This is a handbook containing all the advice and recommendations about learning physics I wished someone had told me when I was younger. It is neither a career guide nor a comprehensive textbook. What’s inside? - Understand why self-learning is an effective strategy. Learn why most university students never develop a deep understanding and what alternatives are possible. - Grasp the internal structure of physics. Learn how the fundamental theories of physics are connected and why physics works at all. - Develop an understanding of the landscape. Read bird's eye overviews that give a first taste of what the various theories of physics are all about. - Everything you need to get started. Read detailed reading and learning recommendations that allow you to carve out a personal learning path.

A master teacher presents the ultimate introduction to classical mechanics for people who are serious about learning physics "Beautifully clear explanations of famously 'difficult' things," -- Wall Street Journal If you ever regretted not taking physics in college -- or simply want to know how to think like a physicist -- this is the book for you. In this bestselling introduction to classical mechanics, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Challenging, lucid, and concise, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.

This book on the teaching and learning of physics is intended for college-level instructors, but high school instructors might also find it very useful.Some ideas found in this book might be a small 'tweak' to existing practices whereas others require more substantial revisions to instruction. The discussions of student learning herein are based on research evidence accumulated over decades from various fields, including cognitive psychology, educational psychology, the learning sciences, and discipline-based education research including physics education research. Likewise, the teaching suggestions are also based on research findings. As for any other scientific endeavor, physics education research is an empirical field where experiments are performed, data are analyzed and conclusions drawn. Evidence from such research is then used to inform physics teaching and learning.While the focus here is on introductory physics taken by most students when they are enrolled, however, the ideas can also be used to improve teaching and learning in both upper-division undergraduate physics courses, as well as graduate-level courses. Whether you are new to teaching physics or a seasoned veteran, various ideas and strategies presented in the book will be suitable for active consideration.

This reissued version of the classic text Basic Physics will help teachers at both the high-school and college levels gain new insights into, and deeper understanding of, many topics in both classical and modern physics that are commonly taught in introductory physics courses. All of the original book is included with new content added. Short sections of the previous book (174 in number) are labeled 'Features.' These Features are highlighted in the book, set forth in a separate Table of Contents, and separately indexed.Many teachers will value this book as a personal reference during a teaching year as various topics are addressed. Ford's discussions of the history and meaning of topics from Newton's mechanics to Feynman's diagrams, although written first in 1968, have beautifully withstood the test of time and are fully relevant to 21st-century physics teaching.

Everything you ever need to know about making it as a scientist. Despite your graduate education, brainpower, and technical prowess, your career in scientific research is far from assured. Permanent positions are scarce, science survival is rarely part of formal graduate training, and a good mentor is hard to find. In A Ph.D. Is Not Enough!, physicist Peter J. Feibelman lays out a rational path to a fulfilling long-term research career. He offers sound advice on selecting a thesis or postdoctoral adviser; choosing among research jobs in academia, government laboratories, and industry; preparing for an employment interview; and defining a research program. The guidance offered in A Ph.D. Is Not Enough! will help you make your oral presentations more effective, your journal articles more compelling, and your grant proposals more successful. A classic guide for recent and soon-to-be graduates, A Ph.D. Is Not Enough! remains required reading for anyone on the threshold of a career in science. This new edition includes two new chapters and is revised and updated throughout to reflect how the revolution in electronic communication has transformed the field.

What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

Calculus Made Easy by Silvanus P. Thompson and Martin Gardner has long been the most popular calculus primer. This major revision of the classic math text makes the subject at hand still more comprehensible to readers of all levels. With a new introduction, three new chapters, modernized language and methods throughout, and an appendix of challenging and enjoyable practice problems, Calculus Made Easy has been thoroughly updated for the modern reader.

'This is about gob-smacking science at the far end of reason ... Take it nice and easy and savour the experience of your mind being blown without recourse to hallucinogens' Nicholas Lezard, Guardian For most people, quantum theory is a byword for mysterious, impenetrable science. And yet for many years it was equally baffling for scientists themselves. In this magisterial book, Manjit Kumar gives a dramatic and superbly-written history of this fundamental scientific revolution, and the divisive debate at its core. Quantum theory looks at the very building blocks of our world, the particles and processes without which it could not exist. Yet for 60 years most physicists believed that quantum theory denied the very existence of reality itself. In this tour de force of science history, Manjit Kumar shows how the golden age of physics ignited the greatest intellectual debate of the twentieth century. Quantum theory is weird. In 1905, Albert Einstein suggested that light was a particle, not a wave, defying a century of experiments. Werner Heisenberg's uncertainty principle and Erwin Schrodinger's famous dead-and-alive cat are similarly strange. As Niels Bohr said, if you weren't shocked by quantum theory, you didn't really understand it. While "Quantum" sets the science in the context of the great upheavals of the modern age, Kumar's centrepiece is the conflict between Einstein and Bohr over the nature of reality and the soul of science. 'Bohr brainwashed a whole generation of physicists into believing that the problem had been solved', lamented the Nobel Prize-winning physicist Murray Gell-Mann. But in "Quantum", Kumar brings Einstein back to the centre of the quantum debate. "Quantum" is the essential read for anyone fascinated by this complex and thrilling story and by the band of brilliant men at its heart.

This textbook aims to provide a clear and concise set of lectures that take one from the introduction and application of Newton's laws up to Hamilton's principle of stationary action and the lagrangian mechanics of continuous systems. An extensive set of accessible problems enhances and extends the coverage.It serves as a prequel to the author's recently published book entitled Introduction to Electricity and Magnetism based on an introductory course taught sometime ago at Stanford with over 400 students enrolled. Both lectures assume a good, concurrent, course in calculus and familiarity with basic concepts in physics; the development is otherwise self-contained.A good introduction to the subject allows one to approach the many more intermediate and advanced texts with better understanding and a deeper sense of appreciation that both students and teachers alike can share.

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.