Image recognition algorithm based on hybrid deep learning

As we embrace the information age, our lives have experienced revolutionary transformations. With the continuous advancement of computer technology, data sharing and information exchange have become increasingly robust. Consequently, the widespread adoption of hybrid deep learning algorithms has been prioritized. The emergence of deep learning is intricately linked to the progress of artificial intelligence, with deep learning serving as a tangible manifestation of AI. Deep learning algorithms are an important part of the field of robotics research and development. In image recognition, deep learning algorithms are playing an irreplaceable role. Based on the technological breakthrough of convolutional neural network, deep learning is unique in image recognition. In addition, aspects such as speech recognition, body function monitoring, and sports data analysis all have deep learning, and they are advancing all the way with unstoppable development momentum. Of course, with the current development of computer technology, image data also shines on its basis. Not only has it achieved a substantial surpass in quantity, but its types and styles are coming out in a variety of colors. On the basis of this series of developments, we are faced with a problem: traditional recognition algorithms cannot meet our next development requirements. Therefore, a new type of algorithm to deal with this problem has emerged: the research of image recognition algorithms based on hybrid deep learning. In the research of this article, we will focus on comparing its various algorithms to find out their advantages and disadvantages. So as to promote the further development of image recognition.