Kirolos T.

I have worked on many projects related to machine learning field and Embedded systems. I also have good research experience that allow me implement machine learning models and use it for new applications and make great Embedded system project. I've completed various online courses, including Deep learning Ai led by Prof. Andrew Ng, where I delved into both the theoretical foundations and practical applications of several essential topics in machine learning (ML) and natural language processing (NLP). My studies encompassed a range of techniques such as linear regression, logistic regression, locally weighted linear regression, principal component analysis (PCA), distribution fitting, neural networks with backpropagation, deep learning with a focus on unsupervised feature learning, autoencoders, convolutional neural networks (ConvNets), and dropout regularization. Additionally, I gained proficiency in linear support vector machines (SVM), K-means clustering, Gaussian mixture models, Gaussian processes, and explored various aspects of feature engineering, clustering, and preprocessing. Expertise: - C, C++, Java script, Matlab, Python. - OpenCV, Numpy, Pandas, Tensor Flow, Keras. - Artificial NN, Convolutional NN. - Simultaneous Detection and Segmentation, Transfer Learning (alexnet, googlenet, vgg16) - Linux based systems.