SKU: 64851446985

Stalingrad '42: Southern Russia, June-December, 1942 2nd Edition

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Description

Stalingrad '42: Southern Russia, June-December, 1942 2nd EditionStalingrad 42 is a division level game on the Axis 1942 summer offensive towards Stalingrad and the Caucasus. Historically, this epic struggle lasted for 6 months and saw the Axis armies reach the Volga and the Caucasus Mountains. But Soviet resistance stiffened and final victory eluded the German army at Stalingrad and in the Caucasus. The ensuing November Soviet offensive trapped the Wehrmachts largest army (the 6th) at Stalingrad and marked the

Stalingrad ’42 is a division-level game on the Axis 1942 summer offensive towards Stalingrad and the Caucasus. Historically, this epic struggle lasted for 6 months and saw the Axis armies reach the Volga and the Caucasus Mountains. But Soviet resistance stiffened and final victory eluded the German army at Stalingrad and in the Caucasus.The ensuing November Soviet offensive trapped the Wehrmacht’s largest army (the 6th) at Stalingrad and marked the beginning of the end for Axis fortunes in WW2.

Stalingrad ’42 uses the same scale and nearly all the rules of Ukraine ’43. Many modifications have been made to improve the system and to show crucial features of the campaign. New rules include leaders, elite panzer divisions, planned operations, hidden Soviet buildup, and Army/Front offensive support.

With three maps and low unit density, the game delivers a grand view of the campaign, where decisions about movement and direction of attack have lasting effects that propel or curtail your future strategic plans. The effect is like watching a story unfold and noticing a growing emotional involvement with your forces and plans. In the end, whether in victory or defeat, players of Stalingrad ’42 will enjoy an epic gaming experience.

Scenarios

  • Campaign Game: June 28th - December 31st (34 turns)
  • Case Blue: June - August (14 turns)
  • Operation Uranus: November - December (7 turns)
  • Introductory Scenario: First 6 turns of Case Blue (uses one only map)
  • Battle of the Caucasus: August - November (16 turns) (uses only one map)

Features

  • Ease of play
  • Accurate Order of Battle
  • Detailed game maps
  • Rules for: Blitzkrieg warfare, Offensive Planning, Sea Movement, Leaders, City Battles, North Caucasus Volunteers, and hidden Soviet buildup

Sequence of Play Outline

A. Weather Phase

B. Axis Player Turn

     1. Initial Phase

     2. Movement Phase

     3. Combat Phase

     4. Recovery Phase

     5. Supply Phase

C. Soviet Player Turn (Identical to Axis Player Turn)

D. Victory Determination Phase

Little Saturn/Winter Storm adds a 5th scenario to Stalingrad ’42 covering the period from December 14th through February 5th, 1943. The scenario starts with the Soviets launching a major attack against the Italians along the Don River while Manstein's Operation Winter Storm to relieve Stalingrad is in progress.

The scenario uses all three maps (not included). Each turn represents 4 days. More than 50 new units included.

STALINGRAD '42 COMPONENTS

  • Two Full Map Sheets, Two Half Map Sheets
  • Three Countersheets (9/16" playing pieces)
  • Five Scenario Cards
  • Two Identical Player Aid Cards
  • One Rules Booklet
  • One Playbook
  • Two Dice

LITTLE SATURN/WINTER STORM COMPONENTS

  • One Half Countersheet
  • Two Setup Cards
  • Two (Identical) Player Aid Cards
  • One 4-page 11" x 17" Flyer of the Scenario Rules

Map scale: 1 hex = 10 miles

Time scale: 1 turn = 5-7 days

Unit scale: Divisions/Brigades/Regiments

Players: 2-4

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SKU: 64851446985

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Shannon
New York, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
New York, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
Lexington, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Louisville, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
San Leandro, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018

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