DecodeChess Reviews

Scikit-learn Reviews

DecodeChess mentions (13)

• What could I contribute to chess as a developer?

  Edit - I'll add a very complex idea: an AI-powered tool that analyzes a position as a person would, using natural language to explain positional and long-term ideas, not pointing out simple tactics. decodechess.com has tried this but it's not there yet. Source: 7 months ago

• Computer Learning Options

  It's not a free app, but they provide a demo that shows the main features: https://decodechess.com/. Source: about 1 year ago

• Why is this checkmate? Couldn’t the black Queen have blocked the check by moving to d7 (and THEN white could have taken the Queen and it would have been checkmate on the next move)?

  Instead I'd play real people and use something like decodechess.com or just the analysis board. Source: over 1 year ago

• chess analysis app?

  You could try Decode Chess, that will analyse one game per day for free, and explains the effects of each move in a lot more detail than the chess.com game review. Source: over 1 year ago

• How to get better?

  A couple of sources I've found that is helpful are Learning Chess and Decode Chess, because they offer solid analysis and evaluations telling you why one move is better than the other, helping you understand the reason behind the moves. Source: over 1 year ago

Scikit-learn mentions (29)

• Essential Deep Learning Checklist: Best Practices Unveiled

  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

• How to Build a Logistic Regression Model: A Spam-filter Tutorial

  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

• Link Prediction With node2vec in Physics Collaboration Network

  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

• WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point

  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

• PSA: You don't need fancy stuff to do good work.

  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