For Machine Learning

Let’s take a look at a few different ways to filter and select rows in a Pandas dataframe based on multiple conditions.

TensorFlow users on Mac powered by Apple’s new M1/M2 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of TensorFlow 2.4.

This post will talk about GPU-accelerated PyTorch training using the MPS backend on Mac platforms. MPS enables high-performance training on GPU for MacOS devices with a Metal programming framework. I’ll also provide an overview of the software stack. So let’s talk briefly about the MPS backend and software components it relies on.

In this post we are going to use Miniconda, it’s a Python environment and it has a lot of scientific packages available for data science. I use Miniconda rather than Anaconda they’re both from the same company but Miniconda does not install a whole plethora of additional packages.

All parameters passed to the optimizer are retained inside the optimizer object so the optimizer can update their values and access their grad attribute.

In PyTorch, a model is updated by an optimizer and the learning rate is a parameter of the optimizer. The learning rate schedule is an algorithm to update the learning rate in an optimizer.

The modules (nn.Module) use internally the functional API. There is no difference as long as you store the parameters somewhere (manually if you prefer the functional API or in an nn.Module “automatically”)

We learned how to generate and plot the Normal and Uniform distributions using PyTorch and seaborn respectively.

PyTorch 2.0 provides two data primitives: datapipes and DataLoader2 that allow you to use pre-loaded datasets as well as your own data.

Normalize images and make a list of the common image augmentation techniques to increase the size and diversity of images present in the Pytorch 2.0 DataPipe.