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Unlock trendy machine studying and deep studying strategies with Python through the use of the most recent cutting-edge open supply Python libraries.
Key Options
Second version of the bestselling ebook on Machine Studying A sensible strategy to key frameworks in knowledge science, machine studying, and deep studying Use probably the most highly effective Python libraries to implement machine studying and deep studying Get to know the perfect practices to enhance and optimize your machine studying programs and algorithms
E book Description
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Writer’s Observe: This version from 2017 is outdated and isn’t suitable with TensorFlow 2 or any of the latest updates to Python libraries. A brand new third version, up to date for 2020 and that includes TensorFlow 2 and the most recent in scikit-learn, reinforcement studying, and GANs, has now been printed.
Machine studying is consuming the software program world, and now deep studying is extending machine studying. Perceive and work on the slicing fringe of machine studying, neural networks, and deep studying with this second version of Sebastian Raschka’s bestselling ebook, Python Machine Studying. Utilizing Python’s open supply libraries, this ebook gives the sensible information and strategies it’s good to create and contribute to machine studying, deep studying, and trendy knowledge evaluation.
Totally prolonged and modernized, Python Machine Studying Second Version now consists of the favored TensorFlow 1.x deep studying library. The scikit-learn code has additionally been absolutely up to date to v0.18.1 to incorporate enhancements and additions to this versatile machine studying library.
Sebastian Raschka and Vahid Mirjalili’s distinctive perception and experience introduce you to machine studying and deep studying algorithms from scratch, and present you easy methods to apply them to sensible business challenges utilizing practical and attention-grabbing examples. By the tip of the ebook, you’ll be prepared to fulfill the brand new knowledge evaluation alternatives.
For those who’ve learn the primary version of this ebook, you’ll be delighted to discover a stability of classical concepts and trendy insights into machine studying. Each chapter has been critically up to date, and there are new chapters on key applied sciences. You’ll have the ability to study and work with TensorFlow 1.x extra deeply than ever earlier than, and get important protection of the Keras neural community library, together with updates to scikit-learn 0.18.1.
What You Will Be taught
Perceive the important thing frameworks in knowledge science, machine studying, and deep studying Harness the ability of the most recent Python open supply libraries in machine studying Discover machine studying strategies utilizing difficult real-world knowledge Grasp deep neural community implementation utilizing the TensorFlow 1.x library Be taught the mechanics of classification algorithms to implement the perfect device for the job Predict steady goal outcomes utilizing regression evaluation Uncover hidden patterns and constructions in knowledge with clustering Delve deeper into textual and social media knowledge utilizing sentiment evaluation
Who this ebook is for
If you already know some Python and also you need to use machine studying and deep studying, choose up this ebook. Whether or not you need to begin from scratch or prolong your machine studying information, that is a vital and unmissable useful resource. Written for builders and knowledge scientists who need to create sensible machine studying and deep studying code, this ebook is right for builders and knowledge scientists who need to educate computer systems easy methods to study from knowledge.
ASIN : 1787125939
Writer : Packt Publishing
Publication date : Sept. 20 2017
Version : 2nd ed.
Language : English
Print size : 622 pages
ISBN-10 : 9781787125933
ISBN-13 : 978-1787125933
Merchandise weight : 1.14 kg
Dimensions : 19.05 x 3.58 x 23.5 cm
Finest Sellers Rank: #1,404,469 in Books (See Prime 100 in Books) #414 in Enterprise Intelligence Instruments #753 in Synthetic Intelligence Textbooks #1,074 in Python (Books)
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