SKU: 33417928423

HyperSpark Distributor - Chrysler 383 / 400 - 565-305BK

$233.98 $259.98
Save 10%
No.2 Bestseller on 2pierres.com >

Pay in installments of $65.00 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Sep 18 - Sep 23

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

HyperSpark Distributor - Chrysler 383 / 400 - 565-305BKOverview: HyperSpark distributors are plug and play with all Holley Sniper EFI systems, featuring a high quality billet distributor housing, hall effect crank trigger sensor, and they come included with the foolproof patented clear installation cap. Hyperspark CD Ignition Box (556 152) and Ignition Coil (556 151) sold separately. Features: Hall Effect crank trigger sensor housing provides a noise free RPM signal to the Sniper ECU Billet distributor

Overview:

HyperSpark distributors are plug and play with all Holley Sniper EFI systems, featuring a high-quality billet distributor housing, hall effect crank trigger sensor, and they come included with the foolproof patented clear installation cap. Hyperspark CD Ignition Box (556-152) and Ignition Coil (556-151) sold separately.

Features:

  • Hall Effect crank trigger sensor housing provides a noise-free RPM signal to the Sniper ECU
  • Billet distributor housing provides corrosion resistance and stylish looks!
  • Patented Clear Installation cap eliminates any confusion while installing the distributor.
  • Easiest plug and play way to add timing control to a Sniper EFI Installation.
  • Distributor shutter wheel is pinned and welded to the shaft, for decades of reliable accuracy.
  • Includes Wire Retainer for a clean installation.
  • Comes with Cast Gear Installed - contact camshaft manufacturer for recommended gear material.
  • Designed to be used with Sniper EFI HyperSpark CD box P/N 556-151 and Ignition Coil 556-152
  • Available in Black or Shiny Billet Aluminum to match any engine bay

Application:

Year Make Model Submodel Engine Size
1965 - 1967 Plymouth Fury III 383/6.3L V8
1967 - 1971 Fargo W200 Pickup 383/6.3L V8
1975 - 1978 Plymouth Fury Sport 400/6.6L V8
1962 - 1966 Chrysler 300 383/6.3L V8
1975 - 1976 Dodge Coronet Base 400/6.6L V8
1969 Dodge Charger 500 383/6.3L V8
1965 - 1971 Dodge Monaco 383/6.3L V8
1976 - 1978 Plymouth Fury Salon 400/6.6L V8
1960 - 1971 Dodge Polara 383/6.3L V8
1968 - 1971 Plymouth GTX 383/6.3L V8
1960 - 1961 Plymouth Sport Wagon 383/6.3L V8
1975 - 1976 Dodge Coronet Crestwood 400/6.6L V8
1971 - 1974 Dodge D300 Pickup 400/6.6L V8
1975 - 1978 Plymouth Fury Base 400/6.6L V8
1972 - 1978 Dodge Charger 400/6.6L V8
1964 Dodge 880 Custom 383/6.3L V8
1960 - 1964 Plymouth Savoy 383/6.3L V8
1968 - 1971 Dodge D300 Pickup 383/6.3L V8
1965 - 1971 Plymouth Satellite 383/6.3L V8
1972 - 1974 Plymouth Satellite 400/6.6L V8
1970 - 1971 Dodge Challenger Base 383/6.3L V8
1970 - 1971 Plymouth Fury Sport Suburban 383/6.3L V8
1959 - 1961 Dodge Custom 383/6.3L V8
1959 - 1971 Dodge Coronet 383/6.3L V8
1967 - 1971 Plymouth Barracuda 383/6.3L V8
1975 - 1977 Plymouth Gran Fury Brougham 400/6.6L V8
1966 Plymouth Fury VIP 383/6.3L V8
1975 - 1977 Plymouth Gran Fury Sport 400/6.6L V8
1960 - 1963 Plymouth Fleet Special 383/6.3L V8
1964 - 1971 Plymouth Fury Sport 383/6.3L V8
1972 - 1974 Plymouth Fury I Base 400/6.6L V8
1975 - 1977 Plymouth Gran Fury Suburban 400/6.6L V8
1972 - 1974 Plymouth Fury II Base 400/6.6L V8
1975 Dodge Coronet Custom 400/6.6L V8
1959 - 1971 Chrysler Town & Country 383/6.3L V8
1965 - 1970 Chrysler Newport 383/6.3L V8
1970 Plymouth Fury S-23 383/6.3L V8
1959 Dodge Sierra 383/6.3L V8
1960 - 1970 Plymouth Belvedere 383/6.3L V8
1971 - 1974 Dodge D200 Pickup 400/6.6L V8
1971 - 1974 Dodge W100 Pickup 400/6.6L V8
1967 - 1971 Jensen Interceptor 383/6.3L V8
1972 - 1974 Plymouth Fury III Base 400/6.6L V8
1967 Dodge Monaco Base 383/6.3L V8
1968 - 1971 Plymouth Fury III Base 383/6.3L V8
1968 - 1971 Plymouth Road Runner 383/6.3L V8
1965 - 1966 Plymouth Fury II 383/6.3L V8
1959 - 1963 Facel Vega Excellence 383/6.3L V8
1967 Dodge W200 Series 383/6.3L V8
1975 - 1977 Dodge W100 400/6.6L V8
1968 - 1971 Plymouth Fury II Base 383/6.3L V8
1970 Dodge Polara Special 383/6.3L V8
1970 - 1971 Plymouth Cuda 383/6.3L V8
1972 - 1978 Chrysler Newport 400/6.6L V8
1966 - 1971 Dodge Charger 383/6.3L V8
1960 - 1969 Plymouth Fury 383/6.3L V8
1970 - 1971 Plymouth Fury Suburban 383/6.3L V8
1975 - 1977 Dodge Royal Monaco 400/6.6L V8
1959 - 1960 Chrysler Saratoga 383/6.3L V8
1970 - 1971 Dodge Challenger R/T 383/6.3L V8
1972 Fargo W100 Pickup 400/6.6L V8
1972 - 1976 Dodge Monaco 400/6.6L V8
1972 - 1974 Dodge Coronet 400/6.6L V8
1972 Plymouth Fury Gran Coupe 400/6.6L V8
1960 - 1961 Plymouth Custom 383/6.3L V8
1969 Dodge Charger R/T 383/6.3L V8
1971 - 1974 Dodge B300 Van 400/6.6L V8
1970 Plymouth Fury GT 383/6.3L V8
1967 - 1971 Fargo D210 Pickup 383/6.3L V8
1968 - 1971 Dodge D100 Pickup 383/6.3L V8
1967 Fargo D100 Panel Delivery 383/6.3L V8
1972 - 1973 Dodge Polara 400/6.6L V8
1977 - 1978 Dodge Ramcharger 400/6.6L V8
1972 Jensen Interceptor MK III 383/6.3L V8
1967 Dodge D100 Series 383/6.3L V8
1968 - 1971 Dodge W100 Pickup 383/6.3L V8
1972 Plymouth Fury Gran Sedan 400/6.6L V8
1975 - 1977 Plymouth Gran Fury Sport Suburban 400/6.6L V8
1972 Chrysler New Yorker Brougham 400/6.6L V8
1974 Dodge B200 Van Base 400/6.6L V8
1963 - 1970 Dodge Polara Base 383/6.3L V8
1967 - 1971 Fargo D200 Pickup 383/6.3L V8
1972 - 1975 Plymouth Road Runner 400/6.6L V8
1972 - 1977 Plymouth Gran Fury Base 400/6.6L V8
1963 - 1964 Dodge 440 383/6.3L V8
1965 - 1967 Plymouth Belvedere II 383/6.3L V8
1971 - 1973 Dodge B200 Van 400/6.6L V8
1961 Dodge Pioneer 383/6.3L V8
1976 - 1978 Dodge D100 400/6.6L V8
1971 - 1974 Dodge W200 Pickup 400/6.6L V8
1972 - 1977 Chrysler Town & Country 400/6.6L V8
1977 - 1978 Dodge Monaco Crestwood 400/6.6L V8
1967 Dodge W100 Series 383/6.3L V8
1968 - 1971 Dodge W300 Pickup 383/6.3L V8
1969 Dodge Charger Base 383/6.3L V8
1965 - 1967 Dodge Coronet Base 383/6.3L V8
1961 Dodge Seneca 383/6.3L V8
1976 - 1978 Dodge CB300 400/6.6L V8
1960 - 1968 Dodge Dart 383/6.3L V8
1962 - 1964 Facel Vega Facel II 383/6.3L V8
1961 - 1969 Dodge Dart Base 383/6.3L V8
1968 - 1971 Dodge D200 Pickup 383/6.3L V8
1972 Fargo D210 Pickup 400/6.6L V8
1969 Dodge Dart Custom 383/6.3L V8
1975 - 1978 Dodge D200 400/6.6L V8
1962 - 1966 Plymouth Fury Base 383/6.3L V8
1976 - 1978 Plymouth PB300 400/6.6L V8
1972 - 1974 Dodge Challenger 400/6.6L V8
1969 - 1973 Bristol 411 383/6.3L V8
1972 Plymouth Fury Suburban 400/6.6L V8
1975 - 1978 Chrysler Cordoba 400/6.6L V8
1967 - 1971 Fargo D110 Pickup 383/6.3L V8
1960 Dodge Matador 383/6.3L V8
1960 - 1961 Dodge Phoenix 383/6.3L V8
1975 - 1978 Chrysler New Yorker 400/6.6L V8
1967 Dodge W300 Series 383/6.3L V8
1967 Dodge Coronet 440 383/6.3L V8
1961 Plymouth Suburban Custom 383/6.3L V8
1960 - 1963 Dual-Ghia L6.4 383/6.3L V8
1959 Dodge Lancer 383/6.3L V8
1975 - 1976 Plymouth Gran Fury Custom 400/6.6L V8
1970 - 1971 Plymouth Fury Custom Suburban 383/6.3L V8
1969 Dodge Dart Swinger 383/6.3L V8
1977 - 1978 Dodge W150 400/6.6L V8
1974 Dodge B200 Van Maxi Wagon 400/6.6L V8
1969 - 1971 Fargo W110 Pickup 383/6.3L V8
1967 Fargo D200 Panel Delivery 383/6.3L V8
1969 Dodge Charger SE 383/6.3L V8
1967 - 1971 Fargo W100 Pickup 383/6.3L V8
1976 - 1978 Plymouth PB200 400/6.6L V8
1959 - 1961 Chrysler Windsor 383/6.3L V8
1976 - 1978 Dodge B200 400/6.6L V8
1968 - 1971 Plymouth Fury I Base 383/6.3L V8
1972 Fargo D100 Pickup 400/6.6L V8
1959 - 1961 Facel Vega HK500 383/6.3L V8
1960 Plymouth Suburban 383/6.3L V8
1968 - 1971 Dodge W200 Pickup 383/6.3L V8
1975 - 1976 Dodge Coronet Brougham 400/6.6L V8
1971 Chrysler Newport Custom 383/6.3L V8
1976 - 1978 Dodge D300 400/6.6L V8
1971 - 1974 Dodge D100 Pickup 400/6.6L V8
1972 Fargo D110 Pickup 400/6.6L V8
1967 - 1971 Fargo D100 Pickup 383/6.3L V8
1961 Plymouth Suburban Sport 383/6.3L V8
1976 - 1978 Dodge B300 400/6.6L V8
1967 Dodge Coronet R/T 383/6.3L V8
1964 - 1966 Jensen C-V8 383/6.3L V8
1971 - 1974 Dodge W300 Pickup 400/6.6L V8
1976 - 1978 Dodge W200 400/6.6L V8
1977 - 1978 Dodge Monaco Brougham 400/6.6L V8
1977 - 1978 Dodge Monaco Base 400/6.6L V8
1973 - 1974 Plymouth Fury 400/6.6L V8
1969 Dodge Dart GT 383/6.3L V8
1972 Fargo D200 Pickup 400/6.6L V8
1972 Plymouth Fury Sport Suburban 400/6.6L V8
1975 - 1976 Plymouth Gran Fury Custom Suburban 400/6.6L V8
1972 Plymouth Fury Custom Suburban 400/6.6L V8
1975 Plymouth Fury Custom 400/6.6L V8
1959 Dodge Royal 383/6.3L V8
1970 Plymouth Fury Gran Coupe 383/6.3L V8
1974 Bristol 411 400/6.6L V8
1977 Dodge D150 400/6.6L V8
1963 - 1964 Dodge 880 Base 383/6.3L V8
1965 Dodge 880 383/6.3L V8
1978 Dodge Magnum 400/6.6L V8
1963 - 1964 Dodge 330 383/6.3L V8
1971 Chrysler Newport Royal 383/6.3L V8
1974 Dodge B200 Van Sportsman 400/6.6L V8
1977 - 1978 Plymouth Trailduster 400/6.6L V8
1967 Dodge D200 Series 383/6.3L V8
1970 Dodge Polara Custom 383/6.3L V8
1974 Dodge B200 Van Maxi 400/6.6L V8
1967 Plymouth VIP 383/6.3L V8
1972 Fargo W200 Pickup 400/6.6L V8
1978 Dodge D150 Base 400/6.6L V8
1959 - 1960 DeSoto Adventurer 383/6.3L V8
1969 Dodge Dart GTS 383/6.3L V8
1959 DeSoto Firedome 383/6.3L V8
1971 Chrysler Newport Base 383/6.3L V8
1959 DeSoto Fireflite 383/6.3L V8
1961 Plymouth Suburban Base 383/6.3L V8
1972 Fargo W110 Pickup 400/6.6L V8
1976 - 1978 Dodge W300 400/6.6L V8
1967 Dodge Coronet 500 383/6.3L V8
1967 Fargo W100 Panel Delivery 383/6.3L V8
1967 Dodge D300 Series 383/6.3L V8
1963 Dodge Polara 500 383/6.3L V8

Specs:

Brand Holley Sniper EFI
Color Black
Distributor Type Hall Effect
Emission Code 3
Material Billet Aluminum
Product Type Distributor
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: 33417928423

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.0 ★★★★★
Based on 13 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
K
Kirsten
Whiting, US
★★★★★ 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!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 17, 2026
X
Verified Purchase
0x00000000:00000000
Lake Worth, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Dallas, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Alexandria, 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Waukegan, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017

recommand products

Handle/frame asmbly, 6 cf cntr mach
332.50
Smart Wi-fi Garage Door Opener - APP Controlled Garage Door
99.00
In-Ear Invisible Spy Microphone Two-Way Communication
127.50
Recl, press, 900 cfm 16" w/2 hp exh
3267.50
Professional Camera & Infrared IR Detector
195.00