Short, simple summaries of machine learning topics, to help you prepare for exams, interviews, reading the latest papers, or just a quick brush up. In less than two minutes, we'll cover the most obscure jargon and complex topics in machine learning. For more details, including small animated presentations, please visit erikpartridge.com. Please do join the conversation on Twitter for corrections, conversations, support and more at #mlbytes
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Contact path
Public web form
Booking probability
15%
Guest openness
Selective
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Signup to Generate a PitchShort, simple summaries of machine learning topics, to help you prepare for exams, interviews, reading the latest papers, or just a quick brush up. In less than two minutes, we'll cover the most obscure jargon and complex topics in machine learning. Please do join the conversation on Twitter for corrections, conversations, support and more at
Machine Learning Bytes is hosted by Erik Partridge. The show has published 6 episodes to date.
Recent episodes include "K-Fold Cross Validation" (31 July 2019, 1 minute), "Stratified Sampling" (30 July 2019, 1 minute), and "Boosting" (26 July 2019, 1 minute).
Machine Learning Bytes publishes roughly a few times a week, with episodes averaging 1 minute across 6 episodes.
Booking probability for Machine Learning Bytes is 15%, and the host is rated Selective on guests. Machine Learning Bytes requires a Pod Score of 80 or higher. Machine Learning Bytes's contact route on record: public web form.
The strongest fits for Machine Learning Bytes are IT Services & Software Development and Business & Professional Training. Machine Learning Bytes has a technology audience.
Erik Partridge hosts Machine Learning Bytes, a technology podcast with 6 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Episode #6
<p>K-fold cross validation is the practice by which we separate a large data set into smaller pieces, independently process each data set, and then train our models on some number of the segments, and validate it on the...
Episode #5
Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can...
Machine Learning Bytes
<p>Boosting is also an ensemble meta-algorithm, like boosting. However, in boosting we teach a large number of weak, but specialized learners, and combine them according to their strengths. For more information on...
Episode #3
<p>Bagging is an ensemble meta-algorithm. Basically, we take some number of estimators (usually dozens-ish), train them each on some random subset of the training data. Then, we average the predictions of each...
<p>The bias-variance trade-off is a key problem in your model search. While bias represents how well your model can capture the salient details of a problem, and generally correlates with more complex algorithms, it...
Episode #1
<p>The concept of empirical risk minimization drives modern approaches to training many machine learning algorithms, including deep neural networks. Today's thirty second summary covers the basics of what you need to...
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