bangalorefor working professionals.
