Through exposure to the news and social media, you are probably familiar with the fact that machine learning has become one of the most exciting technologies of our time. While it may seem that machine learning has become the buzzword of our age, it is certainly not just hype. This exciting field opens up the way to new possibilities and has become indispensable to our daily lives.
Being exposed to practical code examples and working through example applications
of machine learning are great ways to dive into this field. If you want to become a machine learning practitioner or a better problem solver, or maybe you are even considering a career in machine learning research, then Python Machine Learning, Third Edition is for you! This book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.
Let’s hear Sebastian Raschka’s views on the benefits of TensorFlow 2.0 and the key takeaways from the new edition of his bestselling Python Machine Learning book.
I think we are still very, very far away from true AI, which is also known as strong artificial intelligence and artificial general intelligence. As of today, there is no clear path towards achieving artificial general intelligence or even predicting a rough time estimate for when we’ll get there.
I would argue that the closest we got to human-level performance in complex tasks is AlphaGo and AlphaStar, which are both based on reinforcement learning. However, a model like AlphaGo that can beat players in a complex board game cannot be compared to human-level thinking—it cannot even generalize to other, more or less related tasks without retraining the model from scratch.
Obviously, reinforcement learning allows us to solve very complex tasks, and in that sense, it is much more advanced than algorithms for predictive analytics. At the same time, reinforcement learning models are costly to train and are specific to particular tasks. Whether reinforcement learning will play a role in achieving artificial general intelligence remains to be seen when we eventually get there.Many readers and students told us how much they love the first 12 chapters as a comprehensive introduction to machine learning and Python's scientific computing stack. To keep these chapters relevant, we went back and updated these chapters to support the latest versions of NumPy, SciPy, and scikit-learn. Also, we refined several sections to improve the readability and explanations based on reader feedback.
Machine learning can be useful in almost every problem domain. We cover a lot of different subfields of machine learning in the book. My hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications. Also, using well-developed and maintained open source software makes machine learning very accessible to a wide audience of experienced programmers, as well as those who are new to programming.
Python Machine Learning, Third Edition is also different from a classic academic machine learning textbook due to its emphasis on practical code examples. However, I think this approach is highly valuable for both students and young researchers who are getting started in machine learning and deep learning. We heard from readers of previous editions that the book strikes a good balance between explaining the broader concepts supported with great hands-on examples, giving a light introduction to the mathematical underpinnings.About the Authors
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.
Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.
Posted 1 March 2021
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