TensorFlow Newsletter June 2023
Explore new tools, use LLMs in real-world applications, and more
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Learn how Keras makes deep learning easy
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Explore the components of the Keras API, which provides an approachable interface for solving machine learning problems with TensorFlow.
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Create an autocomplete Android app with KerasNLP and TensorFlow Lite
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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.
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Bring ML ideas to life using Visual Blocks
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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.
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Expanding access to ultrasound technology with TensorFlow Lite
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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.
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Visualize and interpret decision trees with dtreeviz
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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.
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Augment recommendation systems with state-of-the-art LLMs
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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.
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Transitioning from software engineering to ML engineering
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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.
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Stay Connected
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