January 24, 2021

Top 10 Free Machine Learning Papers Every Student Researcher Must Read

I have made a list of this 10 research paper I believe very student and researcher in area of Artificial Intelligence and Machine learning must have.

You could learn about how the following is applied: Neural Network, Deep Learning, Convolutional Neural Network, Recurrent Neural Network, Learning algorithms and more

I have provided the links to download this papers for free:

I also found some really good papers that are not free and I’m trying to reach the authors to see if they could make it available to researchers based on some bargain. I’ll let you know once it’s done.

1. “Show and Tell: A Neural Image Caption Generator“.  
In this publication, the authors create a model for image captioning using deep recurrent neural network architecture by applying computer vision and machine translation.
Download this paper


2. “Deep Residual Learning for Image Recognition
This paper examines the benefits of residual networks which improves accuracy of prediction using increased depth.
Download this paper


3. “A Review of Deep Learning
This paper is a comprehensive review of Deep Learning as well as application of deep learning is various fields such as image recognition and processing, speech recognition, video and text processing.
Download this paper

4. “Computer Vision Based Date Fruit Grading System: Design and Implementation”This is a design and implementation research work for a prototype fruit grading system based on computer vision. The system performs feature extraction and then uses a classifier to grade the dates into three pre-defined classes.
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5. “Improving Quality Inspection of Food Products by Computer Vision
This Paper describes a system of adopting computer vision approach to the food industry. Image recognition is used to analyse the quality of different categories of food (bread, milk, pizza, chocolate etc) and assign quality ratings to them.
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6. “Machine Vision System for Food Grain Quality Evaluation: A review”
The research proposes the use of machine vision based systems as a replacement for visual methods of inspection.
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7. “Fast and Accurate Deep Neural Networks by Exponential Linear Units (ELU)
This research paper highlights the adoption of “exponential linear unit” as a means of  improving the learning process of deep neural networks. According to the authors, exponential linear units provides better performance than rectified linear unit (RELU) in terms of learning rate and generalization performance.
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8. “Application of Counterpropagation Networks”
This introduces a completely novel learning algorithm – counterpropagation as a better alternative the existing algorithms like backpropagation for neural networks.
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9. “Exploring Strategies for Training Deep Neural Networks
The paper focuses on experiments to analyze the performance of various training algorithms as well as relationship between the depth of the network and quality of the model.
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10. “Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
This publication explores a method of recognizing natural language building a Neural Network based model for handling different variation of speech including noise-filtering as well as accent and language variations.
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Disclaimer: This papers does not belong to me (kindsonthegenius.blogspot.com) but it’s provided by the owners for free. I simply provide links in this website(kindsonthegenius.blogspot.com) to the location of the publications and I bear no responsibility with respects to the use of this publications.

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