In it you will find popular algorithms and architectural solutions that are intuitive to any novice developer. The book covers important topics: from learning neural networks, language processing, feedback neural networks, to mathematical prerequisites and the history of artificial intelligence.

The main idea of this ISO is to use StarGAN (A type of GAN model) to perform training and testing on an emotion dataset resulting in a emotion recognition which can be generated by the valence arousal score of the 7 basic expressions. We have created an entirely new dataset consisting of 4K videos. This dataset consists of all the basic 7 types of emotions: Happy, Sad, Angry, Surprised, Fear, Disgust, Neutral. We have performed face detection and alignment followed by annotating basic valence arousal values to the frames/images in the dataset depending on the emotions manually. Then the existing StarGAN model is trained on our created dataset after which some manual subjects were chosen to test the efficiency of the trained StarGAN model.

Have you ever wondered how machine learning models work? Or what, exactly, goes on inside these models and whether we can trust them? Well, you're in luck, because I'm going to try to give you a very general overview of what XAI is and why we need it by answering a few common questions. After reading this, you should be able to understand the necessity of XAI and whether you need to start thinking about integrating it with your ML projects/products. Explainable AI (XAI) is a rather new field in machine learning (ML) in which researchers try to develop models that are able to explain the decision-making process behind ML models. XAI has many different research branches but, generally speaking, it either tries to explain the results of complex, black-box ML models or tries to incorporate interpretability into current ML architectures.

Continual learning is the unending process of learning new things on top of what has already been learned (Ring, 1994).Temporal Transition Hierarchies (TTHs) were developed to allow prediction of Markov-k sequences in a way that was consistent with the needs of a continual-learning agent (Ring, 1993).However, the algorithm could not learn arbitrary temporal contingencies.This paper describes Recurrent Transition Hierarchies (RTH), a learning method that combines several properties desirable for agents that must learn as they go.In particular, it learns online and incrementally, autonomously discovering new features as learning progresses.It requires no reset or episodes.It has a simple learning rule with update complexity linear in the number of parameters.