• Stars
    star
    2
  • Language
    Jupyter Notebook
  • License
    GNU General Publi...
  • Created about 4 years ago
  • Updated almost 3 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. It is essential in preventing falsification of documents in numerous financial, legal, and other commercial settings. This is a comparative analysis of different already known deep learning architectures to check which of those performs the best on the classification. It was solely for offline handwritten signatures.

More Repositories

1

Data-Science-Case-Studies-And-Algorithms

ExcelR Assignments and Projects
R
10
star
2

AWS-Fundamentals-Specialization

About this Specialization - This specialization gives aspiring IT professionals an overview of the features, benefits, and capabilities of Amazon Web Services (AWS). As you proceed through these four interconnected courses, you will gain a more vivid understanding of core AWS services, key AWS security concepts, strategies for migrating from on-premises to AWS, and basics of building serverless applications with AWS. Additionally, you will have opportunities to practice what you have learned by completing labs and exercises developed by AWS technical instructors.
3
star
3

Certifications

Certificates and Accomplishments of Ashish Gore.
1
star
4

NLP-Top5-QA-Project-Deployment

HTML
1
star
5

Ashish-Gore

Ashish's Profile
1
star
6

Web-Scraping-using-Selenium-DS-job-market-Analysis

https://ashishgore234.wixsite.com/ds-jobs-analysis
Jupyter Notebook
1
star
7

Impact-Prediction-of-an-incident-Project-AWS

http://ec2-18-188-91-112.us-east-2.compute.amazonaws.com:8080/
Jupyter Notebook
1
star
8

Loan-Prediction-Project

HTML
1
star
9

Deeplearning.ai-Specialization

About this Specialization - In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow.
Jupyter Notebook
1
star