In this episode, Seth and Tania will talk about the Python community and the scientific Python ecosystem. So if you always wanted to know what is so great about Python for Machine learning and its community this episode is for you.

More Information:

Adding GPU compute support to Windows Subsystem for Linux (WSL) has been the #1 most requested feature since the first WSL release.

Learn how Windows and WSL 2 now support GPU Accelerated Machine Learning (GPU compute) using NVIDIA CUDA, including TensorFlow and PyTorch, as well as all the Docker and NVIDIA Container Toolkit support available in a native Linux environment.

Clark Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2.

Additionally, Clarke will demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.

Learn more: has a two hour, ad-free tutorial on how to use TensorFlow 2.0 in this full course for beginners.

Course created by Tech with Tim. Check out his YouTube channel:

Course Contents

  • (0:00:00) What is a Neural Network?
  • (0:26:34) How to load & look at data
  • (0:39:38) How to create a model
  • (0:56:48) How to use the model to make predictions
  • (1:07:11) Text Classification (part 1)
  • (1:28:37) What is an Embedding Layer? Text Classification (part 2)
  • (1:42:30) How to train the model – Text Classification (part 3)
  • (1:52:35) How to saving & loading models – Text Classification (part 4)
  • (2:07:09) How to install TensorFlow GPU on Linux

Generative models, commonly referred to as GANs, are a family of AI architectures whose aim is to create data samples from scratch. They work by capturing the data distributions of the type of things we want to generate.

Here’s an interesting read on the topic.

These kind of models are being heavily researched, and there is a huge amount of hype around them. Just look at the chart that shows the numbers of papers published in the field over the past few years:

This talk from io19 is for people who know how to code, but who don’t necessarily know machine learning.

Watch this video to learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code.

Chances are that you already know what TensforFlow is and why it’s important. However, as AI spreads from the lab to data science departments and into production, tools like TensorFlow will start crossing paths with the rest of enterprise IT.

TechRebublic has details on a free ebook on TensorFlow: a Guide for IT Pros.

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. It offers tremendous opportunities for developers building machine learning into their products. This ebook looks at what TensorFlow is, where it’s headed, and how it’s being put to work.