## Continuing Adventures in Machine Learning

In the last post, I wrote about calculating the cost of linear regression learning models combined with using gradient descent to find the minimized cost. Quick review of the key equations. Hypothesis: $$h_\theta(x) = \theta_0 + \theta_{1}x$$ Parameters: $$\theta_0, \theta_1$$ Cost Function: $$J(\theta_0,\theta_1) = \frac{1}{2m} \sum_{i=1}^m(h_\theta(x^{(i)}) - y^{(i)})^2$$ Goal: $$\underset{\rm \theta_0,\theta_1}{\rm minimize}$$ $$J(\theta_0, \theta_1)$$ With these tools, we can perform a gradient descent, an optimization algorithm designed to find $$\underset{\rm \theta_0,\theta_1}{\rm minimize}$$ $$J(\theta_0, \theta_1)$$. [Read More]

## Rediscovering Math Through Machine Learning

There are two major, obvious, technology trends of interest to me that are being used to solve business problems today: blockchain and machine learning. The promise of AI has tantalized computer scientists and the general public for a long time, with general human intelligence out of grasp even still, however, modern advances in approaches to implementing machine learning algorithms coupled with a dramatic growth in computational capacity have yielded powerful tools to address discrete problem domains. [Read More]