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Reading list

Deploy an Image Classification Model Using Flask
Introduction to Computer Vision
Reading and Stacking Images Understanding Colorspaces Image Processing Edge Detection Blur Detection Image Operations HOG features SIFT features Camera Calibration Interesting Applications of OpenCV
Why Do We Need CNN? How Does Filters Work in CNN? Parameter Sharing and Local Connectivity in CNN Understanding CNN Architecture What is Pooling in CNN? Math Behind Convolutional Neural Networks Image Classification Tutorial using CNN Introduction to Separable Convolutions
Introduction to Transfer Learning
CNN Visualization
LeNet AlexNet VGG16 Implement VGG Dataloaders using Pytorch Implementing VGG16 using Dataloaders
Understanding Inception Modules
Understanding Residual Blocks Building Your Own Residual Block from Scratch Understand ResNet 34 Build ResNet34 from Scratch With Python! Different Versions of ResNet
Understanding the Architecture of DenseNet
Implement Crowd Counting using CSRNet
Introduction to Object Detection Bounding Box Evaluation: (Intersection over union) IOU Calculating IOU
Why do we need Region Based Convolulional Neural Network? Fast R-CNN and Faster R-CNN Building Social Distancting Tool using Faster R-CNN
Single Shot Detector SSD Custom Object Detection on the browser using TensorFlow.js YOLOv1 Other Versions of YOLO (v2 and v3) YOLOv3 YOLOv4 YOLOv5 YOLOv7 RetinaNet
DETR
Introduction to Face Detection Why is Face Alignment Important for Face Recognition? Implementing Face Detection
Introduction to Object Tracking Object Tracking Applications
Comprehensive Guide on Pose Estimation Implement Human Pose Estimation
Introduction to Image Segmentation Different Types of Image Segmentation Implementing Background removal Image Segmentation Algorithms
Unet DeepLabv3 Mask R-CNN Architecture
Build Your Own Video Classification Model
Introduction to Image Generation What are Generative models? Understanding GANs Implementing Texture Generation using GANs Better GAN Architectures
Few Shot Learning

The DataHour: Feature Engineering on Images using CNN

D
Dhruv Last Updated : 21 Jul, 2022
3 min read

Introduction

Eduardo Xamena is hosting a DataHour with us. He works as a Scientific Researcher at National Scientific and Technical Council, developing methods and architectures for information extraction and retrieval from enormous amounts of text. The history of the Argentinian Revolution and the number of complaints received by the Public Ministry of Salta are two application areas in which these techniques can be used (Argentina). Cleaning up text in OCR-acquired volumes and creating text representations that are appropriate for categorization processes. He will explain Feature Engineering on Images using Convolutional Neural Networks(CNN). Feeling energetic? Register Now!

The DataHour

BOOK YOUR SEAT NOW! 🛋️

About DataHour

Convolutional Neural Networks are a method for representing non-linear features in a variety of data samples, primarily pictures. By establishing a complex hierarchy of interpretive neural layers and other components that give a more robust collection of features for differentiating objects and characteristics seen on images, CNNs emulate the behaviour of human and animal brains. We can create exact categorization and object identification Machine Learning models for images with the addition of this additional knowledge before the fundamental dense neuronal layers.

In this DataHour, the fundamental ideas of CNN will be discussed along with various examples utilising Python and Tensorflow/Keras. In addition, certain cutting-edge designs will be discussed, along with the potential applications for applying them in software development.

About the Speaker

Eduardo Xamena holds a PhD in Computer Science, focusing on the structural analysis of mathematical models from chemical engineering and the propagation of relevance in topical networks. Currently, a Scientific Researcher is working on developing methodologies and architectures for large-scale text retrieval and extraction. Such tools’ application fields include the history of the Argentine revolution and the volume of complaints received by the Salta Public Ministry (Argentina). Carrying out text cleaning tasks in OCR acquired volumes and creating text representations suitable for classification procedures.

In addition, he is working on Deep Learning applications to image processing tasks, such as handwritten text recognition. He is currently a professor in the Computer Vision and Data Science chairs.

Prerequisites

The desire to learn about Deep learning and you’re well-versed with the basics of Python.

Who is this DataHour for?

  • Students and Freshers with interest in Data Science and Deep Learning.
  • Data science professionals and enthusiasts wanted to accelerate their career growth.

Any Questions

Drop us an email at [email protected], or you can chat with the speaker directly during the session.

Contact

To take advantage of this fantastic opportunity. Register for this DataHour here.

If you missed any of the past episodes of “The DataHour,” you may watch the recordings on our YouTube channel. You can read a summary of previous DataHour sessions on our blog by clicking here.

Connect

If you’re having trouble enrolling or would like to conduct a session with us. Contact us at [email protected].

BOOK YOUR SEAT NOW! 🛋️

D
Dhruv

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