About

Akash Antony is an IT Engineer specializing in Data Analytics Consulting at Qualcomm RF360 Europe. With over 2 years of experience in designing end-to-end data processing systems and building efficient deep learning models, he is a Machine Learning Engineer with expertise in various programming languages such as Python, C++, Java, and SQL. Akash's Master's thesis focused on the simulation of network attacks and the implementation of a deep neural network to work as a honeypot. As a Facebook scholar, he has acquired specific knowledge and extensive hands-on experience in Deep Neural Networks, PyTorch, Differential Privacy, Federated Learning, and Encryption in Deep Learning. He possesses a vast array of skills, including Numpy, Scikit-learn, Pandas, Matplotlib, OpenCV, Seaborn, SLAM, Kalman Filters, AWS Sagemaker, AWS EC2, Flask, LaTeX, Android Development, SAP ERP, SAP SCM, Google BigQuery, PySyft, and experience with Differential Privacy, Federated Learning, and Encryption in Deep Learning. Akash's expertise in Statistics/ML includes Logistic/Linear Regression, SVM, and Random Forests, while his DL Frameworks expertise includes Keras and PyTorch, with good experience in Autoencoders, CNN, RNNs, LSTMs, and GANs. He is also skilled in Computer Vision, with expertise in CNN Filters (Gradient and Sobel Filters), Canny Edge Detectors, Hough Transform, and Haar Cascades. Before joining Qualcomm, Akash worked as a Mentor and Project Reviewer for Deep Learning Nanodegree at Udacity, where he shared skills, knowledge, and expertise in topics like CNNs, RNNs, and GANs. He also evaluated completed projects by providing constructive feedback and provided tips to improve Python codes. As a Master Thesis Student at Otto-von-Guericke-Universität Magdeburg, Akash developed and built from scratch a Network Intrusion Detection System using Deep Learning for Multi-Class Attack Classification. He simulated attack packets (DDOS, Ping of Death) using HPing3, generated a dataset with network characteristics using Zeek security tool, pre-processed and visualized the dataset using Pandas, Numpy, Seaborn, and Scikit-learn, and implemented Deep Learning models for classification using CNN and Stacked Autoencoder, using Keras and Python. Akash's education history includes