Biologists, too, have used AI—in the form of ‘artificial life’ (A-Life), which develops computer models of differing aspects of living organisms. This helps them to explain various types of animal behaviour, the development of bodily form, biological evolution, and the nature of life itself. Besides affecting the life sciences, AI has influenced philosophy. Many philosophers today base their accounts of mind on AI concepts. They use these to address, for instance, the notorious mind–body problem, the conundrum of free will, and the many puzzles regarding consciousness. However, these philosophical ideas are hugely controversial. And there are deep disagreements about whether any AI system could possess real intelligence, creativity, or life. Last, but not least, AI has challenged the ways in which we think about humanity—and its future. Indeed, some people worry about whether we actually have a future, because they foresee AI surpassing human intelligence across the board. Although a few thinkers welcome this prospect, most dread it: what place will remain, they ask, for human dignity and esponsibility?
Artificial Intelligence – A Very Short Introduction – Margaret A.Boden
Pattern recognition and machine learning - Christopher M.Bishop
Neural Networks - A visual introduction for beginners - Michael Taylor
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning and Neural Networks - Jeff Heaton
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Deep Learning with PyTorch - Vishnu Subramanian
An introduction to neural networks - Kevin Gurney & University of Sheffield
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning with Theano - Christopher Bourez
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Java Deep Learning Essentials - Yusuke Sugomori
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Deep Learning Cookbook - Indra den Bakker
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Coding Theory - Algorithms, Architectures and Application
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho