SKU: 58259259796

Explore Scientific 102mm f/7 Essential Apochromatic ED Triplet Refractor Telescope (OTA only)

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Description

Explore Scientific 102mm f/7 Essential Apochromatic ED Triplet Refractor Telescope (OTA only)102mm Aperture 714mm Focal Length, f 7 Focal Ratio Extra Low Dispersion (ED) Glass Proprietary EMD Enhanced Multicoatings Vixen Style Dovetail Plate Apochromatic Triplet Lens System Mounting Cradle Ring with Handle Two Speed 2" Rack and Pinion Focuser 1. 25" Eyepiece Adapter 90 Star Diagonal The ED102 102mm f 7 Essential Apochromatic ED Triplet Refractor Telescope from Explore Scientific features a triplet apochromatic lens system with Extra low

  • 102mm Aperture
  • 714mm Focal Length, f/7 Focal Ratio
  • Extra-Low Dispersion (ED) Glass
  • Proprietary EMD Enhanced Multicoatings
  • Vixen-Style Dovetail Plate
  • Apochromatic Triplet Lens System
  • Mounting Cradle-Ring with Handle
  • Two-Speed 2" Rack-and-Pinion Focuser
  • 1.25" Eyepiece Adapter
  • 90° Star Diagonal

The ED102 102mm f/7 Essential Apochromatic ED Triplet Refractor Telescope from Explore Scientific features a triplet apochromatic lens system with Extra-low Dispersion (ED) glass and Explore Scientific's proprietary anti-reflection EMD fully multicoated optics. This combination of glass, lenses, and coatings virtually eliminates chromatic and spherical aberrations for clear and bright images without distortion across the entire field of view, with true color rendition. This is a versatile optical tube assembly that is powerful enough to clearly see the planets and bright deep sky objects. It has a relatively small form-factor that makes it perfect as a portable grab-and-go option, or set up on a dual-mount rig for astrophotography/astroimaging.

A mounting cradle-ring clamps over optical tube assembly (OTA). The cradle-ring allows the user to adjust the orientation and position of the scope relative to the mount to optimize viewing comfort and balance, while avoiding interference with other equipment. The scope is offered without a mount or tripod, but a Vixen-style dovetail plate makes it compatible with any alt-az or equatorial mount with a Vixen-style saddle. Its two-speed rack-and-pinion focuser helps make fine-focusing fast and precise. A 90° star diagonal is also included for more comfortable viewing. The mirror has dielectric coatings for a 99% light transmission for minimal light loss when using it.

Optical Performance
  • 102mm objective lens
  • 714mm focal length
  • Focal ratio: f/7
  • Refractor-style optical design
  • Extra-low Dispersion (ED) glass
  • Triplet apochromatic lenses eliminate chromatic and spherical aberrations for true color rendition and without distortion across the entire field of view
  • Proprietary anti-reflection EMD enhanced multicoatings on all optics
Use and Handling
  • Two-speed 2" rack-and-pinion focuser
  • Bracket for user-supplied finderscope
  • One-piece 2" 90° star diagonal with highly-reflective dielectric coatings for up to 99% light transmission
  • 1.25" eyepiece adapter
Mount
  • Vixen-style dovetail plate included
  • User-supplied mount and tripod required
  • Compatible with any alt-az or equatorial mount with a Vixen-style saddle
  • Cradle-ring assembly for plate allows the scope orientation to be changed based on mount configuration
  • Integrated carry handle on cradle-ring assembly
UPC: 812257010011
Optical Design Refractor
Aperture 4.0" (102 mm)
Focal Length 714 mm
Focal Ratio f/7
Eyepiece Barrel Diameter 2", 1.25" with included adapter
Diagonal 90°
Finderscope None
Tripod None
Mount Type Vixen-style dovetail
Optical Tube Dimensions Diameter: 4.7" (11.9 cm)
Length: 30.5" (77.4 cm), with dew shield
Length: 23.8" (603 cm), without dew shield
Weight 9.6 lb (4.4 kg)
Packaging Info
Package Weight 17.9 lb
Box Dimensions (LxWxH) 31.0 x 10.5 x 8.0"
In the Box
Explore Scientific 102mm f/7 Essential Apochromatic ED Triplet Refractor Telescope (OTA only)
  • 2" ES Star Diagonal
  • 1.25" Eyepiece Adapter
  • Limited 1-Year USA Warranty, Extendable to Transferrable Lifetime Warranty with Registration
All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
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Exchange/Return Notes
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  • Final sale items are not eligible for returns or exchanges.
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SKU: 58259259796

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Verified Purchase
Amazon Customer
Massapequa, 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
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Kindle Customer
Birmingham, 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
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Tommy Jonsson
Los Angeles, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Louisville, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Natrona Heights, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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