Machine Learning
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Machine Learning is, put simply, getting computers to generalize from examples. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) consist primarily of beginner-focused introductions to common ML models or concepts. I felt like too many ML tutorials weren't accessible enough, so I strove to make my guides as clear and beginner-friendly as possible.
Unsure where to start? Here are some of my best / most popular posts:
- An Introduction to Neural Networks
- An Introduction to Convolutional Neural Networks
- An Introduction to Recurrent Neural Networks
- Random Forests for Complete Beginners
Similar tags include Neural Networks, Computer Vision, and Random Forests.
Happy Reading!
Analyzing Applications of Deep Learning in Aquaculture
Deep Computer Vision to protect the Ocean and feed humanity sustainably
ReadSimultanous Super Resolution of Underwater Images using Cycle Generative Adversarial Networks
Enhancing underwater imagery using GANs
ReadA first glimpse of a Neural network: Building a Digit Classifier
Keras is an easy-to-use abstraction built on top of tensorflow. Let's use Keras to build our first "hello world" of Machine Learning - The Digit Classifier
ReadThe Deep Learning Series: History of Machine Learning
A brief introduction to the history of Machine Learning.
ReadThe Deep Learning Series
A one stop shop to learning eveything you need to know about Deep Learning.
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