SKU: 66649646082

Towtrust Swan Neck Automotive Towbar + 13P Univ. Wiring For Citroen C4 Hatchback 2011 To 2018

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Description

Towtrust Swan Neck Automotive Towbar + 13P Univ. Wiring For Citroen C4 Hatchback 2011 To 2018Company Profile Tow Trust Towbars Ltd are one of the UK's largest manufacturers of towbars and towing accessories. Based in Atherstone, Warwickshire the company has set about forging a strong reputation amongst the trade for supplying the highest quality products. From commercial and non commercial towbars to an expanding range of trade accessories, Tow Trust are confident we have the right towing solution for you. Here at Tow Trust we have

Company Profile
Tow-Trust Towbars Ltd are one of the UK's largest manufacturers of towbars and towing accessories. Based in Atherstone, Warwickshire the company has set about forging a strong reputation amongst the trade for supplying the highest quality products. From commercial and non-commercial towbars to an expanding range of trade accessories, Tow-Trust are confident we have the right towing solution for you.

Here at Tow-Trust we have deliberately chosen to site ourselves at the premium end of the market, and this means sourcing the best quality steel and using the latest technology for design and manufacturing processes. This enables us to produce products that our nationwide stockists recommend without hesitation. The Tow-Trust promise from start to finish is simple, 'absolute quality'.

In today's competitive market, every brand and manufacturer seem to make the claim for the quality of their product, yet we constantly hear headlines of how companies are trying to cut costs in order to cheapen the manufacturing process of their products. Cheaper manufacturing costs may lead to cheaply made products, a concept Tow-Trust have challenged through heavy investment in our production process. The whole Tow-Trust operation, from initial computer aided design right through to our high-grade packaging of each towbar, exhibits a remarkable attention to detail.

You may be forgiven for now expecting that this makes our products expensive, however our dedicated sales team constantly monitor our prices to ensure we are competitively priced against all alternative manufacturers. Although our products may not be the cheapest on the market we firmly believe they offer true 'value for money'. We think anyone would agree that you cannot be the cheapest and the best at the same time, and for that very reason we have decided to become the best. After all, ask yourself the question: with a safety critical component such as a towbar would you really want to rely on using the cheapest product?

Tow-Trust have been a quiet success in the towing industry for over 20 years and until relatively recently, it is fair to say that most people who require use of a towbar may only know of a few brands. However, in today's modern world the internet has meant motorists have better access than ever before to new products and possibilities. It is time therefore for the towing industries previous best kept secret to now reap the rewards of years of hard work.

From commercial fleets to the private motorist who drives to the tip once a month, the demands of a towbar are relatively simple: that it is safe, fits well, looks good, and does the job it was intended for. Despite not seeming like a lot to ask, without the proper attention to detail and passion for the product it can be surprisingly difficult to find a towbar that ticks all the boxes and we believe this is where Tow-Trust comes in. In today's market where money is tight and competition is high, it turns out that quality is still a word that means something and with such dedication to producing quality, why would you trust anyone else ....

 

Please note: Images are for illustration purposes only

 

Fixed swan neck towbar
This design of fixed towbar is becoming increasingly popular in the UK. The fixed swan neck towbar is compatible for use with an Alko stabiliser without the requirement of a replacement towball. This design of towbar is less likely to affect reversing sensors due to its slim-line design. Unfortunately the fixed swan neck towbar is not compatible with common accessories such as bumper protectors or tow-steps and cannot be used for a cycle carrier whilst towing at the same time.

Universal wiring kit

Our universal wiring kits can be installed in nearly every vehicle. The kits can transmit signals related to the tail lights. Because these are analogue signals, a universal wiring kit cannot be programmed and can therefore not connect to the on-board computer that controls the car electronics. We offer standard 7-pin and 13-pin universal wiring kits.

Disclaimer: All towbars and electrics or accessories for bundle offers are sent separately!

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Exchange/Return Notes
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SKU: 66649646082

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4.8 ★★★★★
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Verified Purchase
Par
Dallas, 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
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Verified Purchase
Richard Hackathorn
New York, 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
Phoenix, 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
Chelsea, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Lake Worth, 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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