SKU: 37078463565

TUMI Dopp Kit Gift Set - Includes ATLAS [00:00 GMT], CONTINUUM [12:00 GMT] & Travel Toiletry Bag - .5 oz Eau de Parfum (Pack of 2)

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Description

TUMI Dopp Kit Gift Set - Includes ATLAS [00:00 GMT], CONTINUUM [12:00 GMT] & Travel Toiletry Bag - .5 oz Eau de Parfum (Pack of 2)Brand: TUMI Features: Men's Cologne Dopp Kit: This fragrance set features ATLAS [00: 00 GMT] and CONTINUUM [12: 00 GMT]; these colognes are complemented by a sophisticated TUMI travel toiletry bag Fresh & Earthy Perfume Fragrances: These men's colognes respectively feature fresh and earthy fragrance notes; this perfume has notes like sandalwood, smoked musk, and more Travel Toiletry Bag for Men: This men's dopp kit toiletry bag is 9" x 5" x 4",

Brand: TUMI

Features:

  • Men's Cologne Dopp Kit: This fragrance set features ATLAS [00:00 GMT] and CONTINUUM [12:00 GMT]; these colognes are complemented by a sophisticated TUMI travel toiletry bag
  • Fresh & Earthy Perfume Fragrances: These men's colognes respectively feature fresh and earthy fragrance notes; this perfume has notes like sandalwood, smoked musk, and more
  • Travel Toiletry Bag for Men: This men's dopp kit toiletry bag is 9" x 5" x 4", offering substantial space for essentials while fitting neatly in your luggage
  • About TUMI: When we make our luxurious and unique products, we strive for superior quality, design excellence, and innovation
  • Atlas [00.00 GMT] – The fragrance that transports him to his exclusive and personal escape. Atlas is undeniably bold, fresh and profoundly sophisticated. A fragrance that reveals the spirit of a man who chooses his own destiny with individuality and determination

model number: "PATU072

Part Number: PATU072

Details: Immerse yourself in luxury with the TUMI Dopp Kit Gift Set, a fragrance set with a duo of meticulously crafted colognes accompanied by a sleek TUMI dopp kit bag. This ensemble is the epitome of elegance and convenience, designed for the discerning gentleman who values sophistication and functionality in his grooming essentials.The dopp kit, crafted from high-quality materials, is not just a travel toiletry bag but a statement of style. It's spacious enough to hold all your grooming needs yet compact enough to fit in your carry-on. Its durable construction ensures it can withstand the rigors of travel, making it a reliable companion for both short business trips and extended adventures.Delve into a fragrance journey with TUMI ATLAS [00:00 GMT] and CONTINUUM [12:00 GMT]. ATLAS is a men's perfume fragrance that captures the essence of a worldly adventurer with its invigorating top notes of Italian bergamot and grapefruit, mingling with the warmth of amberwood. The heart of the fragrance reveals a spicy blend of labdanum, geranium, and ginger, grounded by base notes of vetiver, Indian sandalwood, and moss.CONTINUUM offers a more introspective experience. Its top notes of green mandarin and earthy amber are enveloped by the mystique of incense, while tobacco leaves and orris roots form the core, culminating in a base fragrance of oud, suede, and smoked musk.Whether you're a man of constant movement or one who appreciates moments of stillness, this TUMI fragrance selection is sure to match your rhythm. Both ATLAS and CONTINUUM embody the essence of the TUMI brand—empowerment, inspiration, and relentless pursuit of perfection.We design for you. Our commitment to exceptional quality and high-performing products has made TUMI the leading lifestyle and accessories brand that travelers depend on consistently. This is what we call the TUMI Difference.

EAN: 0850016678751

Package Dimensions: 9.7 x 4.8 x 4.7 inches

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SKU: 37078463565

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4.5 ★★★★★
Based on 28 reviews
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Verified Purchase
Par
Battle Creek, 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
Alexandria, 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
Battle Creek, 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
Waukegan, 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
Houston, 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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