Home > Computer & Internet > Computer science > Artificial intelligence > Machine learning > Machine Learning Algorithms in Depth
7%
Machine Learning Algorithms in Depth

Machine Learning Algorithms in Depth

          
5
4
3
2
1

Out of Stock


Premium quality
Premium quality
Bookswagon upholds the quality by delivering untarnished books. Quality, services and satisfaction are everything for us!
Easy Return
Easy return
Not satisfied with this product! Keep it in original condition and packaging to avail easy return policy.
Certified product
Certified product
First impression is the last impression! Address the book’s certification page, ISBN, publisher’s name, copyright page and print quality.
Secure Checkout
Secure checkout
Security at its finest! Login, browse, purchase and pay, every step is safe and secured.
Money back guarantee
Money-back guarantee:
It’s all about customers! For any kind of bad experience with the product, get your actual amount back after returning the product.
On time delivery
On-time delivery
At your doorstep on time! Get this book delivered without any delay.
Notify me when this book is in stock
Add to Wishlist

About the Book

Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems.

In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms including:

  • Monte Carlo Stock Price Simulation
  • Image Denoising using Mean-Field Variational Inference
  • EM algorithm for Hidden Markov Models
  • Imbalanced Learning, Active Learning and Ensemble Learning
  • Bayesian Optimization for Hyperparameter Tuning
  • Dirichlet Process K-Means for Clustering Applications
  • Stock Clusters based on Inverse Covariance Estimation
  • Energy Minimization using Simulated Annealing
  • Image Search based on ResNet Convolutional Neural Network
  • Anomaly Detection in Time-Series using Variational Autoencoders

Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you'll learn the fundamentals of Bayesian inference and deep learning. You'll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the tradeoffs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

About the book

Machine Learning Algorithms in Depth dives deep into the how and the why of machine learning algorithms. For each category of algorithm, you'll go from math-first principles to a hands-on implementation in Python. You'll explore dozens of examples from across all the fields of machine learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the time you're done reading, you'll know how major algorithms work under the hood--and be a better machine learning practitioner for it.

About the reader

For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus.

About the author

Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.


Best Sellers



Product Details
  • ISBN-13: 9781633439214
  • Publisher: Manning Publications
  • Publisher Imprint: Manning Publications
  • Height: 235 mm
  • No of Pages: 325
  • Spine Width: 21 mm
  • Width: 187 mm
  • ISBN-10: 1633439216
  • Publisher Date: 29 Aug 2023
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Weight: 386 gr


Similar Products

How would you rate your experience shopping for books on Bookswagon?

Add Photo
Add Photo

Customer Reviews

REVIEWS           
Click Here To Be The First to Review this Product
Machine Learning Algorithms in Depth
Manning Publications -
Machine Learning Algorithms in Depth
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Machine Learning Algorithms in Depth

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book
    Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!