SKU: 35161934296

Rolex Datejust ref. 1601 Steel/Gold Bezel - Black Dial - Jubilee bracelet

$2335.50 $2595.00
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Description

Rolex Datejust ref. 1601 Steel/Gold Bezel - Black Dial - Jubilee braceletFor sale at Debonar Watches: pre owned Rolex Datejust ref. 1601 in steel and yellow gold, with a beautiful black dial, and a Jubilee bracelet. The Rolex Datejust is considered the definitive watch: its design has barely changed in 60 years and is a familiar sight even to non enthusiasts. This Rolex has all the signature aesthetics of a Datejust, which include a fluted bezel, baton hour markers, and the Cyclops magnifying bubble over the date window.

For sale at Debonar Watches: pre-owned Rolex Datejust ref. 1601 in steel and yellow gold, with a beautiful black dial, and a Jubilee bracelet.

The Rolex Datejust is considered the definitive “watch”: its design has barely changed in 60 years and is a familiar sight even to non-enthusiasts. 

This Rolex has all the signature aesthetics of a Datejust, which include a fluted bezel, baton hour markers, and the Cyclops magnifying bubble over the date window.

BRAND ROLEX
MODEL DATEJUST
REFERENCE 1601
CASE MATERIAL STEEL
MOVEMENT AUTOMATIC
DIMENSION 36 MM
DIAL BLACK
BEZEL YELLOW GOLD
CRYSTAL PLEXIGLASS
BRACELET TYPE BRACELET
BRACELET MATERIAL JUBILEE
WRIST DIAMETER UP TO 18 CM
ORIGINAL BOX NO
ORIGINAL PAPERS NO
YEAR APPROX. THE '70s
CONDITION GOOD
PAYMENTS

1. Order fulfillment begins once the payment is credited to the Store's bank account or upon confirmation of payment via available online payment methods.

2. Available payment methods include:

a) bank transfers,

b) fast online payments,

c) payment by debit or credit card.

3. Card payments are processed via trusted payment operators who ensure the security of transactions.

4. Card data is not stored by the Store – it is processed only by the payment operator in accordance with applicable regulations and security standards (e.g., PCI DSS).

5. Transactions above 5000 EUR are not accepted by the Store via credit cards. In such cases, the customer should choose another payment method.

6. The order fulfillment time depends on product availability and ranges from 7 to 14 business days.

7. The customer is obliged to provide accurate data necessary for the order fulfillment. These data must correspond with the data provided during payment.

a) If the data from the payment do not match the data provided in the order, the transaction may be canceled by the Store's customer service.

b) The customer has the option to specify a different shipping address for the order; however, the order details, including the name and surname, must match the information of the person making the payment. For instance, if an order is placed by a customer named John Smith, the payment must also be made by John Smith, ensuring consistency between the order and payment details.

8. Sales are conducted exclusively via the Store's website available at debonarwatches.com.

9. Stationary sales are only available in exceptional situations, after prior arrangement with the Store's customer service.

If you have any query regarding a watch you are purchasing, please do contact a member of our team on Whatsapp +39 3381504670 or via email [email protected] and we’ll be happy to help.
DELIVERY

We will ship your order of stock items within 1-2 working days from time of order. If this is inconvenient please let us know preferred delivery date and we will schedule delivery at your convenience.

We will advise approximate delivery time of available to order watches prior to purchase.

Shipping to EU
Orders made within the EU will be shipped via DHL Express or Fedex Express with appropriate insurance.

International Shipping

Standard orders made outside the EU will be shipped via DHL or Fedex guarded delivery depending on value of items. We are not responsible for import duties and associated taxes so please check due fees in your Extra-EU country when ordering, please check it before ordering.

Estimated delivery time is approx 2-7 days subject to terms and conditions.
WARRANTY

Our pre-owned watches are accompanied by Full 12 month warranty (the 'Warranty') from the day you receive your watch, protecting your watch against manufacturing and mechanical defects, subject to the following terms and conditions.

Our warranty is subject to the same terms and conditions as the manufacturer’s warranty. The warranty does not cover theft or loss. Normal wear and tear or damage caused to the watch by accidents or mishandling/mistreatment are also excluded as well as damage caused due to submersion in water against the manufacturer’s guidance. Wear of the watch strap is not covered by the warranty.

In the event you decide to take your watch to a repairer or watchmaker without prior approval from Debonar Watches, we will not be held liable for any costs you may incur.

Any modification of a watch by addition of substitution of components which have not been provided by the manufacturer will also invalidate the warranty, as will evidence that the watch case has been opened by anyone other than our authorised watchmakers.

It is your responsibility to arrange adequate insurance cover for your watch whilst in transit back to us, and this cost will be at your expense.
RETURNS

We offer a 14 day return period on any watches purchased online or personally, in which you can exchange your watch for another product or receive a full refund.

The watch must be returned in the same condition and with the same accessories and paperwork/warranty cards as when you received it.

If your watch arrived with a return tag attached, this must still be attached to the watch and not tampered with.

You can return your watch by shipping it or in person at our office.

Please allow 7 working days for refunds to reach your account.

If you return a watch to us and it has been marked, scratched or the stickers have been removed, we may charge you up to 20 percent of the sales figure, subject to returned condition of a watch.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 35161934296

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4.9 ★★★★★
Based on 10 reviews
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Product Reviews
P
Verified Purchase
Par
Grantham, 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
Carnegie, 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
Dallas, 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
San Leandro, 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
Pawtucket, 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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