Voukoder Reviews

Scikit-learn Reviews

Voukoder mentions (22)

• Loss of video quality after rendering

  Beyond that x.264 can give better quality renders though it's CPU-only and will take a while. Render from VEGAS through Voukoder and see if you like it better. I do my final renders this way. Source: about 1 year ago

• Video will render when divided into parts, but not all at once

  You can still render through Voukoder or use the built-in VEGAS encoders. voukoder.org/. Source: about 1 year ago

• Video will render when divided into parts, but not all at once

  Personally I'd just render through Voukoder which will likely complete. Try x264 for quality or a GPU-enabled mode (NVENC) if your drivers are new enough to work. Source: about 1 year ago

• I came back to Vegas after like 8 years, currently on version 18. Is this a bug or did they really not add .webm/.mkv support after all these years?

  Finally you can render to a number of formats through Voukoder voukoder.org/. Source: over 1 year ago

• NVENC Render starts/pauses/starts. Not continuous.

  If you want more consistent loading try NVENC through Voukoder. Expect about a 10% improvement. QSV works well too if you have an iGPU. 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 / 15 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