The topic for Volume I is "MAINLY MECHANICS, RADIATION, AND HEAT". According to the Foreword, this series of books is based on lectures given by Prof. Richard Feynman during his teaching at Caltech—one of the most prominent institutions in physics and my dream school. I failed to enroll in physics for my bachelor's degree due to poor performance on the university enrollment test (known as Gaokao in China). As a form of self-punishment, I chose computer and data science as my major, thinking it would be the subject I'd dislike most. Fortunately, I discovered an aptitude for both fields. With major breakthroughs in deep learning during 2017, 2020, and 2022, this field became increasingly attractive and promising. However, since ChatGPT's arrival, entry-level machine learning engineering positions have become scarce, even as machine learning becomes mainstream. Employers now typically require years of experience and primarily offer senior-level positions. As an international Asian male with Chinese citizenship, I've found myself stuck in the current job market. Meanwhile, many people are moving into AI agents—a direction that doesn't feel like real science to me (no offense), and one I don't see myself pursuing in the next decade. Deep learning, as of now, still lacks the strong theoretical foundation found in physics or mathematics; many aspects remain uncertain, including unexplained scaling laws. I've decided to return to studying physics because: 1. Physics teaches me a way to perceive the world, beyond just knowledge itself, 2. There's great potential in applying physics to improve deep learning research, including PINN, Neural Operators, energy-based systems, and quantum machine learning, opening more doors in deep learning research, and 3. Studying and reading help me stay calm while navigating this challenging job market.