**2. Applications in industry**

Our GPS navigation systems' traffic predictions are made using machine learning algorithms [1–3]. Based on location- and velocity-related data acquired from our daily data feedback, they indicate busy routes. In order to suggest people we might know, ML software analyze our social media usage data, including the people we have connected with, the profiles we have visited, and our hobbies. Email spam filters use ML to deliver a more dependable and resilient solution. It guarantees that the filter is continually updated to recognize the most recent spammers' tricks. The AlphaGo program from Google DeepMind is among the most well-known applications of deep learning. Go is a board game that the system learned to play by competing against expert players. By making plays without the aid of a human, it was finally able to play at a level above that of the world champion Lee Sedol. One business adopting deep learning for self-driving cars is Tesla. Their deep learning program is utilized for semantic segmentation, road item detection and avoidance, monocular depth estimation, and image analysis. Amazon developed Alexa Conversations to give people a more organic engagement experience. Deep learning is used to ensure more natural interactions rather than ones that are forced or inflexible. On the basis of the presented photos, a deep learning (DL) system is asked to distinguish between dogs and cats. These photos will first be sent via the neural network's various levels. The subsequent layers will individually identify the distinctive features of dogs and cats, ultimately establishing the proper characteristics of each species. Finally, it will generate a result that accurately separates the photos into those of dogs and cats. Unlike the preceding machine learning example, the deep learning system in this case does not need structured data to categories the animals.
