SKU: 54881643121

2022-23 Upper Deck Skybox Metal Universe Hobby Box

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Description

2022-23 Upper Deck Skybox Metal Universe Hobby BoxEach 2022 23 Upper Deck Skybox Metal Universe Hockey Hobby Box contains 15 packs with 7 cards in each pack! Skybox Metal Universe sports a 200 card base set, which breaks down as follows: 100 Veteran Cards 50 Rookie Cards 50 Veteran Superstar Cards Grab at least 1 Rookie and 1 Veteran Superstars card per pack, on average, and keep an eye out for a bevy of numbered parallels, including 1 of 1 Gold PMG (Precious Metal Gems) and autograph parallels!

Each 2022-23 Upper Deck Skybox Metal Universe Hockey Hobby Box contains 15 packs with 7 cards in each pack!

Skybox Metal Universe sports a 200-card base set, which breaks down as follows:

  • 100 Veteran Cards
  • 50 Rookie Cards
  • 50 Veteran Superstar Cards

Grab at least 1 Rookie and 1 Veteran Superstars card per pack, on average, and keep an eye out for a bevy of numbered parallels, including 1-of-1 Gold PMG (Precious Metal Gems) and autograph parallels! Additionally, collect the 1998-99 Metal Universe Retros, a 50-card design variation featuring the top rookies from the base set. Each card in this collection sports a unique background featuring original artwork from the 1998-99 Metal Universe base set!

Collect the Skybox Premium Prospects and 1998-99 Skybox Premium Prospects sets featuring the top 2022-23 rookie prospects. The latter is a design variation inspired by the 1998-99 Skybox Premium Rookies – Star Rubies set. Look for low-#’d and/or autograph parallels of both sets.

Look for new insert sets Flash the Glove, Aspects, Rookie Aspects and Bottle Rockets featuring the top veterans and rookies from the NHL. Make sure to look out for Gold and Autograph parallels of these insert sets.

Find the new awesome PETG insert The House and returning fan-favorite insert Palladium, each featuring top active stars and rookies. Grab at least 1 PETG card per box, on average, and look for low-#’d Spectrum parallels of both insert sets.

Look for rare Hot Shots die-cut cards featuring the game’s top players as well as stunning Metal-X cards.

The uniquely-designed and super rare Jambalaya insert boasts many of the best players in the game today.

BOX BREAK

  • 5 Aspects, Bottle Rockets, Flash the Glove and/or Rookie Aspects Cards
  • 3 Skybox Premium Prospects Cards
  • 3 1998-99 Metal Universe Retros Cards
  • 1 The House, Palladium and/or Hardware PETG Card
  • 1 All-Starring, Jarring or Metal Ore Card
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    SKU: 54881643121

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    4.6 ★★★★★
    Based on 30 reviews
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    Par
    Lake Worth, 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.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on December 20, 2024
    R
    Verified Purchase
    Richard Hackathorn
    Pawtucket, 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.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on February 26, 2022
    A
    Verified Purchase
    Amazon Customer
    Los Angeles, 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
    Natrona Heights, US
    ★★★★★ 5
    Very useful book
    Format: Paperback
    I use it for the machine learning class I teach.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on May 3, 2026
    T
    Verified Purchase
    Tommy Jonsson
    Omaha, 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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