Transformer is a recent breakthrough in neural machine translation. Natural languages are complicated. A word in one language can be translated into multiple words in another, depending on the context. But what exactly a context is, and how you can teach the computer to understand the context was a big problem to solve. The invention […]
As we learned what a Transformer is and how we might train the Transformer model, we notice that it is a great tool to make a computer understand human language. However, the Transformer was originally designed as a model to translate one language to another. If we repurpose it for a different task, we would […]
When you work on a machine learning problem related to images, not only do you need to collect some images as training data, but you also need to employ augmentation to create variations in the image. It is especially true for more complex object recognition problems. There are many ways for image augmentation. You may […]
Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How […]
When you build and train a Keras deep learning model, you can provide the training data in several different ways. Presenting the data as a NumPy array or a TensorFlow tensor is common. Another way is to make a Python generator function and let the training loop read data from it. Yet another way of […]
We usually use TensorFlow to build a neural network. However, TensorFlow is not limited to this. Behind the scenes, TensorFlow is a tensor library with automatic differentiation capability. Hence you can easily use it to solve a numerical optimization problem with gradient descent. In this post, you will learn how TensorFlow’s automatic differentiation engine, autograd, […]
Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. A neural network is probably a concept older than machine learning, dating back to the 1950s. Unsurprisingly, there were many libraries created for it. The following aims to give an overview of some of the […]
Python for Machine Learning Crash Course. Learn core Python in 7 days. Python is an amazing programming language. Not only it is widely used in machine learning projects, you can also find its presence in system tools, web projects, and many others. Having good Python skills can make you work more efficiently because it is […]
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 […]
Python is a dynamic scripting language. Not only does it have a dynamic type system where a variable can be assigned to one type first and changed later, but its object model is also dynamic. This allows us to modify its behavior at run time. A consequence of this is the possibility of monkey patching. […]
