Tomarc The Barbarian – Atari 2600 classic video game. Xonox game cartridge only Great condition!!! Tested and works like new.
———This game is fully cleaned, tested & working. Includes the Disc/Cartridge Only. May have some minor scratches/scuffs.This description was last updated on October 28th, 2020.
Sale!
TOMARC THE BARBARIAN – Atari 2600 Game
$444.97
-24%In stock
Condition:Used
We have a 121-day warranty period for all of our products. If you are experiencing any issues or aren’t satisfied with your item within 121 days, contact us and get your issue resolved. It’s that simple! View our full Return policy page here.
Every order is eligible for free shipping (5-7 business days) to any address in the United States. Orders to Canada are available for free shipping (9-12 business days) over $120! Expedited Shipping of 3-5 Business days is available in checkout. We ship worldwide! Shipping is via UPS, USPS or Fedex. We ship from Allentown, PA.
It’s an excellent book which explains why of things. What could make it even better are some numerical examples and some sample R code. Also, Kindle version has TOC(table-of-contents) not hyperlinked makes it cumbersome to navigate. Regardless, it’s both a solid text & reference
MagnÃfico libro. Lo utilizo como referencia. Claro y práctico. Inumerables referencias a aplicaciones prácticas. Muy bien documentado. Lo recomiendo sin duda.
One of the best book on this topic: comprehensive, practical and well written.
muy claro y detallado. A diferencia de otros libros que son meramente aplicados, este libro cuenta con el desarrollo y explicación metamática que un cientÃfico necesita
Some parts are repeating and very detailed. And something seems to be wrong in definition of Adjusted Cosine similarity in section 2.3.3
The authoritative book on recommender systems research, algorithms and system design. It lists a lot of the modern achievements in the space, and organizes and describes the math extremely well.
I’m only at page 50 so far, but I LOVE this book! It’s a great balance of theory and practical advice. I’ve been applying what I’ve learned by building some simple recommender systems using Python as I follow the textbook. I like some of the subtle details the author points out. For example, if you are building a simple neighborhood user-based collaborative filter system, you can find nearest neighbors by computing the Pearson similarity between user rows. For each row, you need to compute the mean rating. The author points out two ways this mean can be computed:(1) Compute the row mean of each user(2) Compute the row mean of each user but only at columns such that both users have values in those columnsThe Pearson correlation coefficient is defined to use option (2), but you can save computational time by using (1). Also, (1) might make more sense if (2) only yields very few values to estimate the mean for each user.It’s subtle points like these I feel give me the confidence to grow my skillset in this area. I definitely recommend (no pun intended) to buy this book!
Great book and content.
very complete book on recommender systems in nearly 500 pages of lucid writing.Almost every major topic is studied in detail. Matrix factorization material in the bookis lovely. The book can be helpful to both newcomers and advanced readers.
Great book.