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Graph Neural Networks (GNNs) are deep learning methods that operate on graphs and are used to perform inference on data described by graphs. Graphs have been used in mathematics and computer science for a long time and give solutions to complex problems by forming a network of nodes connected by edges in various irregular ways. Traditional ML algorithms allow only regular and uniform relations between input objects, struggle to handle complex relationships, and fail to understand objects and their connections which is crucial for many real-world data. Google researchers added a new library in TensorFlow, called TensorFlow GNN 1.0 (TF-GNN)

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically...

Many developers who use Python for machine learning are now switching to PyTorch. Find out why and what the future could hold for TensorFlow.

Realtime and offline SKU detection in the browser using Tensorflow.js

The Ultimate Guide To Always Install The Latest GPU Version Of TensorFlow on your PC no matter what

To understand the differences between automatic differentiation libraries, let’s talk about the engineering trade-offs that were made. I would personally say that none of these libraries are “better” than another, they simply all make engineering trade-offs based on the domains and use cases they were aiming to satisfy. The easiest way to describe these trade-offs is to follow the evolution and see how each new library tweaked the trade-offs made of the previous. Early TensorFlow used a graph building system, i.e. it required users to essentially define variables in a specific graph language separate from the host language. You had to define “TensorFlow variables” and “TensorFlow ops”, and the AD would then be performed on this static graph. Control flow constructs were limited to the constructs that could be represented statically. For example, an `ifelse` function statement is very different from ... READ MORE

Should you use PyTorch vs TensorFlow in 2023? This guide walks through the major pros and cons of PyTorch vs TensorFlow, and how you can pick the right framework.

Why is Model Compression important?  A significant problem in the arms race to produce more accurate models is complexity, which leads to…

Delve into the comprehensive comparison of PyTorch and TensorFlow, two leading machine learning frameworks. This article covers vital differences in ease of use, graph definition, and deployment capabilities, including insights on transitioning from PyTorch to TensorFlow Lite.

We’ll show you how to quickly build a Streamlit app to synthesize celebrity faces using GANs, Tensorflow, and st.cache.

Are you not able to load your NumPy data into memory? Does your model have to wait for data to be loaded after each epoch? Is your Keras…

Posted by Le Hou and Youlong Cheng, Software Engineers, Google Research Deep neural network models form the backbone of most state-of-the-art ima...

What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.

Take an inside look into the TensorFlow team’s own internal training sessions--technical deep dives into TensorFlow by the very people who are building it. On this episode of Inside TensorFlow, Software Engineer Alex Passos discusses the eager execution runtime. Let us know what you think about this presentation in the comments below! Watch more from Inside TensorFlow Playlist → https://goo.gle/Inside-TensorFlow Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that everyone seems to be talking about. The second part is a tensorflow tutorial on getting started, installing and building a small use case. This post is the second part of… Read More »Tensorflow Tutorial : Part 2 – Getting Started

In this article, you will explore how you can leverage Kubernetes, Tensorflow and Kubeflow to scale your models without having to worry about scaling the infrastructure.

Learn basic and advanced concepts of TensorFlow such as eager execution, Keras high-level APIs and flexible model building.

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. - wiseodd/generative-models