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Plan Szkolenia
Introduction
- ML Kit vs TensorFlow vs other machine learning services
- Overview of ML Kit features and components
Getting Started
- Setting up the ML Kit SDK
- Exploring APIs and sample apps
Implementing ML Kit Vision APIs
- Automating data entry (Text Recognition)
- Detecting faces for selfies and portraits (Face Detection)
- Interpreting body positions (Pose Detection)
- Adding background effects (Selfie Segmentation)
- Integrating Barcode Scanning
- Identifying objects, places, species, etc. (Image Labeling)
- Locating prominent objects in an image (Object Detection and Tracking)
- Recognizing handwritten texts (Digital Ink Recognition)
Working with Natural Language APIs
- Identifying languages
- Translating texts
- Generating smart replies
- Using entity extraction
Optimizing Existing Apps with ML Kit
- Using custom models with ML Kit
- Migrating from Firebase to the new ML Kit SDK
- Migrating from Mobile Vision to ML Kit SDK
- Reducing app size for deployment
- Refactoring apps to use dynamic feature modules
Troubleshooting Tips
Summary and Next Steps
Wymagania
- An understanding of machine learning
- Experience with mobile development
Audience
- Software Engineers
- Mobile App Developers
14 godzin
Opinie uczestników (5)
Ćwiczenia praktyczne.
Adam Borowski - NetWorkS! Sp. z o.o.
Szkolenie - AI Awareness for Telecom
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
Szkolenie - Artificial Intelligence Overview
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Szkolenie - Applied AI from Scratch in Python
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Szkolenie - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day