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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

Knocktober – Machine learning Competition, 21st Oct, Analytics Vidhya

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Analytics Vidhya Last Updated : 19 Oct, 2016
2 min read

Introduction

This friday it gets better than ever. Machine Learning war between Data Nerds only on Analytics Vidhya.

Knocktober – a machine learning competition starting on 21st Oct 2016. Gear up with all your tools and strategies because this time we bet you would plead if you had couple of more hours.

Knocktober is a power-packed and mindboggling competition that will leave you grasping for breath.

Come be part of this competition, that you will give you more reasons to love hackathons.

Date: 21 Oct 2016 to 23 Oct 2016

Mode: Online

Cash Prize: INR 90,000

Register Here

knocktober-side-banner

Who should join this competition?

Anyone who is well skilled in machine learning techniques, and want to improve his or her knowledge in data science. Hackathons are the fastest way to improve your machine learning knowledge and excel in your career.

 

How much is the fee for this competition?

There is NO FEES for participating in this event.  We believe that learning should not be obstructed due to financial commitments. Hence, you are invited.

 

Why should you participate in this competition?

We have very carefully designed this competition to provide you a competitive and enriching experience. Hackathons are becoming a popular way for individuals to  showcase their machine learning expertise and knowledge. Hackathon mania is a new fever amongst data scientists and machine learning enthusiasts.

 

For more details about the competition, Visit Here

 

A
Analytics Vidhya

Analytics Vidhya Content team

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