June 2023

TensorFlow Newsletter June 2023

Explore new tools, use LLMs in real-world applications, and more

Learn how Keras makes deep learning easy
Explore the components of the Keras API, which provides an approachable interface for solving machine learning problems with TensorFlow.
View developer guide
Create an autocomplete Android app with KerasNLP and TensorFlow Lite
Large language models (LLMs) are trained to generate text based on large datasets. Learn how to load a KerasNLP model, optimize it with quantization techniques, and deploy it to an Android demo app that can run any compatible TFLite LLMs.
View example
Bring ML ideas to life using Visual Blocks
Visual Blocks is a new graphical programming framework for rapid prototyping and experimentation. Use powerful machine learning building blocks like PaLM 2, iterate within a visual interface, and easily deploy.
Try Visual Blocks
Expanding access to ultrasound technology with TensorFlow Lite
Read how Google’s Health AI team is working to expand global access to maternal healthcare by building a mobile-optimized fetal ultrasound system using TensorFlow Lite for on-device inference.
Read the blog
Visualize and interpret decision trees with dtreeviz
Use the dtreeviz library with TensorFlow Decision Forests to visualize how each decision node in a tree splits up a specific feature’s domain and show the distribution of training instances in each prediction.
Read the blog
Try it in Colab
Augment recommendation systems with state-of-the-art LLMs
Explore how you can use the PaLM API to create recommendations in chat applications, generate and sort recommendations, use embeddings to retrieve unknown candidates, and more.
Read the blog
Transitioning from software engineering to ML engineering
What are the key mindset differences between Machine Learning Engineering (MLE) and Software Engineering (SWE)? Find out what a typical workday looks like for each role, their complexities, and how they differ from planning to defining success.
Watch the video
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