knowledge Transfer

All category is not represented in the same quantity. Some of them can be very infrequent which may represent a hard challenge for any ML algorithm.

Implementations of pre-trained BERT models already exist in TensorFlow due to its popularity. I leveraged the popular transformers library while building out this project.

This tutorial is dedicated to the use of the Transformers library using TensorFlow and the Keras API to fine-tune a State-of-The-Art Transformer model.

The image is passed through a stack of convolutional layers, where VGG uses 3×3 filters which are the smallest size to capture the notion of left/right, up/down, center.

We can import the module that we just uploaded, and then call the function. This file in the same directory so that Python knows where to find the module since it’s not a built-in module.

It’s worth noting that different parts of the data pipeline will stress different parts of the system. So for loading from the disk is an I/o- bound task and we’ll generally want to consume this I/O as fast as possible so that we’re not constantly waiting for images to arrive from disk one at a time.

A simple classification layer is added to the pre-trained model, and all parameters are jointly fine-tuned on a downstream task.

It is useful if you have a huge data set or if you are generating random data augmentations on the fly, i.e. infinite size. steps_per_epoch is batches of samples to train.

Using dynamic font serving can help you make sure you only include the fonts that your users need when they need them.

when using the keras callback, we have train and validation showing up on the same charts to make it much easier to compare accuracy, loss, and other metrics. This makes it easier to detect things like over-fitting.