Study of deep learning approaches to face recognition and object detection YOLO

In this research, I have focused on deep learning approaches to face detection and recognition and object detection and recognition. This research has mainly focused on training the neural networks or other models with enough amounts of data so that it achieves desirable results. Starting with the basics of neural network where a neuron, the smallest unit in deep learning field, is defined and explained, I have elevated the research to the topic where I could recognize the face of a person or an object in an image. First the neural networks have been introduced in this research and then training of the neural networks has been attained with both the CPU and the GPU. In an algorithm called matrix form of back propagation, the use of multiple GPUs has been made along with CUDA kernel and cuBLAS library. Application of face recognition has been implemented using the pre trained model Facenet and Deep Convolutional Neural Networks. After analyzing neural networks and its facial recognition application, other approaches of deep learning have been given force to. I have used the library OpenCV along with deep learning approaches to implement face recognition, Image registration and YOLO Object Detection and Recognition with the tensor flow and keras environment supported by anaconda for python