Save $240: Get the latest Dell G5 15 gaming laptop (16GB RAM, 512GB SSD) for $1,199.99 at Dell as of Oct. 27. The onslaught of new game releases is upon us this fall. With titles such as Watch Dogs: Legion kicking off the holiday rush, it's important to prepare yourself with an upgrade to the latest gaming gear. And for anyone who doesn't buy into the PlayStation 5 or Xbox Series X hype, the latest Dell gaming laptop is a fitting alternative. You save $240 or 17% off the original price as part of Dell's early Black Friday sneak peek.
Purchases you make through our links may earn us a commission. I don't know about you, but I've never gotten a techy or tech-adjacent gift I wasn't excited about--it's true. Opening up a gift and getting a new pair of headphones or a fun gadget I wasn't expecting is almost always fun, even if it isn't something I thought I wanted. That's the problem with trying to buy a fun new tech gift for a giftee: How do you single out the small margin of great products from the ocean of sub-par stuff? Easy--you check out our picks below. Reviewed tests tech all year long and this list is chock full of our favorite gadgets. Roku's fancy "Ultra" media streaming device has been our favorite for a couple years running now, and for good reason. Processing is snappy and the UI is extremely friendly and intuitive, making it easy to settle in for a night of Netflix (or Hulu, or Amazon Prime, or YouTube, or Twitch, or anything else) without a hitch.
This article aims to help anyone who wants to set up their windows machine for deep learning. Although setting up your GPU for deep learning is slightly complex the performance gain is well worth it * . The steps I have taken taken to get my RTX 2060 ready for deep learning is explained in detail. The first step when you search for the files to download is to look at what version of Cuda that Tensorflow supports which can be checked here, at the time of writing this article it supports Cuda 10.1.To download cuDNN you will have to register as an Nvidia developer. I have provided the download links to all the software to be installed below.
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GPUs have proven extremely useful for highly parallelizable data processing use-cases. The computational paradigms found in machine learning & deep learning for example fit extremely well to the processing architecture graphics cards provide. One would assume that GPUs would be able to process any submitted tasks concurrently -- the internal steps within a workload are indeed run in parallel, however separate workloads are actually processed sequentially. Recent improvements in graphics card architectures are now enabling for hardware parallelization across multiple workloads, which can be achieved by submitting the workloads to different underlying physical GPU queues. Practical tecniques in machine learning that would benefit from this include model parallelism and data parallelism.
The next generation of high-performance, low-power computer systems might be inspired by the brain. However, as designers move away from conventional computer technology towards brain-inspired (neuromorphic) systems, they must also move away from the established formal hierarchy that underpins conventional machines -- that is, the abstract framework that broadly defines how software is processed by a digital computer and converted into operations that run on the machine's hardware. This hierarchy has helped enable the rapid growth in computer performance. Writing in Nature, Zhang et al.1 define a new hierarchy that formalizes the requirements of algorithms and their implementation on a range of neuromorphic systems, thereby laying the foundations for a structured approach to research in which algorithms and hardware for brain-inspired computers can be designed separately. The performance of conventional digital computers has improved over the past 50 years in accordance with Moore's law, which states that technical advances will enable integrated circuits (microchips) to double their resources approximately every 18–24 months.
Single-board computers (SBCs) are wildly popular AI development platforms and excellent tools to teach students of all ages how to code. The de facto standard in SBCs has been the Raspberry Pi family of mini computers. NVIDIA of course has its own lineup of programmable AI development platforms in its Jetson family, including the recently-announced low cost version of the Jetson Nano. There are a host of others from the likes of ASUS, Hardkernel, and Google. Google's Coral development kit was a rather pricey option at $175, but now the same power is much more affordable.
Most of you are probably familiar with the chip giants like Intel & AMD which command a bigger share of the computing processor market, but this entrant to the chip market in 1993 has solidified its reputation as a big name in the arena. Although most well-known for its graphical processing units (GPUs) -- GeForce is its primary & most popular product line, the company also provides system-on-a-chip units (SoCs) for the mobile computing and automotive market. Since 2014, Nvidia has begun to diversify its business from the niche markets of gaming, automotive electronics, and mobile devices. It is now venturing into the futuristic AI, along with providing parallel processing capabilities to researchers and scientists that allow them to efficiently run high-performance applications. Let's review of some these endeavors.
Save up to $180: On Oct. 19, Walmart announced its 2020 take on early Black Friday deals: A string of three shopping events called Black Friday Deals For Days starting on Nov. 4. Retailers have likely been anticipating a different type of Black Friday chaos -- the virtual, socially-distant kind -- as shoppers have dodged physical stores since the first lockdown in March. With the in-store edge gone, it seems obvious to extend the online shopping portion and let customers get holiday-level deals for weeks. Walmart is taking advantage of this shift (as well as the high that shoppers are still running on from Prime Day) by inching away from the traditional stampede starting Thanksgiving night. In its place is Black Friday Deals For Days: A three-event string of deals leading up to Black Friday.
Google's Coral Dev Board mini has made a tantalizingly brief appearance for pre-order for $100 on Seeed's website – but stocks are already sold out, according to the company. Google unveiled its Linux Coral Dev Board mini in January, offering developers a smaller, cheaper and lower-power version of the Coral Dev Board, which launched for $149 but now costs $129. Instead of an NXP system on chip (SoC), the Mini combines the new Coral Accelerator Module with a MediaTek 8167s SoC, which consists of a quad-core Arm Cortex-A35 CPU. It also features an IMG PowerVR GE8300 GPU that's integrated in the SoC, while the machine-learning accelerator consists of the Google Edge TPU coprocessor that's capable of performing four trillion operations per second (TOPS) or two TOPS per watt. According to Google, it can execute mobile vision models such as Mobile:Net v2 at almost 400 frames per second. The device runs a derivative of Debian Linux called Mendel.