SKU: 79212269283

Hayabusa MMA Shorts Mid-Thigh Apex Carbon Black

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Hayabusa MMA Shorts Mid-Thigh Apex Carbon BlackDe Hayabusa MMA Shorts Mens Apex Mid Thigh Fight zijn gemaakt voor vechters die het maximale uit hun training willen halen. Deze MMA short biedt de perfecte balans tussen mobiliteit, stevigheid en comfort en is daarmee ideaal voor MMA, BJJ, grappling, kickboksen of conditionele vechtsporttrainingen. De lengte tot halverwege het dijbeen geeft je maximale bewegingsvrijheid zonder in te leveren op bedekking en grip. Optimale pasvorm dankzij mid thigh

De Hayabusa MMA Shorts Men’s Apex Mid-Thigh Fight zijn gemaakt voor vechters die het maximale uit hun training willen halen. Deze MMA-short biedt de perfecte balans tussen mobiliteit, stevigheid en comfort – en is daarmee ideaal voor MMA, BJJ, grappling, kickboksen of conditionele vechtsporttrainingen. De lengte tot halverwege het dijbeen geeft je maximale bewegingsvrijheid zonder in te leveren op bedekking en grip.

Optimale pasvorm dankzij mid-thigh design

De mid-thigh lengte van deze Hayabusa short zorgt ervoor dat je benen ongehinderd kunnen bewegen. Of je nu explosief trapt, snel van positie wisselt tijdens groundwork of in clinch werkt – deze short beweegt soepel met je mee. De zijsplitten zijn functioneel ontworpen voor extra mobiliteit tijdens trappen of takedowns. Het resultaat: minder beperkingen, meer controle.

Deze lengte is ideaal voor vechters die een sportieve, moderne pasvorm willen zonder overtollige stof of het gevoel dat je short je belemmert in snelheid of techniek.

Lichtgewicht én duurzaam materiaal

De short is gemaakt van hoogwaardig polyester dat zowel licht als slijtvast is. De stof is bestand tegen wrijving, trekken en intensieve training op de grond. Tegelijkertijd voelt hij comfortabel aan, met voldoende structuur om stevig te blijven zitten – zelfs tijdens explosieve bewegingen of sparring. De naden zijn versterkt en de afwerking is van topkwaliteit, zoals je mag verwachten van Hayabusa.

Blijft altijd perfect op zijn plaats

Niets is storender dan een short die afzakt tijdens het rollen of trappen. Daarom is deze Hayabusa MMA short uitgerust met een dubbele sluiting: een sterke klittenbandsluiting gecombineerd met een intern trekkoord. Deze combinatie zorgt ervoor dat de short perfect op zijn plek blijft, ongeacht hoe intens jouw training wordt. Geen afleiding, geen aanpassingen tussendoor – alleen focus.

Minimalistisch, functioneel ontwerp

De Apex Mid-Thigh Fight Shorts zijn ontworpen met een strak, clean design en subtiele branding. Geen felle prints, geen schreeuwerige kleuren – gewoon pure performance. De vlakke naden (flatlock stitching) zorgen ervoor dat je geen last hebt van irritatie of schuring tijdens lange sessies.

Het ontwerp zonder zakken of andere storende elementen maakt deze short volledig gefocust op prestatie, met een professionele uitstraling die past bij iedere serieuze vechter.

Voor elk type vechter en trainingsvorm

Deze short is breed inzetbaar: van MMA tot no-gi BJJ, van padwerk tot functionele krachttraining. De lichtgewicht stof is ademend en sneldrogend, waardoor je ook tijdens zweterige sessies fris en comfortabel blijft. Of je nu een beginner bent of op wedstrijdniveau traint – deze short past zich aan aan jouw intensiteit.

Buiten de training is hij ook geschikt voor warming-up, hersteltraining of casual wear naar de sportschool. De atletische snit en sobere look zorgen ervoor dat je er altijd scherp uitziet.

Gebouwd voor de lange termijn

Zoals je van Hayabusa mag verwachten, zijn ook deze shorts gemaakt om lang mee te gaan. De stof blijft elastisch en sterk, zelfs na veelvuldig wassen. De kleur blijft zwart en het design vervaagt of bladdert niet af. Of je nu 3 of 10 keer per week traint, deze short blijft presteren – sessie na sessie.

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SKU: 79212269283

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4.1 ★★★★★
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Par
Natrona Heights, US
★★★★★ 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
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Verified Purchase
Richard Hackathorn
Phoenix, US
★★★★★ 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
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Verified Purchase
Amazon Customer
Birmingham, US
★★★★★ 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
K
Verified Purchase
Kindle Customer
New York, 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
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Verified Purchase
Tommy Jonsson
Louisville, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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