There are not many books on deep learning at the moment because it is such a young area of study. There are a few books available though and some very interesting books in the pipeline that you can purchase by early access. In this post, you will discover the books available right now on deep […]
Need Help Getting Started with Applied Machine Learning? These are the Step-by-Step Guides that You’ve Been Looking For! What do you want help with? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: Step 1: Adjust Mindset. […]
As the popularity of attention in machine learning grows, so does the list of neural architectures that incorporate an attention mechanism. In this tutorial, you will discover the salient neural architectures that have been used in conjunction with attention. After completing this tutorial, you will better understand how the attention mechanism is incorporated into different […]
Data preparation is required when working with neural networks and deep learning models. Increasingly, data augmentation is also required on more complex object recognition tasks. In this post, you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras. After […]
TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow. In this post, you will discover the TensorFlow library for Deep Learning. […]
Python is an interpreting language. It means there is an interpreter to run our program, rather than compiling the code and running natively. In Python, a REPL (read-eval-print loop) can run commands line by line. Together with some inspection tools provided by Python, it helps to develop codes. In the following, you will see how […]
Sponsored Post By Luis Bermudez This blog walks through a process for experimenting with hyperparameters, training algorithms and other parameters of Graph Neural Networks. In this post, we share the first two phases of our experiment chain. The graph datasets that we use to make inferences on come from Open Graph Benchmark (OGB). If you […]
Python is a general-purpose computation language, but it is very welcomed in scientific computing. It can replace R and Matlab in many cases, thanks to some libraries in the Python ecosystem. In machine learning, we use some mathematical or statistical functions extensively, and often, we will find NumPy and SciPy useful. In the following, we […]
Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning. Nevertheless, meta-learning might also refer to the manual process of model selecting […]
Test-time augmentation, or TTA for short, is a technique for improving the skill of predictive models. It is typically used to improve the predictive performance of deep learning models on image datasets where predictions are averaged across multiple augmented versions of each image in the test dataset. Although popular with image datasets and neural network […]
