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Description
bass cabinet simulator pedal Strymon Iridium Amp & IR Cab Simulator PedalStrymon Iridium Amp & IR Cab Pedal gives you three iconic amplifiers, with a total of nine impulse response speaker cabinets that complement the tonality of the amp. SOUND DESIGN: Three amplifier models crafted using Matrix Modeling, capturing the subtleties and unique tonal qualities of each amp Round Amp based on* a Fender Deluxe Reverb Chime Amp based on* the Brilliant channel of a Vox AC30 Punch Amp based on* a Marshall Plexi (Super Lead model
Strymon Iridium Amp & IR Cab Pedal gives you three iconic amplifiers, with a total of nine impulse response speaker cabinets that complement the tonality of the amp.SOUND DESIGN:
Three amplifier models crafted using Matrix Modeling, capturing the subtleties and unique tonal qualities of each amp
Round Amp - based on* a Fender Deluxe Reverb
Chime Amp - based on* the Brilliant channel of a Vox AC30
Punch Amp - based on* a Marshall Plexi (Super Lead model number 1959)
Nine Impulse Response speaker cabinets, 24bit / 96kHz 500ms
Stereo (three per amp type), pre-loaded with a curated set of IRs:
Deluxe Reverb 1×12″ by OwnHammer
Blues Junior 1×12″ by CabIR
Vibrolux 2×10″ by CabIR
AC30 2×12″AlNiCo by OwnHammer
1×12″ AlNiCo by Celestion
Mesa 4×12″ by Valhallir
GNR 4×12″ by OwnHammer
2×12″ Vintage 30 by Celestion
Marshall 8×12″AlNiCo by CabIR
Load your own impulse responses using Strymon Impulse Manager software
Hybrid IR/algorithmic Room control with selectable small, medium, and
large rooms
Simple, responsive amp-style controls for Drive, Level, Bass, Middle,
and Treble
Premium stereo high impedance discrete JFET analog front end with
up to 22dB of pure analog gain
INS, OUTS & SWITCHES:
Stereo Input (requires TRS adapter cable)
Stereo Output
Operates at instrument and Line Level input sources
Discrete transistor headphone amplifier
Expression pedal input provides seamless morphing between knob positions (Expression mode), or smooth pre/post volume control with logarithmic taper (Volume mode)
USB jack for connecting to Strymon Impulse Manager software
to add and edit your own speaker cabinet IRs
Compatible with Strymon MultiSwitch Plus for remote selection of up to 4 presets
Full featured MIDI control accessible via EXP Jack for remote parameter control and access to 300 preset locations (requires Strymon MIDI EXP cable or MIDI to TRS interface)
AUDIO QUALITY:
Ultra-low noise, high performance 24-bit 96kHz A/D and D/A
converters provide uncompromising audio quality
Super high performance SHARC and ARM co-processors
32-bit floating point processing
MISC.:
Transparent Analog Buffered Bypass
Strong and lightweight anodized black aluminum chassis
9V DC power supply included
Power requirements: maximum 9 volts DC center-negative, with a minimum of 500mA of current
Dimensions:
4.5″ deep x 4″ wide x 1.75″ tall
11.4 cm deep x 10.2 cm wide x 4.4 cm tall
Designed and built in the USA
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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.
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4.9 ★★★★★
Based on 7 reviews
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Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 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.
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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