Pay in installments of $12.25 with
,
and
Shipping Estimate
USA
- USA
- CAN
- USA
- CAN
Ships within 48 hours · Estimated delivery Aug 22 - Aug 27
Promo Codes Available:
For Your Every Summer RSVP, with Code: SUMMER15
Description
THREE FACES OF PAIN DVDCAST: Aaron Austin, Jimmy Dean, Rob Cryston, DIRECTOR: RON SEXTON RELEASE DATE: 9 21 2009 Taking full advantage of the still to crest popularity of muscle bondage wrestling, Challenger Video delivers its latest bare assed, ball busting, cock sucking, three cum shot bondage blockbuster "THREE FACES OF PAIN." Starring muscleboy Aaron Austin, studboy Robbie Cryston, bullyboy Jimmy Dean, and stunning camera work by the uncontested sure shot of continuous
CAST: Aaron Austin, Jimmy Dean, Rob Cryston,
DIRECTOR: RON SEXTON
RELEASE DATE: 9/21/2009
Taking full advantage of the still-to-crest popularity of muscle bondage wrestling, Challenger Video delivers its latest bare assed, ball busting, cock sucking, three cum shot bondage blockbuster "THREE FACES OF PAIN." Starring muscleboy Aaron Austin, studboy Robbie Cryston, bullyboy Jimmy Dean, and stunning camera work by the uncontested sure shot of continuous action videography in the business... Tim Hamilton. Hamilton`s extremely intimate, blatantly sexual camera action ride over, under, and in-between the incredible bodies of stud buddies Austin, Cryston, and Dean will take your breath away and drain you dry."THREE FACES OF PAIN" is a consensual, sexual, series of fantasy boys in three different bondage combinations dreamed by the ultimate badboy studslut Jimmy Dean. And wait til you see Austin`s and Cryston`s prolonged agony and ecstasy malevolently administered by the budding sadist. Jimmy Dean (tightly cut, spring loaded muscularity with an astonishing ass) dirty wrestles, rip strips, and uses the ring ropes to tie up Robbie Cryston (killer handsome, beautifully proportioned studboy). A failed rescue Robbie attempt also gets Aaron Austin (competitive bodybuilder with a world class ass) dirty wrestled, rip stripped, and tied up. With both Aaron and Robbie stripped and spread-crotch tied in opposite ring corners, Jimmy Dean takes his time bodybashing his bondage boys with LOTS of the hottest prick tease cock sucking you can imagine.
Note: "THREE FACES OF PAIN" marks Jimmy Dean`s on-video cocksucking debut (a personal favor to Bossman Ron Sexton) and we`re here to tell you he`s as good at cock sucking as he is at muscle mashing. Then Cryston and Dean gang up on Austin, and after a very sexual session of physical restraint positions, cock sucking, ass smacking, bodybashing, cum shooting, muscle bondage action... Austin and Cryston turn the tables on Dean. It`s Jimmy Dean`s turn to get his tightly defined bare assed hotbody stretched out face-up and spreadeagled to the four ring corner posts. Then Austin, Robbie, and Tim Hamilton`s camera take you on a wildly sexual ride over every stretched and straining muscle, and every rockhard inch of Jimmy Dean`s violently struggling body. The sexual energy enthusiastically exchanged by these truly exceptional studboys is muscle bondage wrestling fantasy in its consummate video form.
"THREE FACES OF PAIN" transports you through Tim Hamilton`s salaciously sexual camera lens so close to the smell of muscle sweat, and the tightly bound bodies of action studboys Aaron Austin, Robbie Cryston, and Jimmy Dean as to experience Challenger Video`s vision of virtual reality. Get a firm grip on your cock, your other hand`s fingers on your DVD`s slo-mo and stop-action buttons, and hang on for what industry insiders call a roller coaster ride through a bound and bullied bondage fantasy that will leave you happily spent for having spent $49 bucks so wisely. Stacks of "THREE FACES OF PAIN" sit smokin` and throbbin` in Can-Am`s shipping department waiting for you to give your dick something to shoot about.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
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]
- Please click here for more details>>> Return & Exchange Policy
4.3 ★★★★★
Based on 8 reviews
Sort
Product Reviews
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
★★★★★ 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
★★★★★ 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