What's Next?

What's Next

You have implemented the foundational algorithms of machine learning from scratch — from a single linear neuron to gradient descent, k-means, and regularized regression. Here are natural next steps:

  • Deep Learning — Implement backpropagation and multi-layer networks. Add batch normalisation, dropout, and Adam optimisation
  • Convolutional Networks — 2D convolution, pooling, and feature maps for image classification
  • Recurrent Networks — LSTMs and GRUs for sequence modelling, time series, and language
  • Tree-Based Methods — Decision trees, random forests, and gradient-boosted trees (XGBoost)
  • Probabilistic ML — Gaussian processes, variational autoencoders, and Bayesian inference

Further Reading

  • The Elements of Statistical Learning by Hastie, Tibshirani & Friedman — The definitive statistical ML reference, freely available online
  • Pattern Recognition and Machine Learning by Bishop — Bayesian perspective, rigorous treatment
  • Deep Learning by Goodfellow, Bengio & Courville — Covers neural networks from perceptrons to attention
  • fast.ai — Practical deep learning top-down course, highly recommended alongside theory
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