Titlebook: Machine Learning Paradigms; Advances in Deep Lea George A. Tsihrintzis,Lakhmi C. Jain Book 2020 The Editor(s) (if applicable) and The Autho

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Machine Learning Paradigms978-3-030-49724-8Series ISSN 2662-3447 Series E-ISSN 2662-3455

發(fā)起

George A. Tsihrintzis,Lakhmi C. JainPresents recent advances in Deep Learning Theory and Applications.Includes theoretical advances as well as application areas.Written by experts in the field

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A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum Systritic (A2C). Then, the cart-pole balancing problem in OpenAI Gym environment is considered to implement the deep reinforcement learning methods. Finally, the performance of all methods are comparatively given on the cart-pole balancing problem. The results are presented by tables and figures.

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2662-3447 rts in the field.At the dawn of the 4.th. Industrial Revolution, the field of .Deep Learning. (a sub-field of .Artificial Intelligence. and .Machine Learning.) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplin

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Stock Market Forecasting by Using Support Vector Machinesndicators and macroeconomic variables. For evaluating the forecasting ability of SVM, we compare the results obtained by the proposed model with the actual stocks movements for a number of constituents of FTSE-100 in London.

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Survey on Deep Learning Techniques for Medical Imaging Application Arearformance level. This chapter highlights the primary deep learning techniques relevant to the medical imaging application area and provides fundamental knowledge of deep learning methods. Finally, this chapter ends by specifying the current limitation and future research directions.