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Sorry to hear this and good on you for posting and reaching out.. There has been some great advice already about counselling and journalling. Also, I recommend meditation (something I am trying to do more of, as my head is busy). This app is great - https://meditofoundation.org/medito-app. Source: 12 months ago
I would also suggest using a timer app like Medito which is completely free and try to start off with 5 to 10 minutes sessions as many days a week as you can. Source: about 1 year ago
There's a great (totally free) app, medito, which has lessons that I think are some of the best introduction to getting meaningful results from meditation. Source: over 1 year ago
Use the prayer time as mindfulness meditation. Here's a free one. Source: over 1 year ago
Feeling Good by David D Burns, Free meditation app,Paid meditation app,Vipassana on YouTube. Source: over 1 year 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 / 3 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
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: over 1 year ago
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