1. Machine Learning for Beginners: An Introduction to Neural Networks
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A simple explanation of how they work and how to implement one from scratch in Python.
Neural Networks From Scratch
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This 4-post series, written especially with beginners in mind, provides a fundamentals-oriented approach towards understanding Neural Networks. We’ll start with an introduction to classic Neural Networks for complete beginners before delving into two popular variants: Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
For each of each these types of networks, we’ll:
- See the structure of the network.
- Understand the motivation behind using that type of network.
- Introduce a real-world problem that can be solved using that network.
- Manually derive the gradients needed to train our problem-specific network.
- Implement a fully-functioning network completely from scratch (using only numpy) in Python.
Background
This series requires ZERO prior knowledge of Machine Learning or Neural Networks. However, background in the following topics may be helpful:
- Multivariable Calculus, used when deriving the gradients needed to train our networks. These gradient derivations can be skipped if you don’t have the background.
- Linear Algebra, specifically Matrix algebra - matrices are often the best way to represent weights for Neural Networks.
- Python 3, because the Python implementations in these posts are a major part of their educational value. A baseline proficiency in Python is enough.
The Series
Ready to get started? Here we go:
2. An Introduction to Recurrent Neural Networks for Beginners
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A simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
3. CNNs, Part 1: An Introduction to Convolutional Neural Networks
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A simple guide to what CNNs are, how they work, and how to build one from scratch in Python.
4. CNNs, Part 2: Training a Convolutional Neural Network
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A simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python.
What Next?
Still eager to learn? Some more things you can do include:
- Build your first neural network with Keras.
- Apply neural networks to Visual Question Answering (VQA).
- Experiment with bigger / better neural networks using proper machine learning libraries like Tensorflow, Keras, and PyTorch.
- Try your hand at using Neural Networks to approach a Kaggle data science competition.
- Review notes from Stanford’s famous CS231n course on CNNs.
- Take one of many good Neural Networks courses on Coursera.
I plan on writing more about Neural Networks in the future, so subscribe to my newsletter if you want to get notified of new content.
Thanks for reading!