SKU: 19442098076
ultra bike gravel

ultra bike gravel Ceres GT Select 5.1

$18.24 $20.27
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Description

ultra bike gravel Ceres GT Select 5.1Shown with Optional Cane Creek fork. Production model will be fitted with the Tout Terrain Gravel Fork SL The Tout Terrain Ceres GT Select 5. 1 is a high performance steel frame gravel bike engineered for ultra distance adventures and fast gravel racing. This is our sportiest frame to date, combining the legendary resilience and comfort of a Tout Terrain Thermocrom steel frame with a modern, race inspired geometry. Key Features for Performance and

Shown with Optional Cane Creek fork. Production model will be fitted with the Tout Terrain Gravel Fork SL

 

The Tout Terrain Ceres GT Select 5.1 is a high-performance steel frame gravel bike engineered for ultra-distance adventures and fast gravel racing. This is our sportiest frame to date, combining the legendary resilience and comfort of a Tout Terrain Thermocrom steel frame with a modern, race-inspired geometry.

Key Features for Performance and Adventure:
Speed and Agility: The Ceres GT provides improved acceleration, vibration-dampening comfort, and an aerodynamic riding position, making it ideal for all-day, high-pace rides.

Electronic Drivetrain: Equipped with a reliable SRAM Apex AXS / GX Eagle Transmission 1x12 electronic shifting system, offering a huge gear range with a 10-52T cassette and a 40T crankset for maximum versatility.

Race-Ready Fork: The production model comes with the lightweight Tout Terrain Gravel Fork SL carbon fork, featuring threaded bosses for extra bags and water bottles.

Optimized for Bikepacking: Built with plenty of mounting points—4 on the frame and 2 on the fork—for bags, extra bottles, and essential gear, ready for extended bikepacking journeys.

Wheel/Tire Specs: Runs on fast 28" (700c) wheels, fitted with Vredestein Aventura 700Cx44mm tires on DT Swiss G540 rims, and offering a maximum 54mm tire clearance.

Available in sizes S, M, L, XL, and featuring integrated cable routing and a T47 threaded bottom bracket.

 

CERES GT

MODELL

Select 5.1

 

 

Frame:

Ceres Steele frame | Tout Terrain Thermocrom tubing

Frame sizes:

S, M, L, XL

Frame color:

Acqua Bella metallic glossy

Fork:

Tout Terrain Gravel Fork SL | carbon fork

Axle:

Front: 12/100mm thru-axle | Hinten: 12/142mm thre-axle (UDH compatibel)

Break mount:

Rear: Flat Mount 160-180mm | Hinten: Flat Mount 160-180mm

Drivertrain:

SRAM Apex AXS / GX Eagle Transmission 1x12

Crankset:

SRAM Apex 1 Wide | 40T | 172,5mm

Cassette:

SRAM XS-1270 Eagle Transmission | 10-52T | 12s

Gear ratio:

 

Bottom bracket

SRAM DUB T47 85.5

Front Derailleur

n/a

Derailleur

SRAM GX Eagle Transmission AXS | 1x12 T-Type

Chain:

SRAM GX Eagle Transmission Flattop | 12-fach

 

Shift lever:

SRAM Apex AXS | ED-APX-D1

Steering stop:

Tout Terrain Ergo-Stop II

Headset:

Acros | EC34 / EC34

Stem:

Tout Terrain | 70mm | +/- 6°

Hanldebar:

Tout Terrain gravel dropbar | 440mm | 16° Flare

Breaks:

SRAM Apex AXS | gravel disc brake

Break discs:

Jagwire 180/160mm | Centerlock

Grips:

Fizik Microtex Performance bat tape

Wheel size:

28"

Front hub:

Tout Terrain EX-F1 | Centerlock

Rear hub:

Tout Terrain EX-R1 - 10/11S | Centerlock

Tires:

Vredestein Aventura | 700Cx44mm / 44-622

Rims:

DT Swiss G540

Pedals:

n/a

Rack:

 

Seatpost:

Tout Terrain SP1-Zero | 27,2mm

Saddle:

WTB Volt medium

Lighting:

 

Fenders:

N/A

Kickstand

 

Capacity (Total/Rack)

120kg

Mounting positions

4 (frame) + 2 (fork)

Price (US)

                                                                                           $4,099

 

S M L XL
A Seat Tube (mm) 490 530 570 610
A1 Seat Tube (mm) 430 470 510 550
B Top Tube (mm) 540 560 580 600
C Head Tube (mm) 115 135 155 175
D Chain Stay (mm) 430 430 430 430
E Wheelbase (mm) 1020 1036 1046 1060
F BB Drop (mm) 70 70 70 70
G Head Tube Angle (*) 71 71 71.5 71.5
H Seat Tube Angle (*) 74 73.5 73 72.5
I Stack (mm) 559 578 599 618
J Reach (mm) 380 389 397 405
K Standover (mm) 776 804 833 860
L Axle To Crown (mm) 407 407 407 407
M Fork Offset (mm) 45 45 45

45

 

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SKU: 19442098076

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Zygerian99
Charlottesville, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
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Shannon
Massapequa, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Dallas, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
Lowell, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Belleville, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017

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