Top /r/MachineLearning posts, Jan 1824: Kmeans clustering is not a free lunch; A Deep Dive into Recurrent Neural Nets
Textbook Easter Eggs, issues with kmeans, recurrent neural networks, genetic algorithm challenges, and the implementation of machine learning pipelines are all in this week's top /r/MachineLearning posts.
By Grant Marshall.
This week on /r/MachineLearning, there are some great resources on NNs and genetic algorithms, plus a bit of humor.
1. Easter Egg in the General Index of Max Kuhn's Book "Applied Predictive Modeling" +65
This bit of humor points out the Hitchhiker’s Guide to the Galaxy reference in Max Kuhn’s “Applied Predictive Modeling”.
2. Kmeans clustering is not a free lunch (xpost /r/statistics) +65
This thoughtful article goes in depth about some of the assumptions that go into the kmeans model. It’s a good read for those who aren’t sure what makes kmeans feasible or infeasible. A good related read is the comments section of 11 Clever Methods of Overfitting and how to avoid them.
3. A Deep Dive into Recurrent Neural Nets +55
This deep article goes far into the topic of RNNs. It includes many helpful diagrams and examples to make a very technical topic easily digestible.
4. Lab Rat Race: an exercise in genetic algorithms [xpost /r/genetic_algorithms] +30
This Stack Exchange post contains genetic algorithm solutions to a coding challenge to make specimens learn how to get through a maze. It goes through how to address the challenge stepbystep in detail, providing some insight in how to implement genetic algorithm solutions. If you’re the sort that likes to have the code, the solutions are implemented in multiple languages at the end of the post.
5. Building and deploying largescale machine learning pipelines +28
This article is a bit different from most of what’s on /r/MachineLearning. Instead of discussing an algorithm or model, it instead addresses how to implement machine learning systems. This should be of interest to those actively implementing these types of systems in industry.
Related:
This week on /r/MachineLearning, there are some great resources on NNs and genetic algorithms, plus a bit of humor.
1. Easter Egg in the General Index of Max Kuhn's Book "Applied Predictive Modeling" +65
This bit of humor points out the Hitchhiker’s Guide to the Galaxy reference in Max Kuhn’s “Applied Predictive Modeling”.
2. Kmeans clustering is not a free lunch (xpost /r/statistics) +65
This thoughtful article goes in depth about some of the assumptions that go into the kmeans model. It’s a good read for those who aren’t sure what makes kmeans feasible or infeasible. A good related read is the comments section of 11 Clever Methods of Overfitting and how to avoid them.
3. A Deep Dive into Recurrent Neural Nets +55
This deep article goes far into the topic of RNNs. It includes many helpful diagrams and examples to make a very technical topic easily digestible.
4. Lab Rat Race: an exercise in genetic algorithms [xpost /r/genetic_algorithms] +30
This Stack Exchange post contains genetic algorithm solutions to a coding challenge to make specimens learn how to get through a maze. It goes through how to address the challenge stepbystep in detail, providing some insight in how to implement genetic algorithm solutions. If you’re the sort that likes to have the code, the solutions are implemented in multiple languages at the end of the post.
5. Building and deploying largescale machine learning pipelines +28
This article is a bit different from most of what’s on /r/MachineLearning. Instead of discussing an algorithm or model, it instead addresses how to implement machine learning systems. This should be of interest to those actively implementing these types of systems in industry.
Related:
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