A hands-on look at building machine learning models from scratch, testing trading algorithms, and applying LSTM networks to forecast hourly energy demand.

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A hands-on exploration of building machine learning models from scratch, starting with a trading algorithm using regression trees that achieved 220% returns on historical stock data. The author then tackles energy demand forecasting by implementing a feed-forward neural network with backpropagation before upgrading to LSTM networks to handle temporal patterns. Key challenges include addressing gradient explosion through data scaling, switching from ReLU to tanh activation functions, and implementing the Adam optimizer. The final LSTM model with 50 neurons successfully predicts hourly energy interconnection flows without overfitting, demonstrating that foundational ML techniques remain powerful tools for practical time-series forecasting problems.

Getting Back to Basics

From Dollars to Megawatts: Building Trust in Machine Learning Models

The Challenges: Gradient Explosion and Instability

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