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HONDA TALON 1000X 6" PORTAL GEAR LIFTGDP Portals Are Unbeatable Everyone knows theres no such thing as a one size fits all off road experience. It all comes down to where, when, and how you like to ride. Thats why SuperATVs Gen 3 portals are all about giving you options. When you buy our 6 Portal Gear Lift for your Honda Talon 1000X, you get a say in what kind of housings we use (cast or billet aluminum), how much gear reduction you get, and whether or not you want the unbeatable
GDP Portals Are Unbeatable
Everyone knows there’s no such thing as a “one size fits all” off-road experience. It all comes down to where, when, and how you like to ride. That’s why SuperATV’s Gen 3 portals are all about giving you options. When you buy our 6” Portal Gear Lift for your Honda Talon 1000X, you get a say in what kind of housings we use (cast or billet aluminum), how much gear reduction you get, and whether or not you want the unbeatable strength of dual idler gears. So whether you need a 30%, 45%, or 60% gear reduction, GDP has you covered.
What is a Dual Idler®?
Perfection takes time. After years of research and development, we’ve made our dual idler gear portals unlike any other. By placing a second idler gear in the portal box, the load gets spread over twice as much surface area for more reliable torque transfer and the strongest gear set you can get. Two idler gears don’t do a thing if they’re not mated perfectly, though. Precision CNC grinding ensures ours do. Our gears are made from the industry’s toughest 9310 steel alloy. Didn’t think dual idlers mattered? With GDP’s quality and experience, they finally do.
Great for Any Terrain
Mud and portals go together like peanut butter and jelly. Depending on how big your tires are, you’ll be right at home with a 45% or 60% gear reduction. If you’re not into mud, a 30% gear reduction will deliver unbelievable torque while maintaining a higher top speed. No matter what you go with, you’ll enjoy performance you didn’t know was possible.
A Gear Reduction That Protects Your Drivetrain
By putting the gear reduction in the hub, our portals don’t put any extra stress on your machine’s drivetrain. This means your axles, differential, prop shaft, and transmission are able to do their job without acquiring any premature wear and tear. Being able to ride without worry lets you focus on what’s really important—having fun.
Built with Superior Strength
We went all out when designing our portals because we know how important it is for them to be stronger, smarter, and more reliable than anything else out there. And what’s more, you get to choose cast or billet aluminum housings. Add in our 5/16” backing plates made from an advanced steel alloy and you’re left with Honda Talon portals that are second to none.
Advanced Gaskets, Seals, and Bearings
Our portals use:
Add Some Extra Frame Support
After installing these 6” GDP portals, you’ll be riding harder than ever before. Is your machine’s frame up for the challenge? We recommend installing a frame stiffener with these portals. It adds the stability and reinforcement you need to tackle rough terrain without fear of breaking something.
Top-Rated Customer Service
Still need a little help getting exactly what you need? We’ve got a dedicated team just for portal support. Give us a call so we can get the perfect set of portals in your hands today.
Patent # 11,299,042
Patent # 11,318,787
Everyone knows there’s no such thing as a “one size fits all” off-road experience. It all comes down to where, when, and how you like to ride. That’s why SuperATV’s Gen 3 portals are all about giving you options. When you buy our 6” Portal Gear Lift for your Honda Talon 1000X, you get a say in what kind of housings we use (cast or billet aluminum), how much gear reduction you get, and whether or not you want the unbeatable strength of dual idler gears. So whether you need a 30%, 45%, or 60% gear reduction, GDP has you covered.
What is a Dual Idler®?
Perfection takes time. After years of research and development, we’ve made our dual idler gear portals unlike any other. By placing a second idler gear in the portal box, the load gets spread over twice as much surface area for more reliable torque transfer and the strongest gear set you can get. Two idler gears don’t do a thing if they’re not mated perfectly, though. Precision CNC grinding ensures ours do. Our gears are made from the industry’s toughest 9310 steel alloy. Didn’t think dual idlers mattered? With GDP’s quality and experience, they finally do.
Great for Any Terrain
Mud and portals go together like peanut butter and jelly. Depending on how big your tires are, you’ll be right at home with a 45% or 60% gear reduction. If you’re not into mud, a 30% gear reduction will deliver unbelievable torque while maintaining a higher top speed. No matter what you go with, you’ll enjoy performance you didn’t know was possible.
A Gear Reduction That Protects Your Drivetrain
By putting the gear reduction in the hub, our portals don’t put any extra stress on your machine’s drivetrain. This means your axles, differential, prop shaft, and transmission are able to do their job without acquiring any premature wear and tear. Being able to ride without worry lets you focus on what’s really important—having fun.
Built with Superior Strength
We went all out when designing our portals because we know how important it is for them to be stronger, smarter, and more reliable than anything else out there. And what’s more, you get to choose cast or billet aluminum housings. Add in our 5/16” backing plates made from an advanced steel alloy and you’re left with Honda Talon portals that are second to none.
- Our housings are reinforced for gear retention and optimized for oil flow
- Advanced steel alloy backing plates are stronger than chromoly
- Enhance with Portal Blood—gear oil formulated for GDP Portals
- There are no gimmicks and no vents because vents do more harm than good—we know because we tested them
Advanced Gaskets, Seals, and Bearings
Our portals use:
- Advanced gaskets and O-rings with excellent oil resistance and low deterioration rates
- A sealed input gear and double-lipped output seal
- 100% sealing coverage throughout so you can forget about oil leaks
- Double-angular contact output bearings to prevent premature wear
- Roller idler and drive bearings to maintain perfect gear meshing
Add Some Extra Frame Support
After installing these 6” GDP portals, you’ll be riding harder than ever before. Is your machine’s frame up for the challenge? We recommend installing a frame stiffener with these portals. It adds the stability and reinforcement you need to tackle rough terrain without fear of breaking something.
Top-Rated Customer Service
Still need a little help getting exactly what you need? We’ve got a dedicated team just for portal support. Give us a call so we can get the perfect set of portals in your hands today.
| 6” Portal Gear Lift Includes: | ||
|---|---|---|
| (4) Assembled portal hub boxes complete with 9310 gears, seals, output shaft, gaskets, and thrust bearings ✅ | ||
| (4) Universal hubs and precision-ground stainless steel slotted rotors with lugs ✅ | ||
| (4) Specialized steel alloy backing plates ✅ | ||
| (4) Steel caliper mounting plates with spacers ✅ | ||
| (8) Caliper mounting plate shims ✅ | ||
| (2) Steel steering arms ✅ | ||
| (2) Rear adapter plates ✅ | ||
| (2) Preinstalled lower ball joints ✅ | ||
| Extended brake lines | ||
| All required hardware, including recessed castle nut socket | ||
| Frame stiffener (must select at checkout) | ||
| Honda Talon 1000X Portal Gear Reduction Recommendations | ||
|---|---|---|
| Max Tire Size | Required Suspension Setup with 6” Portals | |
| 33" | Stock | |
| 33" | 2" Lift Kit | |
| 36" | 1.5" Forward Offset A-Arms | |
| 36" | 1.5" Forward Offset A-Arms and 2" Lift Kit | |
| If you need help figuring out which gear reduction is right for you, check out our portal gear reduction calculator. | ||
| Max tire sizes are measured at full compression and full turn. The given tires sizes are the largest we fit without rubbing. With minor trimming or a small amount of rub, larger tire sizes can be used. | ||
Patent # 11,299,042
Patent # 11,318,787
WARNING: This product can impact machine operation. Customer and/or user is responsible for ensuring that this product is compatible with their machine as currently configured, properly installed, and understands any impact this product has or might have on the machine's operation.
⚠️ California Proposition 65 Warning ⚠️
WARNING: This product may contain a chemical known to the State of California to cause cancer or birth defects or other reproductive harm.
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4.1 ★★★★★
Based on 6 reviews
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Product Reviews
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it.
For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would.
The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is.
This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 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
★★★★★ 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
★★★★★ 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