Take a guess! What could be the possible reason for high variation in ranks? In other words, why does their model lose stability when evaluated on private leaderboard? Let’s look some possible reason.
The simple practice of a static test/train split is merely a convenience to cut down on operational complexity and difficulty of teaching. It is in no way optimal. That is, using slightly more complicated procedures can build better models on a given set of data.
Unsupervised learning algorithms are machine learning algorithms that work without a desired output label. A supervised machine learning algorithm typically learns a function that maps an input x into an output y, while an unsupervised learning algorithm simply analyzes the x’s without requiring the y’s. Essentially, the algorithm attempts to estimate the underlying structure of the population of x’s (in other words, the algorithm looks for natural groupings or clusters within the x’s). This is similar to the problem of density estimation in statistics. Typical unsupervised learning algorithms include clustering algorithms like K-means or hierarchical clustering methods. Some types of neural networks – like Self-Organizing Maps and Adaptive Resonance Theory models – also follow the unsupervised learning paradigm.
In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about implementing user based collaborative filtering approach using R. In this post, I will be explaining about basic implementation of Item based collaborative filtering recommender systems in r.
In this blog post, I’ll talk about the Visual Question Answering problem, and I’ll also present neural network based approaches for same. The source code for this blog post is written in Python and Keras, and is available on Github.
There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!
With so much hype about “big data” and the industry pushing for “big data” analytical tools for everyone, the question arises how many people have big data (for analytics) and how many of them really need these tools (which are more complex and often more immature compared to the traditional tools for analytics).