Machine Learning

We may have realized it's easier to build a brain than to understand one

if you know who said this let me know please

The process of making computers learn to solve problems themselves, based on examples of the problem.

Suppose we arrange for some automatic means of testing the effectiveness of any current weight assignment in terms of actual performance and provide a mechanism for altering the weight assignment so as to maximize the performance. We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programmed would "learn" from its experience.

Arther Samuel, Artificial Intelligence: A Frontier of Automation

Important concepts

Definitions from Practical Deep Learning Glossary, ch. 1:

Term Meaning
Label The data that we're trying to predict, such as "dog" or "cat"
Architecture The template of the model that we're trying to fit; the actual mathematical function that we're passing the input data and parameters to
Model The combination of the architecture with a particular set of parameters
Parameters The values in the model that change what task it can do, and are updated through model training
Fit Update the parameters of the model such that the predictions of the model using the input data match the target labels
Train A synonym for fit
Pretrained model A model that has already been trained, generally using a large dataset, and will be fine-tuned
Fine-tune Update a pretrained model for a different task
Epoch One complete pass through the input data
Loss A measure of how good the model is, chosen to drive training via SGD
Validation set A set of data held out from training, used only for measuring how good the model is
Training set The data used for fitting the model; does not include any data from the validation set
CNN Convolutional neural network; a type of neural network that works particularly well for computer vision tasks

The training loop: