Deep Learning and Image Recognition

Over the next decade, we can expect artificial intelligence (AI) to have a profound impact on our society. As AI technologies continue to advance and become more integrated into various industries, we will witness significant changes that are shaping the human being's future for the better. This paper reviews the major deep learning algorithms that have accomplished latest achievements on various fields such as manufacturing robots, smart assistants, automated financial investing, social media monitoring, marketing chat-bots and self-driving car. These algorithms are trained on large datasets of labeled images, where each image is associated with a set of labels or categories. This paper provides an overview of popular techniques for image recognition. It discusses the convolutional neural network (CNN), which is made up of multiple convolutional layers followed by pooling layers, dropout layers, batch normalization lays and fully connected layers. The paper addresses other techniques used in deep learning for image recognition include transfer learning, where a pre-trained model is refined on new datasets, and generative adversarial networks (GANs), which can generate realistic images by training two neural networks against each other. This paper highlights that deep learning has revolutionized the field of image recognition and has opened up new possibilities for many applications.