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Description
electric guitar loop pedal Sheeran Loopers: LOOPER + – Motor City GuitarSheeran Loopers LOOPER + Dual Track Looping Pedal INTRODUCING THE SHEERAN LOOPER + Sheeran Looper + is the nextgeneration powerful and portable dual track looper pedal. Looper + features Ed Sheerans looper workflow, stadium grade die cast aluminum pedals, and an intuitive 1. 8" color screen with RGB LED loop status ring for heads up performance information. Designed by Ed and his production team, Looper + utilizes custom DSP powered by HeadRush that
Sheeran Loopers LOOPER + Dual-Track Looping PedalINTRODUCING THE SHEERAN LOOPER +
Sheeran Looper + is the next–generation powerful and portable dual-track looper pedal. Looper + features Ed Sheeran’s looper workflow, stadium-grade die-cast aluminum pedals, and an intuitive 1.8" color screen with RGB LED loop status ring for heads up performance information.
Designed by Ed and his production team, Looper + utilizes custom DSP powered by HeadRush® that offers class-leading audio quality and stacks of connectivity options for performing on the street, in the studio, or up on stage! Looper + is the perfect compact looper whether you’re just starting out, or a professional musician looking to upgrade your existing pedalboard with an intuitive new looper pedal.
TWO PEDALS, INFINITE POSSIBILITIES
Looper + features the exact same premium die-cast pedals found on Ed’s stadium stage looper. These pedals have been precision-engineered with optimal dimensions and spring pressure to be consistently easy-to-operate, helping you focus purely on your looping performance.
These ultra-durable pedals control the track record, overdub, playback and stop status of your loops, and feature hold functionality to undo/redo and clear your loops with ease. You can also connect an optional external single or dual footswitch and/or MIDI controller to Looper + for assignable feature control such as soloing and muting tracks, reverse and 1/2 speed playback, as well as fading loops in and out.
DIFFERENT LOOPER MODES FOR VERSATILITY
Looper + offers intuitive looping out-the-box with four different looper modes to achieve your ideal looping workflow.
• Single Mode: 1 track with limitless layers for creating simple and powerful loops like Ed did when building his live show career
• Multi Mode: 2 tracks with a global loop length for building tracks, just like Ed’s stadium stage looper workflow
• Sync Mode: 2 tracks with different lengths that automatically stay in sync with each other
• Song Mode: 2 tracks with same or different lengths that function as independent song sections (ex. verse, chorus, etc.)
Features:
Top Panel
• Display: 1.8" color LCD display
• Housing: Matte black powder coated steel chassis
• Pedals: (2) die-cast aluminum pedals with titanium grey satin finish
• Controls: 360° navigation wheel with push-to-enter and hold function with RGB LED ring indicator
• Instrument input gain control knob with real-time LED input level meter
• Microphone input gain control knob with real-time LED input level meter
• Main volume control knob with real-time LED output level meter
Real Panel
I/O
• Combo XLR+1/4" (6.35 mm) balanced input (mono)
• (2) 1/4" (6.35 mm) balanced inputs (stereo pair)
• 1/4" (6.35 mm) single or dual footswitch input (TS or TRS)
• (2) 1/4" (6.35 mm) balanced outputs (stereo pair)
• 1/8" (3.5 mm) MIDI Input (5-Pin to 1/8" adapter included)
• USB Type-B port
• Power adapter input with power switch and cable restraint
Power Specifications
• DC Power Input: 9V DC, 500mA, Center-Negative, 2.1mm Barrel (adapter not included)
• USB Power Input: Yes
• Battery Power Type: 4 x AA (Alkaline) (Included)
• Est. Battery Life: Over 6 Hours (may vary based on conditions of use/battery brand)
Looping Specifications
• Looper Modes: 4 (Single, Multi, Song, Sync)
• Looping Functions: Record, Overdub, Play, Stop, Undo/Redo, Clear Track, Clear All, Reverse, 1/2 Speed, Fade In/Out, Solo, Mute
• Max # of Looper Tracks: 2
• Max Loop Length: 1.5 Hours
• Max Overdub Layers Per Loop: Unlimited Layers
• Max Overdub Time Per Loop: 30 Minutes
• Loop File Storage Format: .WAV (32-Bit PCM, 44.1 kHz)
• Max # of Stored Loops: 128
• Onboard Storage Time: Over 3 Hours
• Onboard Storage Space: ~4.1GB
Other Features and Functionality
• External Pedal Assignment: Yes (for Single or Dual Footswitch)
• USB Transfer Mode: Yes
• USB Audio Interface Mode: Yes
• Control from External MIDI Devices: Yes
• MIDI Clock Sync: Receive
• Audio Routing Customization: Stereo, Mono or Split (Loop/Dry)
Audio Processing Specifications
• Processor Type: Custom single-core HeadRush(R) DSP
• System Audio Bit Rate: 32-Bit PCM
• System Audio Sample Depth: 44.1 kHz
• A/D D/A Conversion: 24-Bit
USB Recording Specifications
• Bit Rate: 24-Bit
• Available Sample Depths: 44.1 kHz
• Input Channels: 2
• Output Channels: 2
USB Loop Import Specifications
• Supported File Types: Stereo .WAV
• Supported Bit Rates: 16-bit PCM, 24-bit PCM, 32-bit PCM, or 32-Bit Float
• Supported Sample Rates: 44.1 kHz
Dimensions and Weight
• Pedal Dimensions (WXDXH): 8.52" x 6.24" x 3" (21.64 x 15.85 x 7.62 cm)
• Pedal Weight: 3.75 lbs. (1.7 kg)
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
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4.4 ★★★★★
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Product Reviews
★★★★★ 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.
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Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
★★★★★ 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
★★★★★ 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