Deep Learning for Android Apps

Delve into deploying DL models on Android using TensorFlow Lite. Gain insights into training, converting models, and practical applications through case studies for efficient mobile integration.
83 Lessons
16h
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Traditionally, computationally intensive deep learning (DL) models aren’t deployed to mobile devices due to their limited memory and computational power. But with a lightweight framework such as TensorFlow (TF) Lite, DL models can be deployed and run efficiently on mobile and edge devices, benefiting from fast response time, data privacy, and cost reduction in cloud computing resources. This course aims to equip you with the techniques to deploy DL models on Android devices using the TF Lite framework. You’ll start with a quick introduction to Python and machine learning. Next, you’ll explore different learning paradigms and DL models. You’ll also get practical knowledge of the applications of TF in the context of DL. The course covers TF Lite for mobile applications with different case studies showcasing its use in Android apps. By the end of this course, you’ll gain the necessary skills to train DL models, convert them to the TF Lite format, and deploy them into your Android apps.
Traditionally, computationally intensive deep learning (DL) models aren’t deployed to mobile devices due to their limited memory...Show More

WHAT YOU'LL LEARN

An understanding of Python libraries for data processing, analysis, visualization, and deep learning
The ability to apply TensorFlow and Keras to train deep learning models
Working knowledge of training and evaluating deep learning models on various datasets
The ability to convert TensorFlow models to the TensorFlow Lite format and deploy them to Android applications
An understanding of Python libraries for data processing, analysis, visualization, and deep learning

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TAKEAWAY SKILLS

Python 3

Deep Learning

Deep learning models in Keras

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Author NameDeep Learning for AndroidApps

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