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This e-book of the bestselling and extensively acclaimed Python Machine Studying sequence is a complete information to machine and deep studying utilizing PyTorch s easy to code framework.
Buy of the print or Kindle e-book features a free eBook in PDF format.
Key Options:
– Study utilized machine studying with a strong basis in principle
– Clear, intuitive explanations take you deep into the idea and follow of Python machine studying
– Absolutely up to date and expanded to cowl PyTorch, transformers, XGBoost, graph neural networks, and greatest practices
Guide Description:
Machine Studying with PyTorch and Scikit-Study is a complete information to machine studying and deep studying with PyTorch. It acts as each a step-by-step tutorial and a reference you may maintain coming again to as you construct your machine studying methods.
Filled with clear explanations, visualizations, and examples, the e-book covers all of the important machine studying methods in depth. Whereas some books train you solely to observe directions, with this machine studying e-book, we train the ideas permitting you to construct fashions and functions for your self.
Why PyTorch?
PyTorch is the Pythonic option to be taught machine studying, making it simpler to be taught and easier to code with. This e-book explains the important components of PyTorch and tips on how to create fashions utilizing well-liked libraries, comparable to PyTorch Lightning and PyTorch Geometric.
Additionally, you will find out about generative adversarial networks (GANs) for producing new knowledge and coaching clever brokers with reinforcement studying. Lastly, this new version is expanded to cowl the newest tendencies in deep studying, together with graph neural networks and large-scale transformers used for pure language processing (NLP).
This PyTorch e-book is your companion to machine studying with Python, whether or not you are a Python developer new to machine studying or need to deepen your data of the newest developments.
What You Will Study:
– Discover frameworks, fashions, and methods for machines to be taught from knowledge
– Use scikit-learn for machine studying and PyTorch for deep studying
– Practice machine studying classifiers on photographs, textual content, and extra
– Construct and practice neural networks, transformers, and boosting algorithms
– Uncover greatest practices for evaluating and tuning fashions
– Predict steady goal outcomes utilizing regression evaluation
– Dig deeper into textual and social media knowledge utilizing sentiment evaluation
Who this e-book is for:
In case you have a superb grasp of Python fundamentals and need to begin studying about machine studying and deep studying, then that is the e-book for you. That is a vital useful resource written for builders and knowledge scientists who need to create sensible machine studying and deep studying functions utilizing scikit-learn and PyTorch.
Earlier than you get began with this e-book, you may want a superb understanding of calculus, in addition to linear algebra.
Desk of Contents
– Giving Computer systems the Potential to Study from Knowledge
– Coaching Easy Machine Studying Algorithms for Classification
– A Tour of Machine Studying Classifiers Utilizing Scikit-Study
– Constructing Good Coaching Datasets – Knowledge Preprocessing
– Compressing Knowledge through Dimensionality Discount
– Studying Greatest Practices for Mannequin Analysis and Hyperparameter Tuning
(N.B. Please use the Learn Pattern choice to see additional chapters)
From the Writer
Key Subjects: Parallelizing Neural Community Coaching with PyTorch Going Deeper – The Mechanics of PyTorch Classifying Photos with Deep Convolutional Neural Networks Modeling Sequential Knowledge Utilizing Recurrent Neural Networks Generative Adversarial Networks for Synthesizing New Knowledge …and extra!
What’s new on this PyTorch e-book from the Python Machine Studying sequence?
We gave the third version of Python Machine Studying a giant overhaul by changing the deep studying chapters to make use of the newest model of PyTorch. We additionally added brand-new content material, together with chapters targeted on the newest tendencies in deep studying. We stroll you thru ideas comparable to dynamic computation graphs and automated differentiation. Moreover, we’ve launched a well-liked adversarial coaching regime for neural networks that can be utilized to generate new, realistic-looking photographs.
What’s new:
New content material to cowl the newest model of PyTorch and its options Introduction to libraries together with PyTorch Lightning and Hugging Face transformers Addition of two cutting-edge machine studying methods: transformers and graph neural networks
What are the important thing takeaways from Machine Studying with PyTorch and Scikit-Study?
This e-book takes you on a journey from the origins of machine studying to the newest deep studying architectures. By conceptual and sensible examples, you may develop a repertoire of methods that let you clear up a variety of predictive modeling duties, together with tabular, picture, and textual content knowledge.
PyTorch is a really highly effective and versatile device, and deep studying naturally requires very versatile constructing blocks. Therefore, PyTorch can generally be very verbose in comparison with conventional machine studying libraries comparable to scikit-learn. On this e-book, we clarify how PyTorch works and canopy all of the important components. Nonetheless, we additionally concentrate on code readability to make sure you don’t get overwhelmed.
The e-book takes a deep dive into the underlying strategies and doesn’t draw back from explaining basic deep studying architectures and ideas from scratch. Our goal is to show you deep studying and see how one can put it into follow utilizing PyTorch moderately than the opposite means round.
What makes this e-book totally different from different books on PyTorch?
We put plenty of thought and care into organizing the overall construction of the e-book, the move of subjects, and the way the chapters construct on one another. This consists of the transition from one chapter explaining neural networks by implementing them from scratch in NumPy to a different chapter explaining tips on how to use PyTorch to make this extra handy.
There are a lot of nice books on machine studying and deep studying on the market. Nonetheless, from a few years of instructing and interacting with college students, we heard that many books do not embody hands-on examples that assist readers to place these into follow. Different books have a robust concentrate on code examples on the expense of explanations. Machine Studying with PyTorch and Scikit-Study strikes a superb stability between ideas, principle, and follow and takes benefit of synergistic results when explaining new strategies.
Python Machine Studying by Instance 4E
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Know-how Used
PyTorch, scikit-learn PyTorch TensorFlow, scikit-learn PyTorch, TensorFlow, pandas, NumPy, scikit-learn
Reader Data Stage
Newbie to Intermediate Intermediate to Superior Newbie to Intermediate Newbie to Intermediate
New Subjects
New content material on transformers, gradient boosting, and GNNs New content material on diffusion fashions, recommender methods, cellular deployment, Hugging Face, and GNNs Revised and expanded to incorporate GANs and reinforcement studying Revised with PyTorch builds, expanded greatest practices, and new content material on LLMs and multimodal fashions
Writer : Packt Publishing
Publication date : Feb. 25 2022
Language : English
Print size : 774 pages
ISBN-10 : 1801819319
ISBN-13 : 978-1801819312
Merchandise weight : 1.31 kg
Dimensions : 19.05 x 4.45 x 23.5 cm
Greatest Sellers Rank: #155,972 in Books (See Prime 100 in Books) #8 in Speech & Audio Processing #45 in AI Laptop Arithmetic #56 in AI Skilled Programs
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