Deep learning: Why Now:
Whenever i think about the impact that deep learning will have in Africa in the next 5–10 years from now, My curiosity and passion to understand and implements deep learning increases.
Improving Agriculture yields in Africa with deep learning becomes my first focus.
Deep learning is becoming increasingly relevant because of three key reasons:
Infinitely Flexible Function
- An Infinitely Flexible Function: The function driving deep learning processes is the neural network, which are essentially universal approximation machines. Specifically, the universal approximation theorem tells us that this function is capable of handling any problem we apply it to.
- Parameter Fitting: Through gradient descent/backward propagation, we’re able to fit to any parameters given training data to do so. This allows us to approximate the theoretical function that allows us to do anything, and apply it.
- Speed and Scalability: Deep learning relies upon matrix operations to achieve it’s results, which are computationally expensive. Graphics Processing Units (GPU’s) are processors that are optimized to perform these kinds of computations for computer graphics and image processing. Fortunately, the growth of the gaming industry has resulted in cheap and powerful GPU’s.
As a result of these reasons, it is now possible for anyone to apply deep learning techniques to real problems in a way that is both affordable and fast.