This is the first post in Reading List series where I recap important papers as part of continuing my data science education.
Gradient_Based Learning Applied to Document Recognition by Y. LeCun, L. Bottou, Y. Bengio and P.Haffner.
I start with this paper as it advances a deep learning class that is essentially computers’ vision and hearing, enabling real-life applications such as safe self-driving cars and reading of radiology images.
In discussing solutions to high-dimensional pattern recognition, ie handwritten character or speech, the paper shows that automatic learning machines that operate directly on pixel images are more accurate than hand-crafted individually designed feature extraction modules.
It thus suggested a then-new paradigm of globally trained Graph Transformer Networks, the core of which is a Convolutional Neural Network.
The three conditions that allowed for this progress are: one, low-cost machines with brute-force arithmetic methods; two, large databases for problems with a wide market interest and three, powerful machine learning techniques that can handle high-dimensional inputs and can generate intricate decision functions.
While more automatic learning is beneficial, no learning technique can succeed without a minimal prior knowledge about the task.
Gradient-based learning draws on the fact that it is much easier to minimize the loss on a smooth continuous function than a discrete one, as estimated by the impact of small variations of the parameter values.
The basic idea of back-propagation is that gradients can be computed efficiently by propagation from the output to input.
Keywords: Neural Networks, OCR, Document Recognition, Machine Learning, Gradient-Based Learning, Convolutional Neural Networks, Graph Transformer Networks, Finite State Transducers.