A typical bottleneck is caused by the fact that humans can’t deliver software (or hardware, if operating outside the cloud) fast enough. Even if they were capable of hacking code fast enough, they may be busy maintaining existing systems, which is another critically human activity. This observation helps us to realize that although “infrastructure” sounds very technical, we are not building infrastructure for the machines. We are building infrastructure to make humans more productive. This realization has fundamental ramifications to how we should think about and design infrastructure for data scientists— for fellow human beings, instead of for machines. For instance, if we assume that human-time is more expensive than computer-time, which is certainly true for most data scientists, it makes sense to use a highly expressive, productivity-boosting language like Python instead of a low-level language like C++, even if it makes workloads more inefficient to process. We will dig deeper into this question in chapter 5

If you have a list of files, but you only want to delete files older the a certain date, for example, a maildir folder with 5 years worth of email, and you want to delete everything older then 2 years, then run the following command.

find . -type f -mtime +XXX -maxdepth 1 -exec rm {} \;

The syntax of this is as follows.

1. find – the command that finds the file

How to delete all files before a certain date in Linux

If you have a list of files, but you only want to delete files older the a certain date, for example, a maildir folder with 5 years worth of email, and you want to delete everything older then 2 years, then run the following command.

find . -type f -mtime +XXX -maxdepth 1 -exec rm {} \;

The syntax of this is as follows.

. (dot) – signifies the current folder. You can change this to something like /home/someuser/mail/somedomain/someemail/cur or whatever path you nee

Product Recommendation . Given a sequence of past purchases for a customer, predict the next purchase for a customer

The choice of activation function is most important for the output layer as it will define the format that predictions will take.

Binary Classification (2 class) : Logistic activation function, or sigmoid , and one neuron the output layer.

we can conceal certain parts of the input sequence and train the model to predict what is missing, to ‘‘fill in the blanks”. If we always blank only the last element in a historical sequence, the model effectively learns to predict the most likely future, conditioned on the observed past. Applying this idea to customer transaction records, we can forecast sequences predicting future behavior. We next present our model architecture in detail