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Next time we'll look at the importance and limitations of deep learning

Examples of deep learning applications Now in this Deep learning for beginners tutorial, let’s learn about Deep Learning applications: AI in Finance: The financial technology sector has already started using AI to save time, reduce costs, and add value. Deep learning is changing the lending industry by using more robust credit scoring. Credit decision-makers can use AI for robust credit lending applications to achieve faster, more accurate risk assessment, using machine intelligence to factor in the character and capacity of applicants. Underwrite is a Fintech company providing an AI solution for credit makers companies. underwrite.ai uses AI to detect which applicant is more likely to pay back a loan. Their approach radically outperforms traditional methods. AI in HR: Under Armour, a sportswear company revolutionizes hiring and modernizes the candidate experience with the help of AI. In fact, Under Armour Reduces hiring time for its retail stores by 35%. Under Armour faced a growing popularity interest back in 2012. They had, on average, 30000 resumes a month. Reading all of those applications and begin to start the screening and interview process was taking too long. The lengthy process to get people hired and on-boarded impacted Under Armour’s ability to have their retail stores fully staffed, ramped and ready to operate. At that time, Under Armour had all of the ‘must have’ HR technology in place such as transactional solutions for sourcing, applying, tracking and onboarding but those tools weren’t useful enough. Under armour choose HireVue, an AI provider for HR solution, for both on-demand and live interviews. The results were bluffing; they managed to decrease by 35% the time to fill. In return, the hired higher quality staffs. AI in Marketing: AI is a valuable tool for customer service management and personalization challenges. Improved speech recognition in call-center management and call routing as a result of the application of AI techniques allows a more seamless experience for customers. For example, deep-learning analysis of audio allows systems to assess a customer’s emotional tone. If the customer is responding poorly to the AI chatbot, the system can be rerouted the conversation to real, human operators that take over the issue. Apart from the three Deep learning examples above, AI is widely used in other sectors/industries.

Reinforcement Learning Reinforcement learning is a subfield of machine learning in which systems are trained by receiving virtual “rewards” or “punishments,” essentially learning by trial and error. Google’s DeepMind has used reinforcement learning to beat a human champion in the Go games. Reinforcement learning is also used in video games to improve the gaming experience by providing smarter bots. One of the most famous algorithms are: Q-learning Deep Q network State-Action-Reward-State-Action (SARSA) Deep Deterministic Policy Gradient (DDPG)

CNN is mostly used when there is an unstructured data set (e.g., images) and the practitioners need to extract information from it. For instance, if the task is to predict an image caption: The CNN receives an image of let’s say a cat, this image, in computer term, is a collection of the pixel. Generally, one layer for the greyscale picture and three layers for a color picture. During the feature learning (i.e., hidden layers), the network will identify unique features, for instance, the tail of the cat, the ear, etc. When the network thoroughly learned how to recognize a picture, it can provide a probability for each image it knows. The label with the highest probability will become the prediction of the network.

​Convolutional neural networks (CNN) CNN is a multi-layered neural network with a unique architecture designed to extract increasingly complex features of the data at each layer to determine the output. CNN’s are well suited for perceptual tasks.