Discover @Ashish-Gore Open Source projects

Ashish Gore (@Ashish-Gore)

Ashish-Gore

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Global Rank 587,004 (Top 21 %)
Followers 36
Following 3
Registered over 4 years ago
Most used languages

Jupyter Notebook
57.1 %

HTML
28.6 %

R
14.3 %
Location 🇮🇳 India
Country Total Rank 24,081
Country Ranking

R
100

Jupyter Notebook
6,580

Data-Science-Case-Studies-And-Algorithms

ExcelR Assignments and Projects

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.

Forge-or-Real-Signature-Detection-using-Deep-Learning

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.

Jupyter Notebook

Certifications

Certificates and Accomplishments of Ashish Gore.

NLP-Top5-QA-Project-Deployment

Ashish-Gore

Ashish's Profile

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

https://ashishgore234.wixsite.com/ds-jobs-analysis

Jupyter Notebook

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

http://ec2-18-188-91-112.us-east-2.compute.amazonaws.com:8080/

Jupyter Notebook

Loan-Prediction-Project

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