SKU: 76872587392

Filtrabox MICRO Multi-Stage Fume Extractor

Sale price$1079.99 Regular price$1199.99
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Description

Filtrabox MICRO Multi-Stage Fume ExtractorYour Indoor Laser Solution: All OMTech CO2 lasers are designed with exhaust systems that vent to the outside environment. However, venting outdoors is not always an option due to confined spaces, exhaust exit proximity, local health regulations, or other limitations. The FILTRABOX MICRO provides a class leading and affordable indoor laser exhaust solution. You can now operate your OMTech laser engraver in a windowless room and avoid cutting an exhaust

Your Indoor Laser Solution:

All OMTech CO2 lasers are designed with exhaust systems that vent to the outside environment. However, venting outdoors is not always an option due to confined spaces, exhaust exit proximity, local health regulations, or other limitations. The FILTRABOX MICRO provides a class-leading and affordable indoor laser exhaust solution.

You can now operate your OMTech laser engraver in a windowless room and avoid cutting an exhaust exit in your wall. The FILTRABOX × OMTech Multi-Stage Fume Extraction System utilizes best-in-class filters to remove 99.9% of airborne particulates from your laser engraving exhaust. The Filtrabox system was researched, designed, developed, and constructed in North America.

The MIRCO is specially engineered for 40W to 55W OMTech CO2 laser engravers, plus our complete range of Fiber Laser Markers. 

Micro Size, Mighty Filtration:

  • Overall Dimensions: 26"H ×10"W × 17"D (66 × 25.4 × 43.2 cm) 
  • Weight: 75 lb (34 kg) 
  • Air Flow Rate: 128 CFM
  • Quiet Performance: 55 decibels or less
  • Triple Filtration Media: 3-Stage F9 PreFilter, HEPA H14, & Oxycarbon 20 lb Filter
  • Power Supply: Standard AC 120V (230V available on request)

Compatibility:

  • Compatible with ALL OMTech Fiber Laser Markers - Light-Duty Cycle
  • Compatible with the following OMTech CO2 Laser Engraver workbed sizes: 8" x 12" (heavy-duty cycle) 12" x 20" (light-duty cycle), 16" x 24" (light-duty cycle)

Advanced Air Purification:

  • The three-stage filtration system removes virtually all laser engraving byproducts: fumes, odors, and airborne particulates, without venting outside
  • Best In Class Filters: 1. Pre-Filter (Class F9 6m2), 2. HEPA Filter (Class H14 6m2), 3. Gas Filter: Oxy-Carbon 20lbs.
  • Eliminates virtually ANY smells and odors from laser operations, including acrylic, rubber, leather, and more.
  • Long-term & corrosion-free performance with easily replaceable filters

Convenient Performance:

  • Quick-Release Filter Modules allow quick and easy access for replacement. No frustrating setup or dusty cabinets to deal with.
  • Low-cost replacement filters have a 6-month life cycle but vary depending on daily use.
  • 1-Year warranty from Filtrabox on all parts excluding filtration medias

*This product will be shipped to you directly from Filtrabox. Please contact Filtrabox for your order and tracking info.

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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]
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SKU: 76872587392

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4.2 ★★★★★
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Shannon
Dallas, 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
Bozeman, 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
Charlottesville, 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
Phoenix, 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
M
Verified Purchase
mackster
Cuba, 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.
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Reviewed in the United States on May 15, 2018

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