Learn to build a face detection model using Tensorflow and Python. The course covers collecting images, annotating data, partitioning and augmenting data, building and training a deep learning model, and testing real-time detections. Students will acquire skills in image processing, data annotation, deep learning model building, and object detection. The teaching method includes hands-on coding sessions and practical demonstrations. This course is intended for individuals interested in computer vision, deep learning, and object detection using Python and Tensorflow.
Build a Deep Face Detection Model With Python and Tensorflow - Full Course
Overview
Syllabus
- START
- Explainer
- PART 1 - COLLECt IMAGES & ANNOTATE
- Breakdown Board
- Setting up and Getting Data
- PART 2 - PARTITION & AUGMENT DATA
- Review dataset and build Image Loading Function
- Partition Unaugmented Data
- Apply Image Augmentation on Images and Labels
- Build and Run Augmentation Pipeline
- Prepare Labels
- Combine Label and Image Samples
- PART 3 - BUILD & TRAIN THE DEEP LEARNING MODEL
- Build a Deep Learning Model using the Functional API
- Defining a Custom Loss Function & Optimizer
- Train a Neural Network
- PART 4 - TEST AND PERFORM REAL TIME DETECTIONS
- Final Results
- Ending
Taught by
Nicholas Renotte
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Reviews
5.0 rating, based on 1 Class Central review
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SARANYADEVI P KPRCAS-B.Sc.(CS&DA)So good to learn I understand very well
So i can understand easily. It's very useful for me.In this course I learnt alot.
