ashwin-saxena Goto Github PK
Name: Ashwin Saxena
Type: User
Company: KIET Group of Institutions
Location: Delhi NCR
Name: Ashwin Saxena
Type: User
Company: KIET Group of Institutions
Location: Delhi NCR
All Algorithms explained in simple English Language with example and links to their implementation in various programming languages and other required resources.
Google Sign In using Firebase as a backend
OpenCV Python Neural Network Autonomous RC Car
A complete computer science study plan to become a software engineer.
This will have all the solutions to the competitive programming course's problems by Coding ninjas. Star the repo if you like it.
Cheat Sheets
Detecting Depression in Tweets using Baye's Theorem
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.
In this, I am attaching my code for building a CNN model to detect if a person is wearing face mask or not using the webcam of their PC.
Face recognition using KNN
Just read How_to_Run.txt file. You will understand how to train and run the face recognition project.
A Face Recognition Project USING KNN build from scratch by custom KNN algorithm.
Classifying images of Clothing into 10 Classes using Multi Class Classification
Facial Emotion Recognition on FER2013 Dataset Using a Convolutional Neural Network
Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture
IMDB Movie Review Analysis using Keras (1-D CNN)
Advanced Machine Learning group coursework
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
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Data-Driven Documents codes.
China tencent open source team.