Deep learning for object detection using Tensorflow 2

Understand, train and evaluate Faster RCNN, SSD and YOLO v3 models using Tensorflow 2 and Google AI Platform

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Deep learning for object detection using Tensorflow 2

What You Will Learn!

You will learn how Faster RCNN deep neural network works
You will learn how SSD deep neural network works
You will learn how YOLO deep neural network works
You will learn how to use Tensorflow 2 object detection API
You will learn how to train and evaluate deep neural networks for object detection such as Faster RCNN, SSD and YOLOv3 using your own custom data
You will learn how to "freeze" your model to get a final model that is ready for production
You will learn how to use your "frozen" model to make predictions on a set of new images using openCV and Tensorflow 2
You will learn how to use Google Cloud AI platform in order to train your object detection models on powerful cloud GPUs
You will learn how to use Tensorboard to visualize the development of the loss function and the mean average precision of your model
You will learn how to change different parameters in order to improve your model's performance

Description

This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNN, SSD and YOLO models.

For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work.

After this, you will learn how to leverage the power of Tensorflow 2 to train and evaluate these models on your local machine.

Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GPUs offered by google.

I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including :

Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning.
By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions.
By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GPUs.