SKU: 70515468570

acetech predator xx tracer m blaster ms module 14mm ccw ip64 1

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Description

acetech predator xx tracer m blaster ms module 14mm ccw ip64 1Tracer Mit Flammeneffekt. CNC gefrstes Aluminium, IP64 Schutzart und USB C Anschluss fr einfache Bedienung. Acetech Predator XX ist ein robustes AIRSOFT CNC gefrste Aluminium Leuchtspur mit LED Front, die einen Mndungsblitz simuliert. Das Blaster MS Modul ist vorinstalliert und untersttzt grne Leuchtspur BBs (sowie Gelkugeln, sofern verfgbar). Das Gert verfgt ber drei programmierbare Modi: Leuchtspur + Flamme, nur Flamme oder nur Leuchtspur so knnen

Tracer Mit Flammeneffekt. CNC-gefrästes Aluminium, IP64-Schutzart und USB-C-Anschluss für einfache Bedienung.

Acetech Predator XX ist ein robustes AIRSOFT CNC-gefräste Aluminium-Leuchtspur mit LED-Front, die einen Mündungsblitz simuliert. Das Blaster MS-Modul ist vorinstalliert und unterstützt grüne Leuchtspur-BBs (sowie Gelkugeln, sofern verfügbar). Das Gerät verfügt über drei programmierbare Modi: Leuchtspur + Flamme, nur Flamme oder nur Leuchtspur – so können Sie den Effekt an Ihr Spiel und Ihre Umgebung anpassen.

Die Predator XX ist für den zuverlässigen Einsatz im Feld konzipiert: Sie ist gemäß IP64 staub- und spritzwassergeschützt, ermöglicht bis zu 35 Aufnahmen pro Sekunde und verfügt über ein intelligentes Energiemanagement, das das Gerät bei Nichtgebrauch in den Ruhemodus versetzt. Sie wird über USB-C geladen (Kabel nicht im Lieferumfang enthalten).

Verwenden

Schrauben Sie das Gerät mit dem 14-mm-CCW-Gewinde (-14 mm) direkt auf den Lauf. Wählen Sie mit den Tasten den gewünschten Modus aus, füllen Sie das Magazin mit Leuchtspurmunition für helle Leuchtspuren oder verwenden Sie den Modus „Nur Flamme“ für einen optischen Effekt ohne Leuchtspurmunition.

Vorteile

  • Simulierter Mündungsblitz zusätzlich zur Leuchtspurmunition für mehr Realismus
  • CNC-gefrästes Aluminiumgehäuse – robust und leicht
  • Schutzart IP64 gegen Staub und Spritzwasser
  • Bis zu 35 Umdrehungen pro Sekunde – geeignet für schnelle Einrichtung
  • USB-C-Ladefunktion und intelligente Schlaffunktion
  • Modulares System – kompatibel mit mehreren Acetech-Modulen

Spezifikationen

  • Länge: 155 mm
  • Thread-Typ: 14 mm CCW (-14 mm)
  • Material: Metall/CNC-Aluminium
  • Leistung: bis zu 35 U/min
  • Aktuell: eingebauter Li-Ionen-Akku (USB-C, Kabel nicht im Lieferumfang enthalten)
  • Dichte: IP64
  • Modi: Leuchtspur + Flamme/Flamme/Leuchtspur
  • Kompatibilität (Module): bl.a. Brighter C, Brighter R, Blaster MS, Blaster M; bis ca. Ø32 mm x 110 mm
  • Farbe: Schwarz

In der Box

  • Acetech Predator XX Tracer (mit installiertem Blaster MS Modul)
  • Benutzerhandbuch

Eine gute Wahl für alle, die einen robusten Leuchtspurbrenner mit realistischen Flammeneffekten, einfacher USB-C-Aufladung und zuverlässigem Betrieb unter verschiedenen Bedingungen wünschen.

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SKU: 70515468570

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4.6 ★★★★★
Based on 6 reviews
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Verified Purchase
Shannon
Omaha, 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
Belleville, 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
A
Verified Purchase
Adam
Phoenix, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Los Angeles, 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 14, 2017
M
Verified Purchase
mackster
Carnegie, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018

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