Hi! I'm Himanshu Beniwal

I’m a graduate student, pursuing the Masters of Technology degree in Computer Science and Technology. I am passionate about learning and implementing machine learning and artificial intelligence algorithms in real-world situations.

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Contact me here!

Email: mail.himanshubeniwal@gmail.com

Phone: +91 9034516078

What i do

My interests ❤

Data Science

Data Visualization

Business Analysis

Machine Learning

Deep Learning

Computer Vision


Azure Machine Learning

Coding for Fun! 😎


Checkout a few of my works


Autonomous Driving System simulation using Deep Q-Learning and vehicle control in CARLA

Simulating a self-driving car using graphical components in a 2D map using a combination of Q-Learning and Neural Networks with backpropagation and simulation in 3D town of CARLA simulator with kinematic and dynamic modelling in lateral and longitudinal control of car.

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Image Captioning using CNN & RNN

In this project we combine Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) knowledge to build a deep learning model that produces captions given an input image. Image captioning requires that you create a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language.

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Distributed Assessment of Closeness Centrality Ranking in Large-Scale Networks

Observed the complexity of traditional closeness centrality rankings with the comparison to Distributed Rankings in B.A., E.R., Synthetic and Real-world networks ranging from 1,000 nodes to 2 lakh nodes. Observed the improvements in the proposed method, taking Pearson, Kendall’s and Spearman Correlation coefficient.

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Facial Keypoint Detections using Computer Vision

Combined knowledge of computer vision techniques and deep learning architectures to build a facial keypoint detection system. Facial keypoints include points around the eyes, nose, and mouth on a face and are used in many applications. These applications include: facial tracking, facial pose recognition, facial filters, and emotion recognition.

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Handwritten Digit Recognition using Machine Learning

With the use of deep learning and machine learning, human effort can be reduced in recognizing, learning, predictions and many more areas. This article presents recognizing the handwritten digits (0 to 9) from the famous MNIST dataset, comparing classifiers like KNN, PSVM, NN and convolution neural network on basis of performance, accuracy, time, sensitivity, positive productivity, and specificity with using different parameters with the classifiers.

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Landmark Detection & Robot Tracking (SLAM)

SLAM (Simultaneous Localization and Mapping) was implemented for a 2 dimensional world! we combine what you know about robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features.

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