Deep Learning for Computer Vision with Tensorflow 2.X
Deep Learning for Computer Vision with Tensorflow 2.X, available at $49.99, has an average rating of 3.7, with 60 lectures, based on 10 reviews, and has 135 subscribers.
You will learn about The application of deep learning in computer vision field The course is focused on image classification and object detection We'll review the main state of the art algorithms We'll develop several practical applications such as detecting Covid19 and License Plate Recognition This course is ideal for individuals who are Intermediate level users who want to learn about the latest SOTA algorithms or Users who want to learn in a concise way the theory behind the main SOTA algorithms or Users who want to learn how to implement practical apps for object detection and image classification It is particularly useful for Intermediate level users who want to learn about the latest SOTA algorithms or Users who want to learn in a concise way the theory behind the main SOTA algorithms or Users who want to learn how to implement practical apps for object detection and image classification.
Enroll now: Deep Learning for Computer Vision with Tensorflow 2.X
Summary
Title: Deep Learning for Computer Vision with Tensorflow 2.X
Price: $49.99
Average Rating: 3.7
Number of Lectures: 60
Number of Published Lectures: 60
Number of Curriculum Items: 60
Number of Published Curriculum Objects: 60
Original Price: $69.99
Quality Status: approved
Status: Live
What You Will Learn
- The application of deep learning in computer vision field
- The course is focused on image classification and object detection
- We'll review the main state of the art algorithms
- We'll develop several practical applications such as detecting Covid19 and License Plate Recognition
Who Should Attend
- Intermediate level users who want to learn about the latest SOTA algorithms
- Users who want to learn in a concise way the theory behind the main SOTA algorithms
- Users who want to learn how to implement practical apps for object detection and image classification
Target Audiences
- Intermediate level users who want to learn about the latest SOTA algorithms
- Users who want to learn in a concise way the theory behind the main SOTA algorithms
- Users who want to learn how to implement practical apps for object detection and image classification
This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2.X.
It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.
The course was entirely written using Google Colaboratory(Colab) in order to help students that don’t have a GPU card in your local system, however you can follow the course easily if you have one.
This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms.
We’re going to study in detail the following concepts and algorithms:
– Image Fundamentals in Computer Vision,
– Load images in Generators with TensorFlow,
– Convolution Operation,
– Sparsity Connections and parameter sharing,
– Depthwise separable convolution,
– Padding,
– Conv2D layer with Tensorflow,
– Pooling layer,
– Fully connected layer,
– Batch Normalization,
– ReLU activation and other functions,
– Number of training parameters calculation,
– Image Augmentation, etc
–Different ConvNets architectures such as:
* LeNet5,
* AlexNet,
* VGG-16,
* ResNet,
* Inception,
* The lastest state of art Vision Transformer (ViT)
–Many practical applications using famous datasets and sources such as:
* Covid19 on X-Ray images,
* CIFAR10,
* Fashion MNIST,
* BCCD,
* COCO dataset,
* Open Images Dataset V6 through Voxel FiftyOne,
* ROBOFLOW
In the Object Detection chapter we’ll learn the theory and the application behind the main object detection algorithms doing a journey since the beginnings to the latest state of the art algorithms.
You’ll be able to use the main algorithms of object detection to develop practical applications.
Some of the content in this Chapter is the following:
– Object detection milestones since Selective Search algorithm,
– Object detection metrics,
– Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN,
– Detect blood cells using Faster R-CNN application,
– Theoretical background for Single Shot Detector (SSD),
– Train your customs datasets using different models with TensorFlow Object Detection API
– Object Detection on images and videos,
– YOLOv2 and YOLOv3 background.
– Object detection from COCO dataset application using YOLOv4 model.
– YOLOv4 theoretical class
– Practical application for detecting Robots using a custom dataset (R2D2 and C3PO robots dataset) and YOLOv4 model
– Practical application for License Plate recognition converting the plates images in raw text format (OCR) with Yolov4, OpenCV and ConvNets
-Object detection with the latest state of the art YOLOv7.
–Face Mask detection application with YOLOv7
I have updated the course with a new chapter for Image Segmentation:
– I review the theory behind U-Net for image segmentation
– We develop an application for detecting brain tumors from MRI images using U-Net.
– We train models with U-Net and U-Net with attention mechanism.
You will find in this course a concise review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.
This new course represents a huge improvement of the previous course, however the previous course was very well qualified by the students, some of the inspiring comments are here:
* Maximiliano D’Amico (5 stars):Very interesting and updated course on YOLO!
* Stefan Lankester (5 stars):Thanks Carlos for this valuable training. Good explanation with broad treatment of the subject object recognition in images and video. Showing interesting examples and references to the needed resources. Good explanation about which versions of different python packages should be used for successful results.
* Shihab (5 stars):It was a really amazing course. Must recommend for everyone.
* Estanislau de Sena Filho (5 stars):Excellent course. Excellent explanation. It’s the best machine learning course for computer vision. I recommend it
* Areej AI Medinah (5 stars):The course is really good for computer vision. It consists of all material required to put computer vision projects in practice. After building a great understanding through theory, it also gives hands-on experience.
* Dave Roberto (5 stars):The course is completely worth it. The teacher clearly conveys the concepts and it is clear that he understands them very well (there is not the same feeling with other courses). The schemes he uses are not the usual ones you can see in other courses, but they really help much better to illustrate and understand. I would give eight stars to the course, but the maximum is five. It’s one of the few Udemy courses that has left me really satisfied.
Course Curriculum
Chapter 1: Setup
Lecture 1: Introduction
Lecture 2: Codes & Datasets
Lecture 3: Google Colaboratory
Lecture 4: Tensorflow 2.X GPU local install and setup
Chapter 2: Image Classification with ConvNets
Lecture 1: CNNs Chapter Intro
Lecture 2: Image Fundamentals
Lecture 3: First glance of CNNs – The Input Layer
Lecture 4: Convolution Operation
Lecture 5: Sparsity Connections and Parameter Sharing
Lecture 6: Depthwise Separable Convolution
Lecture 7: Padding and the output shape of a Conv Layer
Lecture 8: Conv2D Layer with Keras – Practical example
Lecture 9: Pooling Layer
Lecture 10: Fully Connected Layer
Lecture 11: ReLU and other activation functions
Lecture 12: Batch Normalization
Lecture 13: Calculating the number of training parameters
Lecture 14: Fashion MNIST Part 1
Lecture 15: Fashion MNIST Part 2
Lecture 16: Train a ConvNet – CIFAR10 – Part 1
Lecture 17: Train a ConvNet – CIFAR10 – Part 2
Lecture 18: Load images with Generators – Tensorflow 2.X
Lecture 19: Data Augmentation – CIFAR10
Lecture 20: Practical App – Detect Covid19 from X-ray images
Lecture 21: Pretrained models – VGG16
Lecture 22: ResNet Model
Lecture 23: ResNet50 with Keras
Lecture 24: Inception Model
Lecture 25: InceptionV3 with keras
Lecture 26: Vision Transformer (ViT) model
Lecture 27: Vision Transformer (ViT) app – Covid19
Chapter 3: Data Sources
Lecture 1: OpenImageV6 and Voxel 51
Lecture 2: Roboflow
Lecture 3: LabelImg
Chapter 4: Object Detection
Lecture 1: Object Detection Intro
Lecture 2: Object Detection – The beginnings
Lecture 3: Metrics in Object Detection
Lecture 4: Fast R-CNN model
Lecture 5: Faster R-CNN model
Lecture 6: Single Shot Detector (SSD) model
Lecture 7: Object Detection on Images and Videos with Tensorflow Object Detection API
Lecture 8: Train custom dataset with TF Object Detection API – BCCD – Part 1
Lecture 9: Train custom dataset with TF Object Detection API – BCCD – Part 2
Lecture 10: Yolov1 model
Lecture 11: Yolov2 model
Lecture 12: Yolov3 model
Lecture 13: Yolov4 model – Part 1
Lecture 14: Yolov4 model – Part 2
Lecture 15: Object Detection on images and videos with YOLOv4
Lecture 16: Train your custom dataset with YOLOv4 – Robot detector app
Lecture 17: License Plate Recognition with YOLOv4-OpenCV-CNNs-Part1
Lecture 18: License Plate Recognition with YOLOv4-OpenCV-CNNs-Part2
Lecture 19: License Plate Recognition with YOLOv4-OpenCV-CNNs-Part3
Lecture 20: License Plate Recognition with YOLOv4-OpenCV-CNNs-Part4
Lecture 21: YOLOv7-Object Detection on Images & Videos
Lecture 22: Face Mask detection with YOLOv7
Chapter 5: Image Segmentation
Lecture 1: U-Net for Image Segmentation
Lecture 2: U-Net for Brain Tumor Segmentation with Keras – Part 1
Lecture 3: U-Net for Brain Tumor Segmentation with Keras – Part 2
Lecture 4: The final class
Instructors
-
CARLOS QUIROS
Industrial Engineer and Data Scientist
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 0 votes
- 3 stars: 2 votes
- 4 stars: 1 votes
- 5 stars: 5 votes
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