Deep learning for object detection using Tensorflow 2
What you'll 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
- You need to have a basic level of Python (if you know what classes and functions are then you are good to go!)
- You need to have a basic understanding of what Tensorflow is.
- You don't need any prior understanding of what object detection is, this is the mission of the course!
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.
Who this course is for:
- AI enthusiasts
- Data scientists
- Computer vision and machine learning students
- software developers
My name is Nour-Islam Mokhtari and I am a machine learning engineer with a focus on computer vision applications. I have 3 years of experience developing and maintaining deep learning pipelines. I worked on several artificial intelligence projects, mostly focused on applying deep learning research to real world industry projects. My goal on Udemy is to help my students learn and acquire real world and industry focused experience. I aim to build courses that can make your learning experience smooth and focused on the practical aspects of things!