Pytorch

Ever want to generate a new repo based on a predefined template? Now you can using Cookiecutter! I will show you how to easily spin up a fresh Cookiecutter repo for your latest data science project in Python. Cookiecutter is an awesome command-line tool and Python package that creates projects (aka populates repo folders) based on cookiecutters (or project templates). What does this mean? Goodbye to manually copying and pasting old project repos. Now, you can automatically clone one of the thousands of cookiecutters or you can create your own. ...

Recommendations systems are good for matching users to their favorite products and are incredibly popular. In fact you have likely used a recommendation system at least once in your life. For example, Amazon uses recommendation systems to suggest new exciting products to purchase based on users’ previous purchase patterns and those similar users. Netflix also utilizes recommendation systems to suggest new TV Shows and movies. Before we get into recommendation systems, it is important to briefly cover two general-purpose approaches for identifying target customer groups and making product recommendations. These two approaches are called Clustering and Association Rules. ...

Feed forward networks cannot learn from the past, but Recurrent Neural Networks (RNNs) can learn by accepting data in a sequence. Examples of applications for RNNs include the text autocomplete feature on your phone and performing language translations. Recurrent Neurons (RNs) act as the building blocks of RNNs. The difference between RNs and feed forward neurons is that RNs accept input x, at time t, as well as a hidden state or output from time t-1 from another RN. The output of a RN is a vector, unlike for a feed forward neuron. RNNs are trained (i.e. their weights are calculated) using backpropagation via Gradient Descent Optimization in time. Output from a single RNN layer at time instance,t is an input to the next layer. Each layer in the RNN represents an instance in time. ...

The purpose of this article is to share what I learned from a recent PyTorch course. We will share general machine learning tips as well as insights specific to deep learning library PyTorch. PyTorch is a deep learning library for Python and was created by Facebook in 2016. PyTorch is good for deep learning beginners. There are several other popular deep learning frameworks such as TensorFlow, Keras, Chainer, and ONNX. TensorFlow was developed by Google and now includes Keras (previously a separate framework). I chose to deepen my knowledge in PyTorch because it is easy to learn and is commonly used for deep learning. Fastai is another deep learning open source library that is really awesome and intuitive to use. While using Fastai, I found myself curious about the inner workings so it’s a plus that fastai wraps PyTorch as I can gain a better understanding of both libraries at once. Two birds, one stone! ...