Kimp Reviews

1. June Xing

   | Marketing Director at Pro Marketing | about 2 years ago
   Great service, very happy!

   Kimp is great. Their entire process is very organized. Really love the experience from on-boarding to handling the tasks. Quality designs, fast turnaround. Highly recommended!

   🏁 Competitors: Design Pickle, Penji, ManyPixels, Graphics Zoo, Flocksy

   👍 Pros:    Very organized structure
2. Cathy Lee

   | Founder/CEO at Phenom Marketing | about 2 years ago
   Amazing service!!

   I really wish that I knew about this kind of service long ago. I was referred to Kimp by a friend so decided to give them a try. I have blown away by how much I was able to get done and free up my time. Great designs from the getgo. My project manager is very responsive and friendly to work with. Overall, I really enjoy working with the Kimp team and I can see this being a very long working relationship.

   👍 Pros:    Can outsource all my design requirement so i can focus on scaling my business|Great quality designs from the getgo, they nail most of the designs in the first attempt.|Very responsive and friendly project manager|Followups from management team|Very affordable|Fast turnarounds

   👎 Cons:    Nothing i can think of at the moment, they are really like part of my team

Scikit-learn Reviews

Kimp mentions (0)

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