Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. […]
The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this […]
Activation functions play an integral role in neural networks by introducing nonlinearity. This nonlinearity allows neural networks to develop complex representations and functions based on the inputs that would not be possible with a simple linear regression model. Many different nonlinear activation functions have been proposed throughout the history of neural networks. In this post, […]
If you’ve looked at Keras models on Github, you’ve probably noticed that there are some different ways to create models in Keras. There’s the Sequential model, which allows you to define an entire model in a single line, usually with some line breaks for readability. Then, there’s the functional interface that allows for more complicated […]
You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll […]
You’ve probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those […]
Have you ever wanted an easy-to-configure interactive environment to run your machine learning code that came with access to GPUs for free? Google Colab is the answer you’ve been looking for. It is a convenient and easy-to-use way to run Jupyter notebooks on the cloud, and their free version comes with some limited access to […]
Big data, labeled data, noisy data. Machine learning projects all need to look at data. Data is a critical aspect of machine learning projects, and how we handle that data is an important consideration for our project. When the amount of data grows, and there is a need to manage them, allow them to serve […]
When working on code, whether we know it or not, we often come across the decorator design pattern. This is a programming technique to extend the functionality of classes or functions without modifying them. The decorator design pattern allows us to mix and match extensions easily. Python has a decorator syntax rooted in the decorator […]
Unit testing is a method for testing software that looks at the smallest testable pieces of code, called units, which are tested for correct operation. By doing unit testing, we can verify that each part of the code, including helper functions that may not be exposed to the user, works correctly and as intended. The […]
