- Computational Complexity of Deep Learning: Solution Approaches - Jun 29, 2021.
Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can?
- Know-How to Learn Machine Learning Algorithms Effectively - Nov 23, 2020.
The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.
- Time Complexity: How to measure the efficiency of algorithms - Jun 24, 2020.
When we consider the complexity of an algorithm, we shouldn’t really care about the exact number of operations that are performed; instead, we should care about how the number of operations relates to the problem size.
- Applying Occam’s razor to Deep Learning - Jan 10, 2020.
Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.
- Generalization in Neural Networks - Nov 18, 2019.
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.
- How To Write Better SQL Queries: The Definitive Guide – Part 2 - Aug 24, 2017.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
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- The Surprising Complexity of Randomness - Jun 15, 2017.
The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.
- KDnuggets™ News 16:n12, Apr 6: Top 10 Essential Books; Perfect Data Science Interview - Apr 6, 2016.
Top 10 Essential Books for the Data Enthusiast; How to Compute the Statistical Significance of Two Classifiers Performance Difference; The Secret to a Perfect Data Science Interview; If Hollywood Made Movies About Machine Learning Algorithms.
- Avoiding Complexity of Machine Learning Problems - Mar 31, 2016.
Sometimes machine learning is the perfect tool for a task. Sometimes it is unnecessary overkill. Here are important lessons learned from the Quora engineering team.
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- How to Balance the Five Analytic Dimensions - Sep 3, 2015.
When developing a solution one has to consider data complexity, speed, analytic complexity, accuracy & precision, and data size. It is not possible to best in all categories, but it is necessary to understand the trade-offs.
- Map of the Complexity Sciences – from von Neumann & Kolmogorov to Hofstadter and Piatetsky-Shapiro (?) - Apr 21, 2015.
A map of the Complexity Sciences traces its intellectual heritage from Isaac Newton and Henri Poincare to John von Neumann, Andrei Kolmogorov, and Duncan Watts, and includes an unexpectedly familiar name.