Based on our record, Scikit-learn should be more popular than bloop. It has been mentiond 29 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 22 days ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
In this blog post, I’ll be comparing 3 distinct AI-first code search tools I recently came across: Cody (developed by late-stage startup, Sourcegraph), SeaGOAT (an open-source project that was trending on HN last week), and Bloop (an early-stage YC startup). I’ll be evaluating them along the dimensions of user-friendliness as well as their accuracy. - Source: dev.to / 9 months ago
If you're confused about any of the code snippets above, you can check out bloop.ai and phind.com (along with its VSCode extension) to answer any of your questions about the repository, noting that both have free plans. - Source: dev.to / 10 months ago
Bro let me turn your life inside out: https://bloop.ai. Source: about 1 year ago
GPT4: Ok, here you go - https://bloop.ai/. Source: over 1 year ago
We've invested a lot into helping LLMs reason and explain large codebases. We use a hybrid approach of local models for semantic search and a mix of OpenAI and Anthropic's models for language output and summarisation. We're two years in but everything still feels super early given how quickly the fundamentals are improving. Would love your feedback - https://bloop.ai. - Source: Hacker News / over 1 year ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Productivity Power Tools - Extension for Visual Studio - A set of extensions to Visual Studio 2012 Professional (and above) which improves developer productivity.
OpenCV - OpenCV is the world's biggest computer vision library
Sourcegraph - Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.
NumPy - NumPy is the fundamental package for scientific computing with Python
EssenceAI - Simplify Code Understanding using the power of GPT-4