SKU: 75545324508

H/H: Legiones Astartes: MKIII Tactical Squad

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Description

H/H: Legiones Astartes: MKIII Tactical SquadTactical squads are the mainstay of the Space Marine Legions and the force by which the Great Crusade has reconquered much of the galaxy. By the fighting power of thousands of superhuman warriors such as these, armed and armoured for battle in the harshest of environments and the deadliest of war zones, the enemies of Mankind have been crushed repeatedly. The Legion Tactical squad is a highly flexible infantry unit, able to attack or defend at will,

Tactical squads are the mainstay of the Space Marine Legions and the force by which the Great Crusade has reconquered much of the galaxy. By the fighting power of thousands of superhuman warriors such as these, armed and armoured for battle in the harshest of environments and the deadliest of war zones, the enemies of Mankind have been crushed repeatedly. The Legion Tactical squad is a highly flexible infantry unit, able to attack or defend at will, assault heavily fortified positions and take and hold strategic objectives, or simply slaughter an enemy comprehensively in almost any situation.

MKIII 'Iron' power armour dates from the wars of the Great Crusade, and is renowned for its use in full frontal assault due to its bulky but durable design.

This multipart plastic kit builds 20 Space Marine Legionaries, which you can field in games of Warhammer: The Horus Heresy as a Legion Tactical Squad of 20 models or two units of 10 models. Each of these Legionaries is armed with a bolter and bolt pistol, and you can optionally equip them with various additional components such as grenades, pouches, and a bayonet for melee fighting. You can also build up to two Legionaries with a Legion vexilla, two with a nuncio-vox, and two with an augury scanner.

Accessories and alternative head options are included to build a Legion Tactical Sergeant for each of your squads – the kit offers a variety of extra weapon options for these unit leaders, including plasma pistols, power swords, power fists, and lightning claws.

These models have no Legion markings, allowing Warhammer hobbyists to paint them in whichever colours they choose. However, the box includes a transfer sheet with 293 optional markings and iconography for the Sons of Horus and Imperial Fists Legions.

This multipart plastic kit contains 368 plastic components, and is supplied with 20x Citadel 32mm Round Bases and 1x Legiones Astartes Infantry Transfer Sheet. These miniatures are supplied unpainted and require assembly

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SKU: 75545324508

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4.0 ★★★★★
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Par
Natrona Heights, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Verified Purchase
Richard Hackathorn
Phoenix, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
West Palm Beach, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Charlottesville, 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
T
Verified Purchase
Tommy Jonsson
Louisville, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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