The Rogue Kettlebell features high quality iron ore, a void free surface, single piece casting, and wide flat machined base. See all your options at Rogue Fitness.
mechanical minimalism: korg's phase8 + spare parts
Official Site - For Glory | SteelSeries
SteelSeries is a leading manufacturer of gaming peripherals and accessories, including headsets, keyboards, mice, and mousepads.
Realistic Waterfall with Bell UH1 / Realistischer Wasserfall in scale 1:48
3 Great ‘Workout Hikes’ in the Bay Area to Try (When You Can’t Face the Gym) | KQED
Want to get some heavy exercise without a treadmill? Pick one of these arduous — but rewarding — local mountain trails.
Pilates for Beginners: An Introduction | Pilates Anytime
Pilates improves balance, strength, and flexibility. Learn more why Mat and Reformer Pilates is good for beginners, then start your journey with us!
Pro — Minecraft Physics Mod
The Pro version of the Physics Mod. Advanced snow, liquid, door, trapdoor, vine, banner, cape, leash and fishing line physics!
LumaDeck — Live Visual Instrument for DJs
The live visual instrument for DJs and performers. Play the light.
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Current deep neural networks(DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate this issue by designing a weighting function mapping from training loss to sample weight, and then iterating between weight recalculating and classifier updating. Current approaches, however, need manually pre-specify the weighting function as well as its additional hyper-parameters. It makes them fairly hard to be generally applied in practice due to the significant variation of proper weighting schemes relying on the investigated problem and training data. To address this issue, we propose a method capable of adaptively learning an explicit weighting function directly from data. The weighting function is an MLP with one hidden layer, constituting a universal approximator to almost any continuous functions, making the method able to fit a wide range of weighting function forms including those assumed in conventional research. Guided by a small amount of unbiased meta-data, the parameters of the weighting function can be finely updated simultaneously with the learning process of the classifiers. Synthetic and real experiments substantiate the capability of our method for achieving proper weighting functions in class imbalance and noisy label cases, fully complying with the common settings in traditional methods, and more complicated scenarios beyond conventional cases. This naturally leads to its better accuracy than other state-of-the-art methods.
I’ve never used a trackball, but Keychron’s Nape Pro looks like the perfect one
This compact trackball seems very versatile.
The Ultimate Arpeggio Masterclass (Fundamentals + Essential Shapes + Workout) | Bradley Hall's Guitar School
Get more from Bradley Hall's Guitar School on Patreon
DIY synths database
Curated collection of DIY-friendly hardware synthesizers and related musical equipment you can build on your own. All open source.
