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Found 58 results

  1. Budget (including currency): 50,000 HKD Country: Hong Kong Games, programs or workloads that it will be used for: Machine Learning, CUDA Other details (existing parts lists, whether any peripherals are needed, what you're upgrading from, when you're going to buy, what resolution and refresh rate you want to play at, etc): Hey fellas, I am a student in a computer science lab and we want to build a pc for machine learning tasks. We don’t have much experience in setting up a heavy processing pc, hence I would like your help. A shop recommended us a build-up (attached in the post) The shopkeeper also recommended us to go with a higher power supply (mentioned in the end as optional) so that we may upgrade it to 4 GPU(s) in the future. However, we are skeptical if the current setup can fit 4 GPU(s). Are we right? We would like to have the option to add more GPU(s) in the future, given it doesn’t exceed the budget too much. Can you please suggest any required changes to achieve the same?
  2. Hi good day, My team is working on some neural network modeling for use in the medical sector. However some of the datasets we are working with are between 10 - 30 GB of data. We are running into some resource complications, having to truncate our data. Google Colab isn't a solution as we are outside the US and can't use Colab Pro. At the moment we can use a machine provided by a professor at our university, however this can only be used for some hours on some days. (i9-9900k, 64GB RAM, Nvidia RTX Titan). Even with this setup some data processing jobs takes 6-10 hours and even more. We are interested in building a Server to run JupyterHub for our use. I would like some recommendations on what would you all recommend for the CPU and the amount of RAM as we regularly hit the caps on both. As well as, some advice on how to ask companies if they are willing to sponsor some components to help further our contributions to the research field. Thanking you in advance for your time and help.
  3. Hello Fellow Scam Baiters! Okay, so that title is a little bit confusing. Here's the thing. I need a lot of sample data. I'm trying to train an Open AI GPT-2 model to try generate natural language texts and try to waste more time. Training these models takes a lot of data. Like, a lot of data. Like tens or hundreds of gigabytes of plaintext data. A lot of data. But, to get started, what better place to get started collecting said data than the Linus Tech Tips forum! SO, here's what I need from you. You can post as many replies as you want to this topic. For each correspondence (scammer contacts you, you respond), just copy what they said and how you responded. Drop as many replies as you want! For context on how your data will be used: I'll be manually moving data out of the forum onto a private database. One training point will be one correspondence, rather than an entire conversation. Think about it this way: when the scammer tries to get contact information, a lot of scam baiters will respond in a similar way. The machine model will try to pick up on some pattern there: ask for contact information means that I should respond by sending a fake name. So, if you want to make my job a little bit easier, only drop one correspondence in one reply. We do not anticipate any risks associated with collection of this information. Your participation is completely voluntary, and there are no penalties if you choose not to participate or skip any correspondences. By participating, you are providing important, real-life information that can be used to train the next generation of neural networks for natural language processing. You can remove your contact information and any email headers, as well as reply or forwarding headers. If you don't, they'll be removed in the data cleansing process. The datasets generated will not be made public unless by valid request (i.e., you're a student at an accredited institution and you're doing research). If you have questions about this policy, please send a direct message.
  4. Hi all - First time post & first time PC build. I have been a MacBook user in my personal life for 8 years, and use a PC for development at work. I am building a PC as my existing MacBook cannot support 4 screens, and its performance for machine learning is lackluster relative to what I am looking for. PCPartPicker Part List CPU: AMD Ryzen 9 3900X 3.8 GHz 12-Core Processor ($432.41 @ Amazon) Motherboard: Asus TUF GAMING X570-PLUS (WI-FI) ATX AM4 Motherboard ($189.99 @ Best Buy) Memory: Corsair Vengeance RGB Pro 32 GB (2 x 16 GB) DDR4-3200 Memory ($164.99 @ Best Buy) Storage: ADATA XPG SX8200 Pro 1 TB M.2-2280 NVME Solid State Drive ($149.97 @ Amazon) Video Card: MSI GeForce RTX 2080 SUPER 8 GB GAMING X TRIO Video Card ($818.00 @ Amazon) Case: Cooler Master MasterCase H500M ATX Mid Tower Case ($239.99 @ Amazon) Power Supply: Corsair RMx (2018) 750 W 80+ Gold Certified Fully Modular ATX Power Supply ($155.98 @ Newegg) Operating System: Microsoft Windows 10 Pro OEM 64-bit ($139.99 @ Other World Computing) External Storage: Seagate Expansion 8 TB External Hard Drive ($131.99 @ Adorama) Total: $2423.31 Prices include shipping, taxes, and discounts when available Generated by PCPartPicker 2020-05-01 01:14 EDT-0400 1. Budget & location $2600 United States. 2. Aim. I have two requirements: A. PC should be able to support 4 monitors for day trading. B. PC should support Machine learning workload. 3. Monitors. I do have the 4 27-inch monitors, and they have HDMI & DVI ports. ASUS VE278 with 1920×1080 resolution. I would like a recommendation for a 4-arm stand if someone has one. 4. Peripherals: I plan on using 64 bit Windows 10 Pro. Is there any other recommended software to buy on a new PC (e.g. for virus protection?) Already have mouse & keyboard. The biggest cost choices came down to CPU, GPU, and motherboard. While I originally was leaning with the Ryzen 3700X, I had extra budget and decided to go with 3900X since the 3800X seems like a little improvement. CPU for Machine Learning helps with some of the pre-processing of data prior to training, so I do see incremental value here. I decided to go with the Asus TUF GAMING X570-PLUS (WI-FI) ATX AM4 Motherboard as it will pair with my Ryzen 3900X and seems to have good reviews overall. Have had good experience with ASUS before. I have been torn between the 2080 Super vs 2080 Ti. I decided to go with the 2080 Super because for machine learning purposes, I would like to see some return on investment in regards to the work I expect to do on the machine. If I see the return on investment in my Machine Learning work, I may just spend more at a later time & get a second GPU to support my workload. One area that is particularly confusing for me is cases. I do care about the double-tempered glass with RGB light aesthetic, but with so many options and lots of variance in reviews, its hard to understand who delivers quality here. Some of the brands that seem good are Corsair, ThermalTake, and Cooler Master. Any recommendations here? The $240 for the case does seem pricey, but I think it should have good air flow. Welcome any feedback, thank you so much. This is a large investment for me.
  5. I was asked to prepare parts list for Machine Learning PC with budget of $1200 (roughly, real budget is 5000PLN which is bit over $1300 but prices in Poland may vary from US). So, here is part list of the build that I prepared: Main requirement was dual GPU for Computer Vision ML applications but with that budget I moved to single GPU with option to upgrade later down the line. But still couldn't go too high and I have chosen RTX 2060. Other parts are meant to stay as they are but still provide ways to upgrade (hence 850W PSU). So there are 2 slots for more RAM left, enough power for second GPU even along with more powerful CPU. Went for fast AF main storage M.2 NVMe SSD with 500GB to keep currently used data sets and 2TB HDD for long time storage of those. If there is something wrong, any overkill part, compatibility issues: please let me know. If there is anyone that does ML and has any advice feel free to roast me, I think advice would be most appreciated regarding GPU choice (keep in mind target dual GPU setup).
  6. I am going to university next year and I wanted to build a PC to take with me, one that can game but also do some machine learning and computing related tasks as I am applying to Computer Science. Also do some very light editing of videos and photos. I have the following part list: AMD Ryzen 7 2700X GIGABYTE AORUS B450 I PRO MSI RTX 2070 AERO ITX Corsair Vengeance LPX Black 32GB DDR4 3200MHz CL16 Dual Channel Kit 512 GB ADATA SX6000 M.2 SSD Seagate Barracuda Guardian 4TB Hard Drive Corsair SF600, 80+ Gold, 600W Velka 5 Case Any thoughts?
  7. Hey everyone, I am currently running an AMD FX8350 on a ASRock 990FX extrem 3 Mainboard and now facing a few issues while getting my K80 to work as a secondary GPU for handling ML workloads. Sadly i can not get BAR 64 (above 4G encoding) enabled on my mainboard, therefore i am currently looking for a cheap replacement. Are there any other search phrases besides "above 4G decoding" or "BAR 64bit" that i can search for in the mainboards datasheets? Or does anyone know how a AM3+ based Mainboard with two PCIE 16x slots, K80 support and BAR 64 option? My current setup is really bodgy 2 PSUs, one for each GPU, and a custom 3D printed fan setup for the K80. Was really fun to set it up, even though it feels kind of dirty Best regards Langhalsdino
  8. I am a huge fan of LTT and I happen to work on machine learning (in academia). Later this year, Dec 8th - Dec 14th, one of the biggest academic conference in machine learning, NeurIPS 2019 will be held in Vancouver. https://neurips.cc/ It might be interesting to make a video about it. Although the conference has an academic nature, all the hardware (Nvidia, Intel... ) or the software compaies (Microsoft, Google...) will be there and will demonstrate their latest technologies. Plus, I think it may also be interesting to interview some academics and ask them how the advances of PC hardwares has changed the machine leanring research. Just an idea.
  9. Hi, Been a long time LTT follower, but just joined the forum and this is my first post. The community here looks really nice - and thank you for being supportive I am looking to buy a laptop for doing serious software engineering work, including training ML models, etc. - which I need to learn/practice moving ahead. My OS of choice is Ubuntu (I am just used to Debian packages, and know where everything is, and feel very comfortable here). As this will be my only machine - it has to be very stable - and hence I am a little scared of getting systems like Razor Blade 15 or Asus ROG Z - where users have installed Linux distros like Ubuntu but with compromises and with some things not working (connecting to projectors, wifi issues, sleep-wake issues, etc) - and hence I am inspired to get a System76 laptop. I did consider a ThinkPad with Ubuntu (which is stable), but they don't seem to have a good graphics card anywhere which I will likely need to use with TensorFlow, etc. I am considering either an Oryx Pro which maxes out with an i7-9750H and RTX2080 (16.x 1080p 144Hz screen), or an Adder WS which maxes out with an i9-9980HK and RTX2070 (4K OLED screen - which I don't really need but I'm not complaining about either). I don't know which of these combinations should I go for considering that this is a laptop (thermals), I need it mostly for coding, ML training, etc. and also - maybe once in a while - running a few games on Steam (but that is not a priority in any way). Can someone please help me out here? I very much appreciate your advise! Thank you again Oryx Pro: https://system76.com/cart/configure/oryp5 Adder WS: https://system76.com/cart/configure/addw1
  10. Hey all! I'd love an opinion on something. For a few years now i've been mainly using a MacBook pro (currently i7 6 cores, 16GB ram, radeon 560x) and lately i've been considering adding a more powerful desktop to my rig and since I have no intention whatsoever on shelling out for an iMac pro/mac pro i've been seriously considering getting a windows/linux desktop system instead. The Aim for the system is mainly for work (coding/machine learning/unity and other computational intensive tasks) but probably also some gaming. As for monitors, for the time being i'll probably use my existing dual dell US27 1440p displays and i'm all set with peripherals. Budget (I adjusted for price differences): 2500$ US prices Lastly, i'll probably hold off any purchase till I see how everything unfolds with Zen3/RTX super etc... so you can take that under consideration Upgrade reason: There's a limit to what a laptop can do compared to a desktop. Main question: Is it possible to build a system that will answer the needs written above and also being an mini itx system which will be thermally stable (not throttling) and quiet? If so, what do you recommend? Thanks in advance for your advice, Pizza.
  11. Sources: New York Times, Author's Note (via Google Docs), OSF (pre-print) wang_kosinski.pdf Comments could get nasty here. I need your moderating powers @iamdarkyoshi for anyone with comments against the CS (political/religious rants) or derails the topic. It resembles a lot of a topic I posted before about an AI predicting the outcome of a relationship. I won't lie that it got me concerned for a bit but reading the actual paper and the FAQ (Google Docs) the authors provided made me think it's indeed a way to warn people about the repercussions of artificial intelligence and machine learning. It reminds me when Elon Musk said that Mark Zuckerberg doesn't know the dangers of AI if left unregulated. I'm with Elon Musk on this one. On the lighter side of things, this technology can be used by dating apps targeted to gay individuals but then, I'd rather see the person face to face rather than letting an AI do it for me. Looking at the comments of other experts, it's obvious that the algorithm is very limited on their sample size and subjects since they only used gay white men and women. The thesis authors said in their Google document that: Just imagine you're a tourist in a country and a facial scanner at the airport identifies someone as gay or lesbian, they can immediately put you in a database. Just imagine oppressive countries who have staunch religious doctrines using this to identify closeted gay people and have them thrown from top of buildings or just be shamed by society into committing suicide. Or better yet, just imagine North Korea developing an AI who determines if someone has doubts on the regime or is planning to defect to South Korea. Knowledge is power and just like a knife that can be used to stab someone or cut that delicious medium rare steak, this algorithm is also a double edged sword. At the moment, it's not ready for prime time as it has limited sample diversity and the authors admit that. In fact as stated in their Google Doc that they want to be wrong and they are terrified of the results. More refinement is definitely needed and just like any scientific theory, it needs to be replicated. At the moment, all humanity knows about the potentials and risks of AI and machine learning is just at the tip of the iceberg. So there's no need to be concerned or alarmed just yet but we also shouldn't dismiss a scientific finding just because we disagree with it or it could pose harm to us and in fact, I would like for this paper to be published soon in the Journal of Personality and Social Psychology. I'm not a Psychology major but I encourage people with that degree to replicate this thesis but with much diversified subjects. Also, it should be kept in mind that "peer-review" is not always cranked up to be as a method of validating or dismissing a theory. Peer-review is important in academiai but most of the time, it's just validating research methodology, checking grammar and look for suspicious and interpolated/forged results. Peer-review doesn't determine if a thesis' conclusion is right or wrong. In fact, there are a lot of bogus theses that are "peer-reviewed". The only way to determine if a thesis' conclusion is right or wrong is if it's replicated and check the results to see if they match up or not. [See NCBI, Nature, CBC, WSJ) Again, I highly encourage everyone reading this thread to read this Google Doc as it clarifies a lot of concerns on privacy, human rights, etc. https://docs.google.com/document/d/11oGZ1Ke3wK9E3BtOFfGfUQuuaSMR8AO2WfWH3aVke6U/edit?usp=sharing
  12. I've been watching a streamer on Twitch for the past couple of months who is building a game agent framework that allows you to play any game (Steam and non-Steam) automatically through various machine learning and "artificial intelligence" algorithms. It's been heavily developed and I wanted to share it with you guys as the developer released the first Beta today. Link to the GitHub repository: SerpentAI Personally, I'm a primarily C++ programmer and this framework got me into Python as well as exploring machine learning. Perhaps this framework triggers the creative minds of some of you. - d0p3t
  13. Hi, I've been been working in the field of Machine Learning for over 7 years now and have been recently handed over a significant chunk of funding to aid my adventures. I require a work horse machine capable of handling immense work loads with lots of computing. i value the suggestion of the LTT community and would like to put my plan forward for your review and criticism. my max budget is about 7K$ ( is flexible) and I've come up with a PC part picker list. i of course will have to find a source to buy/ ship it to India my base of operations. unfortunately it's coming 1k over budget and i would your suggestions in come cost cutting. https://pcpartpicker.com/list/xLLDPs here is the list. the mains here lots of RAM, beefy GPU and enough cores to last a long time with 24x7 operation handling hundreds of gigabytes of workload.
  14. Hello All, This is my first post to the forum, so please take it easy on me. I recently bought a 2006 Mac Pro (bought it for a $1). The unit has a bad motherboard in it, so most of the components are either too old or not worth reusing. It did however have 2 Xeon processors inside, I plan on using for a later project. The parts I plan in reusing are the case itself, the power supply 980 Watts, and the fans. The rest is junk Here's my idea: I want to build a Machine Learning Box that I can use for my Master program. I want to use it to train models, be used in some neural net applications (training/testing), things like that. I have been looking all over the internet, for tips and guides on what makes the best bang for your buck machine. I have used some gaming builds as a starting point, then tweaking to my use case. The machine will run Ubuntu 14.04 or 15.04 primarily with possible dual boot support for Windows. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Build Requirements: Intel Chip: i7 or Xeon (Based on the total budget for the project I believe i7 is the only option) NVIDIA GPU(s): For the initial build phase I will buy 1 GPU, with the intention of buying a second down the road. Thus SLI is important. Plus the developer support with DIGITS, CUDA cores, etc are important. Supporting Motherboard: Ideally having a motherboard that would drop into the Mac Pro case would be AWESOME! But I am almost certain some mods to the case will have to be made. Also, having fast I/O, PCI expandability, and M.2 support are feature requests. Somethings I found that should be important for this build include: PCI Express Lanes -> I believe that 40 lanes is a requirement because I plan on having 2 GPU's (Not sure yet which NVIDIA card I want yet), and I would like the lanes to be divided equally between the 2 GPU's (16x16). That way I avoid any possible bottlenecks. RAM -> From what I have read for this use case, it is best to have enough RAM (memory) to hold entire datasets. My plan is to start with 32 GB initially, then move up to 64 GB and a step 2, then possible to 128 GB as a step 3. So expanding RAM is important. At the time of writing this I believe that quad-chanel Memory is the way to go, the benefit being speed. What do you guys think? M.2 -> I want the system to be extremely fast. I would like to use a M.2 solution for holding the OS (Ubuntu), and OS related file, and applications. All the datasets will be held in Secondary Storage (Western Digital Red Hard Drive) Question I have: 1) When deciding in a CPU, should I go with more cores and a slower clock? Or fewer cores and a higher clock? (Based on this use case) 2) Does my assumptions for PCI Express Lanes hold true? Would there be a bottleneck if 1 GPU is getting 16 lanes and the other is getting 8? 3) Is there any guides that you guys know of that I could follow? 4) What NVIDIA GPU's have the best SLI support? Or which GPU have been found to work best in this use case? (I know Titan X is great, but please keep the budget in mind) 5) Do you guys have any suggestions on the build? Summary I have made 2 different PC Part Picker lists that can be found at: https://pcpartpicker.com/user/sprichard/saved/#view=8TXRBm They are listed under the "Mac Pro $1000 Build" and "Mac Pro $1500 Build" As you can see from the link I have build for $1000 budget and $1500 budget. I am working on a $2000 budget build. I would say $2000 is the limit for this build. Let me know what you guys think! I am open to all suggestions as i have not purchased anything, and I am still trying to get my head wrapped around all that I need to know to make a badass Mac Pro ML Box.
  15. Hi what a first post on the forum, but I just recently was given 2 dell poweredge R710s with 48 gbs of ram each and no hard drives I'm currently using a program called knime on my desktop quad core with 8gbs of ram to do a research project for one of my classes in school which involves image classification. I was wondering is it possible to take a 250 gb ssd with windows 10 and just stick it in one of the power edges to take advantage of the processing power and if so how can I go about doing that because at the moment my quad core can not handle the load I'm putting on it. All help is appreciated
  16. I am just starting to learn about neural networks using Tensorflow.js. Does anyone have any idea for a simple dataset I could use for a regression problem?
  17. Hello there! I want to build a slightly future proof build for my deep learning projects. Some of the specs that I am considering now are: ASUS Z10PE-D8 WS INTEL XEON E5 2620 V4 (only 1 for now) GTX 1080ti (2 way sli). Also, don't know which is better? founder edition or aib? like EVGA's etc. Help me in building the compelete rig for deep learning projects. Tell me please if I am thinking in a correct way for a future proof rig? Thanks in advance.
  18. Hey guys, I am going to build a machine learning focused build, for my studies in university. Maybe going to have a budget of 100,000 PHP (1300 - 1800 USD). Right now this is the build I have planned: https://pcpartpicker.com/list/h8BccY CPU: Ryzen 7 1800x CPU Cooler: Noctua NH-D15 Mobo: Asus Crosshairs Hero (Wifi-AC) RAM: G-Skill Ripjaws (2 x 16) 32 GB Storage (boot): 500 GB Samsung 960 Evo Storage (Mass): 2, 2TB WD Black in RAID1 GPU: Titan XP PSU: Corsair AX760 Case: Phanteks Enthoo Pro M Please note that: I will primarily use Tensorflow and Scikit-Learn Libraries. I am relatively new to PC building, and machine learning. The Titan XP will be lent to me by the university not bought. So, I would gladly accept any advice to make my rig better. Also, please say if there are any incompatibilities with my hardware choices, so I can make the appropriate modifications. Any recommendations will be greatly appreciated. Again, I am relatively new to pc building and hardware, so please be nice. Thank you for your response.
  19. Sources: Cornell University Library, Science Mag, and Threat post The article however didn't mentioned password managers so I guess it's safe to use a reliable password manager with two factor authentication. I have a feeling that as AI, machine learning and neural engines become more powerful, we might see cyber attacks much more serious. At the moment, it predicts what passwords are the easiest to guess to give companies chance to change their weak passwords into a more secure one. But as far as I'm concerned, most websites don't read passwords as plain text like "I<3myhotboss", websites read it hashed like this one "eed4b508e6f5acda3178c880bc490546" and I think there's already an online database containing hashed passwords that are used by hackers to brute force. But then, I can see this being used by legit password managers and they'll notify the user if the password they're using is easy to guess or has been used somewhere else so that they'll notify the user to change for a more secure password. So I'm all for this and I hope this will be implemented to current password managers.
  20. In a step forward for the human-machine interface (or in a scary step to losing your privacy) a study has been published on a new approach for a Neural Network using Machine Learning to interpret functional Magnetic Resonance Images (fMRI) of the visual cortex into actual images. From the MIT Technology Review: https://www.technologyreview.com/s/604332/mind-reading-algorithms-reconstruct-what-youre-seeing-using-brain-scan-data/?set=604340 Looking at the DGMM images, they definitely appear to be the closest to the original presented images... Just hope that this doesn't turn into an Orwellian situation. https://www.engadget.com/2017/05/09/neural-network-recreates-brain-images/
  21. So I am looking to purchase my first GPU and I could use some advice. I would like for the GPU to do well with machine learning and all that great data science stuff. Of course, I sure wouldn't mind if it handled graphics well too. After reading this article (there's a TLDR near the bottom), it seems to me that a GTX 960 or GTX 680 might be about what I'm looking for. I'd rather not get something too much more expensive. This is just my first GPU and I'd like to get a good bang for my buck. I'm not really sure about how often new GPUs come out. I saw the 960 came out in January, when might the next iteration come out? Might I be better off waiting a bit? Is something newer/better for me coming soon? Not sure how much of a difference this will make, but this is what else is in my computer right now: MSI Z87-G43 motherboard Intel i7 4770 - btw, the integrated graphics on this are all I've been using 8 GB RAM Samsung SSD Thanks for your help
  22. https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/Smart-Auto-Cropping-of-Images.html This was a bit of news @RadeonInstinct shared on their twitter from the twitter blog https://twitter.com/RadeonInstinct/status/957342515337793536 They continue with Twitter has looked into eye tracking data from academics records (Check the blog for illustrations) They then state that the tech is too slow for use in production. Although they don't need to run this on every single image since they have The blog post ends with Plus an acknowledgment to the people that have made this happen. 1. Iteration is the act of repeating a process, to generate a sequence of outcomes, with the aim of approaching a desired goal, target or result. Personly; I think that this is really cool, as long as it's only used for image cropping
  23. Hello and sorry in advance if I posted this in a wrong part of the forum - I'm new here. I wanted to suggest to the LTT team to start doing machine learning benchmarks and wanted to provide them with a relatively simple way to do it. I am a physicist and doing particle physics research is almost entirely based on machine learning nowadays. Seeing some benchmarks of new GPUs and CPUs would be rather beneficial - knowing stuff like whether a much cheaper 1080Ti is just as fast as a K80 is definitely useful to many people in that niche - all you nerds screaming now about the bonus of double precision I know it is important but not for all research. I wanted to propose to start benchmarking using TensorFlow from Google, mostly because of the ease of use and automated threading - trust me, you don't want to use CERN's proprietary ROOT, it's ugly and outdated by around 20 years. Below I link a project on my GitHub which uses MNIST (handwritten digits) examples passed through a Convolutional Neural Network for classifying them - extremely simple computer vision if you'd like. I also added almost full support for TensorBoard results visualisation software which is rather marvellous though its small precision on time measurements may make you want to add a snipett of code to measure it yourself (or make many more epochs than this example - easily tweakable - and divide by total time shown and live with the slight error). Link: https://github.com/krisfur/TensorBoard-CNN-Visualization-Example For benchmarking CPUs I'd suggest using the not GPU-accelerated version of TensorFlow. For benchmarking GPUs naturally install the GPU accelerated version, but remember that if installation of that has problems you can overcome some of them by disabling the integrated GPU of your CPU - definitely need to do that on laptops and we are reaching a point where laptops could actually do some basic machine learning stuff. Also you should run it with at least 16GB of ram as the final evaluation basically loads the whole thing onto the memory for evaluation. More ram isn't a problem for you guys anyway. I will attach some screenshots of how nice the TensorBoard output looks. I'd love to provide something different than the usual MNIST example but I'm not in possession of some good practice data that I would be allowed to share and can't be bothered to make some mock data as you may still never use it. Sidenotes: Benchmarking based on final accuracy isn't always the best choice, there's a lot of randomness involved - you should only care about the time. You could save a trained model and just run test batches through it if you want to do that, but that's a bit more work than I have time to describe here - not necessarily hard, but you wouldn't be able to get a lot of data about training speed if you don't do the training (duh!). MNIST images are tiny so training times are short, meaning you can benchmark a lot quickly, with bigger images you'd need to do some tweaking to the CNN and it would take a long time. I remember doing computer vision on ~1500x1500px images and it literally took days on a system with two K80s and we did have to introduce a downsampling mechanism. Also using MNIST data means you just download them every time and delete afterwards, so do have an internet connection. Using your own images requires making a way of reading and batching them which is a day's work. If you do end up using this could you please mention me in a video, my mates would die haha. If not it's fine anyway. Cheers from London.
  24. I am new to the computer build scene, and i am looking for some advice for the best hardware / computer configuration for my new and first proper workstation. Some background information on what i need and where i would like to use it for. I am a multimedia designer / software engineer. And i have always worked with a mac book pro 13 inch. Since that is all i needed so far. But atm i am expanding my interests into 3D rendering and machine learning / AI. And my mac is really lacking in both departments . So my main requirements for this new pc are render speed and speed for modeling models for machine learning / ai. And secondary it would be great to use it for some gaming, but this is definitely NOT my main importance for this pc. So I thought it was time to upgrade to a proper work machine. I did some basic research on my needs and come to the following list of parts for my pc: https://pcpartpicker.com/list/zrpqKZ So my question is are these the best parts for my needs, and are these parts compatible and efficient together? My budget is max 2000 euro. I am open to all your suggestions and tips! And i really appreciate all the help i can get If there is any more information you may need please let me know Best Thomas
  25. Sources: The Next Web & Motherboard (Vice) As compliance to the community standards, I've decided not to include any images of these fake porn GIFs. This reminds a lot of that NVIDIA AI (probably powered by their Titan V) that generates pictures of people but not really from living people. Here's another example of AI that can generate fake videos using machine learning This is creepy. It looks like Elon Musk's warnings about AI not being regulated is real. Google implemented some sort of machine learning to Google Photos to pattern match faces and objects. Say if I search for "Luke Lafreniere" or "Linus Sebastian" on my Google Photos app, it can tell which photos have their faces. I search for Christmas tree, it will show which ones have it. I don't think Google despite their notorious privacy invasions will be nefarious enough to create a fake sex tape of their users but this becomes problematic if the AI described in the OP becomes used in revenge porn. Remember how Facebook is trying to combat revenge porn by sending them nude photos and hashing it to prevent from being sent to other people. Looks like Facebook needs to up their machine learning algorithms further if they're serious in combating revenge porn. Basically with this new AI, anyone can take a normal video of you from YouTube and create short video clips of porn. While it is easy at the moment to spot fake and real sex tapes, the mere fact that many people on Reddit are already self-completing with these AI generated porn clips is already concerning. They also pointed out that you don't need special hardware to do AI porn and all you need is a decent graphics card and processor (probably RX 480 or GTX 1060 and Ryzen 5 1600 or i5-6600) to do this. But the problem is that way too many people are gullible enough to believe everything on the Internet. Someone with a beef or vendetta can use this AI as retaliation. The first victims of these are celebrities. There are studies that demonstrate that porn viewing desensitizes ones genitals and reduces white matter in the brain. https://www.wired.com/2014/06/is-it-really-true-that-watching-porn-will-shrink-your-brain/ The first part of this AI porn maker would be much worse celebrity beefs, then followed by fake political attacks and fake sex tape allegations to make up stories about your arch nemesis. It will be worse than a leaked burn book just like in Mean Girls.
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