I'm a passionate software developer with expertise in full-stack development, cloud technologies, and modern web frameworks. I love building innovative solutions that solve real-world problems and create meaningful user experiences.
Email: mohak109@gmail.com
Phone: +91 9529258921
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.
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.
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.
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.
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.
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.