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Description
A Trip to Cancun by Twister MagicMake any signed card travel to Cancun in a very sensual way! Inspired by the classic Abbotts's Bathing Beauty effect, originally created by Frederic Culpit from England. George Iglesias brings us now his renovated version, with an improved method and a powerful effect. A seven minutes solid comedy routine that have a great twist and plot and that is ready to entertain audiences of all nationalities! EFFECT: An spectator selects a card and sign it,
Make any signed card travel to Cancun in a very sensual way!Inspired by the classic Abbotts's Bathing Beauty effect, originally created by Frederic Culpit from England.
George Iglesias brings us now his renovated version, with an improved method and a powerful effect. A seven minutes solid comedy routine that have a great twist and plot and that is ready to entertain audiences of all nationalities!
EFFECT: An spectator selects a card and sign it, then place it back into the deck and shuffle it. Now the magician announces that he is going to teleport the spectator's card into an enchanting and exotic place, a heavenly dreamed beach in the Caribbean like Cancún. (You can name any other beach in the world, free choice)
The magician does a magic gesture and vanishes the card from the deck. The spectator is invited now, to try to find his signed card in the deck with no luck, one by one the cards are seeing and the selected card has just simply vanished!
To prove it, he shows now a poster with a beautiful lady on the picture, she is wearing a red bikini and a hula skirt, she is also holding a couple of coconuts and a card facing down that can be seen inside her Bra'...
The magician announce that he will make one part of the spectator's body travel in time and distance to Cancún to reach for his card and bring it back.
Now the spectator stick his hand inside and takes out the hula skirt instead... Hummmm the magician says :"Listen, I told you to take the card out, not her skirt" (When open you can see that the picture is missing now the skirt)
Once again the magician gives another chance to the spectator to reach in the two dimensional world to bring back his card... He stick his hand again, but this time he pulls out the top of the bikini out (The Bra) (When open the poster, you can see now that she is covering her top part with the pair of coconuts she had)
The magician assumes no risk, and he decided to go for it, taking the face down card out... Big is his surprise when suddenly and "by mistake" the bottom part of the biking comes out together with the signed card...
The magician is embarrassed and asked the spectator about the card he selected and when turnover it is exactly the card he selected and signed at the beginning...
The magician announce the end of the trick and take a bow... But wait the audience wants more! They want to see inside of the picture! So the magician asks: Do you want to see it? He asked again: Do you really want to see it?...So you can see him!¨ (The poster is open and it "Mario, the baywatch" a muscle guy with a dirty look can be seen)
- Great for Comedy, Parlor and Stage Magic shows.
- Lots of comedy! Very easy to perform and operate!
- 7 Minutes comedy routine
- The card can be signed by spectator or not, you decide.
- You can also use a duplicate card and a forcing card (fully explained on the instructional video).
- Comes with everything you need, including al the clothing pieces SET.
- Great quality, 5 neodymium magnets, will secure all the panels
- Printed in a high resolution paper with a great level of realism and plastic coated to increase durability.
- Comes with an Instructional online video.
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4.1 ★★★★★
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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
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