SKU: 65870215486

Mysterium

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Description

MysteriumIn the 1920s, Mr. MacDowell, a gifted astrologer, immediately detected a supernatural being upon entering his new house in Scotland. He gathered eminent mediums of his time for an extraordinary sance, and they have seven hours to make contact with the ghost and investigate any clues that it can provide to unlock an old mystery. Unable to talk, the amnesiac ghost communicates with the mediums through visions, which are represented in the game by

In the 1920s, Mr. MacDowell, a gifted astrologer, immediately detected a supernatural being upon entering his new house in Scotland. He gathered eminent mediums of his time for an extraordinary séance, and they have seven hours to make contact with the ghost and investigate any clues that it can provide to unlock an old mystery.

Unable to talk, the amnesiac ghost communicates with the mediums through visions, which are represented in the game by illustrated cards. The mediums must decipher the images to help the ghost remember how he was murdered: Who did the crime? Where did it take place? Which weapon caused the death? The more the mediums cooperate and guess well, the easier it is to catch the right culprit.

In Mysterium, a reworking of the game system present in Tajemnicze Domostwo, one player takes the role of ghost while everyone else represents a medium. To solve the crime, the ghost must first recall (with the aid of the mediums) all of the suspects present on the night of the murder. A number of suspect, location and murder weapon cards are placed on the table, and the ghost randomly assigns one of each of these in secret to a medium.

Each hour (i.e., game turn), the ghost hands one or more vision cards face up to each medium, refilling their hand to seven each time they share vision cards. These vision cards present dreamlike images to the mediums, with each medium first needing to deduce which suspect corresponds to the vision cards received. Once the ghost has handed cards to the final medium, they start a two-minute sandtimer. Once a medium has placed their token on a suspect, they may also place clairvoyancy tokens on the guesses made by other mediums to show whether they agree or disagree with those guesses.

After time runs out, the ghost reveals to each medium whether the guesses were correct or not. Mediums who guessed correctly move on to guess the location of the crime (and then the murder weapon), while those who didn't keep their vision cards and receive new ones next hour corresponding to the same suspect. Once a medium has correctly guessed the suspect, location and weapon, they move their token to the epilogue board and receive one clairvoyancy point for each hour remaining on the clock. They can still use their remaining clairvoyancy tokens to score additional points.

If one or more mediums fail to identify their proper suspect, location and weapon before the end of the seventh hour, then the ghost has failed and dissipates, leaving the mystery unsolved. If, however, they have all succeeded, then the ghost has recovered enough of its memory to identify the culprit.

Mediums then group their suspect, location and weapon cards on the table and place a number by each group. The ghost then selects one group, places the matching culprit number face down on the epilogue board, picks three vision cards — one for the suspect, one for the location, and one for the weapon — then shuffles these cards. Players who have achieved few clairvoyancy points flip over one vision card at random, then secretly vote on which suspect they think is guilty; players with more points then flip over a second vision card and vote; then those with the most points see the final card and vote.

If a majority of the mediums have identified the proper suspect, with ties being broken by the vote of the most clairvoyant medium, then the killer has been identified and the ghost can now rest peacefully. If not, well, perhaps you can try again...

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SKU: 65870215486

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Amazon Customer
Whiting, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Lowell, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Tommy Jonsson
Houston, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
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Moses Kayanda
Massapequa, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
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Gabe Rigall
Battle Creek, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
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Reviewed in the United States on February 26, 2022

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