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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: Bias and Fairness in NLP

D
Dhruv Last Updated : 22 Jul, 2022
2 min read

Introduction

We are excited to have Shantam Saxena, who will host DataHour on our platform. He is a Data Scientist @Mckinsey & Company with over 5 years of experience, primarily working on NLP and related problems. He is currently pursuing a master’s degree in artificial intelligence at IIT Jodhpur and will begin his research on graph neural networks. Shantam enjoys writing poetry in his spare time and is currently dabbling in photography, primarily street style. He will discuss Bias and Fairness in NLP. Excited? Register Now!

The DataHour

BOOK YOUR SEAT NOW! 🛋️

About DataHour

Biases are stereotyped/unjust associations that a model encodes about protected attributes or poor model performance for specific groups. As a result, bias mitigation refers to the process of reducing the severity of stereotypical/unjust associations and disparate model performance.

Fairness is concerned with ensuring that a model performs equitably for all groups of people in terms of protected characteristics.

This DataHour will provide an overview of how biases can occur in NLP systems and why it is critical to identify them. The session will also cover techniques for identifying and mitigating these biases and how to estimate fairness.

About Speaker

Shantam currently works at Mckinsey and Company and has over 5 years of experience working primarily with NLP and related problems. He is currently pursuing a master’s degree in artificial intelligence at IIT Jodhpur and will begin his research on graph neural networks.

Shantam enjoys writing poetry in his spare time and is currently dabbling in photography, primarily street style.

Prerequisites

Passion for learning Data Science and familiarity with ML and NLP.

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 for The DataHour

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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