Bøger af Phil Barber

When his Polish ex-serviceman father denies paternity, after an extra-marital affair, the infant Phil is left with his poverty stricken mother in a 1950s West Yorkshire mill town. Overwhelmed by Victorian levels of hardship, abandonment and repeated pregnancies Annie develops catastrophic mental health problems. She releases the infant Phil to a distant orphanage. More moves follow each confusing separation take their toll on this spirited, imaginative boy who - at every turn - feels violently cast off and set adrift. In this memoir of life under the roof of 1960s orphanages Phil Barber has travelled with love and a wry humour into the dark earth of his tangled roots. Phil's story is a winding river of struggles for freedom and identity. This is not a bleak and sombre tale. Its a search for beauty and belonging among the oceanic pain of feeling exiled from society.

The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest.In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.