An implementation with Keras. Whenever you build and train a model for a machine learning task, regardless of its being a classification or regression one.
Whenever you build and train a model for a machine learning task, regardless of its being a classification or regression one, your final goal is to make reliable predictions on new, never seen before input data. In other words, you want your model to generalize well on new data.
To achieve this goal, you have to prevent your model from being either excessively adjusted to training data (overfitted), or not capable of capturing pattern in data at all (underfitted).
Note that the concepts of overfitting and underfitting are strictly related to the notion of bias-variance trade-off.
In this article, I’m going to dwell on the problem of overfitting and how to deal with it.
One of the reasons why overfitting might occur is the lack of data. Indeed, if you are training your model on too few data, it will try to exasperate its extraction of features from the training data, with the risk of identifying patterns that do not exist.
However, it often happens that available data are very few and that is all we can have. Namely, imagine a manufacturing company that wants to examine snapshots of its machinery with the goal of classifying them as “healthy” or “at risk of breakdown”. To train its algorithm (let’s say, a convolutional neural network, CNN) the company will need a bunch of pre-labeled images. The procedure of data collection will need time, but what if the company wants to accelerate the process, starting from a small sample of images? Well, rather than waiting for new images to come, the company could use the available data and derive new images from them, in such a way that each “new image” is created consistently with respect to the existing ones.
This process is called data augmentation and it is extremely powerful in terms of the increase of accuracy of the model. In the next paragraphs, we are going to see different types of data augmentation for image data, plus their implementation with Keras.
My inspirational muse for this activity will be a majestic golden retriever:
The problem with deep learning models is they need lots of data to train a model. There are two major problems while training deep learning models is overfitting and underfitting of the model. Those problems are solved by data augmentation is a regularization technique that makes slight modifications to the images and used to generate data.
A few compelling reasons for you to starting learning Computer. In today’s world, Computer Vision technologies are everywhere.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.