22 skills found
EDCD / EDMarketConnectorDownloads commodity market and other station data from the game Elite: Dangerous for use with all popular online and offline trading tools.
EDCD / EDDICompanion application for Elite Dangerous
abusufyanvu / 6S191 MIT DeepLearningMIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning
KIMB-technologies / Radio APISilicon Frontier, Frontier Silicon, or Frontier Nuvola (Smart) Internet Radio alternative Web API — 📻💡
flammy / FsapiFrontier Silicon API for PHP
crawler-commons / Url FrontierAPI definition, resources and reference implementation of URL Frontiers
Marginal / HabZoneEDMC plugin to help explorers find high-value planets
Athanasius / Fd ApiCode dealing with Frontier Developments' API(s) for Elite Dangerous
tiwilliam / FsapiFrontier Silicon API for Python
z1c0 / FsApiFrontier Silicon API for .NET
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http://edge.linknetott.swiftserve.com/live/BSgroup/amlst:foodiee/playlist.m3u8?fluxustv.m3u8 #EXTINF:-1 group-title="LOCAL CHANNEL",GLOBAL TV HD https://live.rctiplus.id/rctiplus/gtv_720p.m3u8 #EXTINF:-1 group-title="SALURAN LOKAL",GTV https://live.rctiplus.id/rctiplus/gtv_720p.m3u8 #EXTINF:-1 group-title="SALURAN LOKAL",Rumah & Tempat Tinggal http://edge.linknetott.swiftserve.com/live/BSgroup/amlst:homelivinghd/playlist.m3u8 #EXTINF:-1 group-title="LOCAL CHANNEL",INDOSIAR HD http://id1.indostreamingtv.com/live/indosiar/index.m3u8 #EXTINF:-1 group-title="SALURAN LOKAL",iNews Plus https://live.rctiplus.id/rctiplus/inews_720p.m3u8 #EXTINF:-1 group-title="SALURAN LOKAL",J'go http://edge.linknetott.swiftserve.com/live/BSgroup/amlst:jgoch/chunklist_b2288000.m3u8?fluxustv.m3u8 #EXTINF:-1 group-title="SALURAN LOKAL",METRO TV HD http://edge.metrotvnews.com:1935/live-edge/smil:metro.smil/chunklist_w2006790992_b1492000_sleng.m3u8 #EXTINF:-1 group-title="LOCAL CHANNEL",MNC TV Plus 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http://203.153.218.28:9981/stream/channelid/1943600623?ticket=694FA36A0D756D8572C705243A10521FAF796351&profile=pass #EXTINF:-1 group-title="INDIHOME", Saluran Makanan Asia http://203.153.218.28:9981/stream/channelid/1092664456?ticket=E20984982529618B739BCAB7412D568D65147A93&profile=pass #EXTINF:-1 group-title="INDIHOME",AXN http://203.153.218.28:9981/stream/channelid/882386103?ticket=DF543FFEC6C497F485D61A1907E83292A2DB1741&profile=pass #EXTINF:-1 group-title="INDIHOME",Bloomberg International http://203.153.218.28:9981/stream/channelid/78064174?ticket=4490D953FCAC1483DE94BE2C4D1F241E5319BCD8&profile=pass #EXTINF:-1 group-title="INDIHOME",Semut Biru Ekstrim http://203.153.218.28:9981/stream/channelid/1299824667?ticket=EDA811CE90A26AD414D02B63E0A9171A68A9454C&profile=pass #EXTINF:-1 group-title="INDIHOME",Cinemax http://203.153.218.28:9981/stream/channelid/255605374?ticket=8419D5E6A24EE915F246B23011407540BE9B0E50&profile=pass #EXTINF:-1 group-title="INDIHOME",CNBC International http://203.153.218.28:9981/stream/channelid/1744735983?ticket=07E4904EDFA549D56E57107CB6140923FE593775&profile=pass #EXTINF:-1 group-title="INDIHOME",Discovery Channel http://203.153.218.28:9981/stream/channelid/1151931232?ticket=D05F5F5AB713CBADFBFABB642E4F37D5004BB751&profile=pass #EXTINF:-1 group-title="INDIHOME",Saluran Disney http://203.153.218.28:9981/stream/channelid/31701170?ticket=02DB3A3E44B8E741CC4D2913B2F42F452A2C3AB9&profile=pass #EXTINF:-1 group-title="INDIHOME",Film Keluarga FOX http://203.153.218.28:9981/stream/channelid/1949182550?ticket=07F04FD978CF0D1626AA28A8A68A1182729AC6EE&profile=pass #EXTINF:-1 group-title="INDIHOME",Film Fox http://203.153.218.28:9981/stream/channelid/756717990?ticket=B63A8893B7D332F2F7ACFD3C14E761032035F66E&profile=pass #EXTINF:-1 group-title="INDIHOME",Saluran Sejarah http://203.153.218.28:9981/stream/channelid/1868154562?ticket=21CAD98B63EE6B1D16AFD5B33F7C68F48FA445CD&profile=pass #EXTINF:-1 group-title="INDIHOME",NAT GEO People http://203.153.218.28:9981/stream/channelid/1114683377?ticket=39736C153BA658BB8604E39D479282644E09703E&profile=pass #EXTINF:-1 group-title="INDIHOME",NAT GEO Wild http://203.153.218.28:9981/stream/channelid/1497533758?ticket=36B439E64C5884A941F9938AD9373C4B849363BA&profile=pass #EXTINF:-1 group-title="INDIHOME",National Geographic http://203.153.218.28:9981/stream/channelid/681683355?ticket=B3C34399E8376048D3433529578519E51673EA32&profile=pass #EXTINF:-1 group-title="INDIHOME",Nickelodeon Asia http://203.153.218.28:9981/stream/channelid/648970596?ticket=72A19F89119921817ADF08AEBFF2D7E0009D1A20&profile=pass #EXTINF:-1 group-title="INDIHOME",Setanta Sports http://203.153.218.28:9981/stream/channelid/981299476?ticket=3A4FD3607BE4AAC6DAA7E01E60F3218471B987DD&profile=pass #EXTINF:-1 group-title="INDIHOME",TLC http://203.153.218.28:9981/stream/channelid/391618553?ticket=08323DCACE84958A87EA829F012E919CA861DF74&profile=pass #EXTINF:-1 group-title="INDIHOME",TVRI Sports http://210.210.155.35/session/69e800f6-97c0-11e9-888f-b82a72d63267/qwr9ew/s/s107/01.m3u8 #EXTINF:-1 group-title="MOVIE",DC COMIC TV https://aldirect.hls.huya.com/huyalive/29169025-2686221436-11537233217434157056-2847699210-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",DWAYNE JOHNSON TV https://aldirect.hls.huya.com/huyalive/28466698-2689656864-11551988268341919744-2847699194-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",Film Pertama http://edge.linknetott.swiftserve.com/live/BSgroup/amlst:jgoch/chunklist_b2288000.m3u8 #EXTINF:-1 group-title="MOVIE",FLIX http://pockettv.xyz/api/zee.m3u8?c=andflixhd #EXTINF:-1 group-title="MOVIE",FOX MOVIES http://203.153.218.28:9981/stream/channelid/756717990?ticket=B39138F5DEF9CCED9C08D27C9766035E330AEBA3&profile=pass #EXTINF:-1 group-title="MOVIE",TEMAN TV http://aldirect.hls.huya.com/huyalive/29169025-2686220018-11537227127170531328-2847699120-10057-A-1524041208-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",FILM LUCU TV https://aldirect.hls.huya.com/huyalive/30765679-2554414680-1097112751022305280-3048991634-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",FILM HOLLYWOOD 1 http://aldirect.hls.huya.com/huyalive/29169025-2686219962-11537226886652362752-2710080226-10057-A-0-1.m3u8 #EXTINF:-1 group-title="MOVIE",FILM HOLLYWOOD 3 http://aldirect.hls.huya.com/huyalive/30765679-2504742278-10757786168918540288-3049003128-10057-A-0-1.m3u8 #EXTINF:-1 group-title="MOVIE",FILM HOROR http://170.178.189.66:1935/live/Stream1/chunklist_w929463259.m3u8 #EXTINF:-1 group-title="MOVIE",FILM HOROR JEPANG https://aldirect.hls.huya.com/huyalive/30765679-2554414808-10971128060778119168-3048959636-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",MARVEL SUPERHEROES TV https://aldirect.hls.huya.com/huyalive/30765679-2504742278-10757786168918540288-3049003128-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",film MBC 2 http://livecdnh3.tvanywhere.ae/hls/mbc2/05.m3u8 #EXTINF:-1 group-title="MOVIE",film MBC Max http://livecdnh3.tvanywhere.ae/hls/mbcmax/05.m3u8 #EXTINF:-1 group-title="MOVIE",MKC MOVIE 8 http://js.hls.huya.com/huyalive/30765679-2478268764-10644083292078342144-2847699106-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",Film 29 http://aldirect.hls.huya.com/huyalive/30765679-2504742278-10757786168918540288-3049003128-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesDisney http://95.170.215.118:80/hls/m3u8/Sky-MoviesDisney.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesDrama. http://95.170.215.118:80/hls/m3u8/Sky-MoviesDrama.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesFamily http://95.170.215.118:80/hls/m3u8/Sky-MoviesFamily.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesPremie http://95.170.215.118:80/hls/m3u8/Sky-MoviesPremie.m3u8 #EXTINF:-1 group-title="MOVIE",film VH1 http://content-ausc2.uplynk.com/channel/7a16e3d5ffd0413ba4d8ac89688ed7cd/e.m3u8?ct=c&ad.locationDesc=vh1_channel&ad.cust_params=_fw_ae%3D53da17a30bd0d3c946a41c86cb5873f1%26_vmn_ar%3Dtrue&expand=simulcast_standard&ad.kv=_fw_ae%2C53da17a30bd0d3c946a41c86cb5873f1%2C_vmn_ar%2Ctrue% 2C_fw_vcid2%2C82125:vh1_67b04b0c30774b06ad1ec820ee7d0beb&delay=10800&euid=67b04b0c-3077-4b06-ad1e-c820ee7d0beb&exp=1531977295&rn=1284714433&ad.euid=67b04b0c-3077-4b06-ad1e-c820ee7d0beb&cid=7a16e3d5ffd0413ba4d8ac89688ed7cd&tc=1&sig=b3143cb0429574cc3c877de30e62fab7d66c763ec2f61cd9bfde33fdab12dfb7&pbs=62317bc417264eaeb05c22777b5c8697 #EXTINF:-1 group-title="FILM CAMPURAN",12 TAHUN BUDAK http://www.deadlyblogger.com/NewRelease/12slave.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",28 HARI KEMUDIAN http://www.deadlyblogger.com/NewRelease/28days.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",3 HARI UNTUK MEMBUNUH http://www.deadlyblogger.com/NewRelease/3days.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",300 RISE OF AN EMPIRE http://www.deadlyblogger.com/NewRelease/300rise.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",47 RONIN http://www.deadlyblogger.com/NewRelease/47ronin.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",A Haunted HOUSE 2 (COMEDY) http://www.deadlyblogger.com/NewRelease/haunted2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",MALAM DI ROXBURY http://www.deadlyblogger.com/NewRelease/roxbury.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",A.Dog's.Way.Home.2019 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/547.mkv #EXTINF:-1 group-title="FILM CAMPURAN",A.Dog's.Way.Home.2019.720p.BluRay.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/581.mkv #EXTINF:-1 group-title="FILM CAMPURAN",DENGAR http://www.deadlyblogger.com/NewRelease/afflicted2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",AFTER EARTH http://www.deadlyblogger.com/NewRelease/after.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Air_Strike.2018.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/553.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Aladdin (2019) https://www.googleapis.com/drive/v3/files/1WgX85ihHMCU5lJ-2rO9XqKbCnlvUczY-?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Alexander-DVDRip.AC3-5.1[Eng]2004_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/554.mkv #EXTINF:-1 group-title="FILM CAMPURAN",ALI http://www.deadlyblogger.com/NewRelease/ali1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Alladin 2019 http://ftp.alphamediazone.com/Movies/Hollywood/2019/Aladdin%20%282019%29/Adventures.Of.Aladdin.2019.1080p.BluRay.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Alpha.2018.WEB-DL.x264-FGT http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/556.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Selalu.Jadilah.Saya.Mungkin.2019.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/557.mkv #EXTINF:-1 group-title="FILM CAMPURAN",AMERICAN GANGSTER http://www.deadlyblogger.com/NewRelease/agangster.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",AMERICAN HUSTLE http://www.deadlyblogger.com/NewRelease/hustle2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",An.Interview.with.God.2018 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/558.mkv #EXTINF:-1 group-title="FILM CAMPURAN",ANCHORMAN 2: LEGENDA BERLANJUT http://www.deadlyblogger.com/NewRelease/anchorman2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",ANCHORMAN THE LEGENDS OF RON BURGUNDY http://www.deadlyblogger.com/NewRelease/anchorman.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Angel Has Fallen (2019) https://www.googleapis.com/drive/v3/files/1e8zI98_BM0K4sVMo9byeInovIqfwLk7F?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Angel Of Mine (2019) https://www.googleapis.com/drive/v3/files/19JVSSekPOaBJhx6uATOHKuadjHVIarV7?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",HEWAN http://www.deadlyblogger.com/NewRelease/animal.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Aquaman.2018 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/559.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Arctic.2019.HC.HDRip http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/560.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Asteriks.2018.HRSink.720p-BD4YU http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/562.mkv #EXTINF:-1 group-title="FILM CAMPURAN",At.Middleton_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/563.mkv #EXTINF:-1 group-title="FILM CAMPURAN",AUSTIN POWERS http://www.deadlyblogger.com/NewRelease/austin1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",AUSTIN POWERS: THE SPY YANG SHAGGED ME http://www.deadlyblogger.com/NewRelease/austin2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Avengement.2019.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/564.mkv #EXTINF:-1 group-title="FILM CAMPURAN",BAD BOYS 2 http://www.deadlyblogger.com/NewRelease/badboys2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BAD BOYS http://www.deadlyblogger.com/NewRelease/badboys1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",KECUK BURUK http://www.deadlyblogger.com/NewRelease/badgrandpa2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",KATA BURUK http://www.deadlyblogger.com/NewRelease/badwords.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Balkanska_medja_(2019) http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/567.mkv #EXTINF:-1 group-title="FILM CAMPURAN",BALLER BLOCKIN http://www.deadlyblogger.com/NewRelease/blockin.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BATTLE FOR THE PLANET OF THE APES http://www.deadlyblogger.com/NewRelease/apes5.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BATTLE OF THE YEAR http://www.deadlyblogger.com/NewRelease/battleyear2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BEANEATH PLANET OF THE APES http://www.deadlyblogger.com/NewRelease/apes2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BELLY http://www.deadlyblogger.com/NewRelease/belly1998.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BEWITCHED http://www.deadlyblogger.com/NewRelease/bewitched.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Biser.Bojane.2017 (1) http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/568.mkv #EXTINF:-1 group-title="FILM CAMPURAN",BLADE 1 http://www.deadlyblogger.com/NewRelease/blade1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BLADE 2 http://www.deadlyblogger.com/NewRelease/blade2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BLADE 3 http://www.deadlyblogger.com/NewRelease/blade3.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BLADES OF GLORY http://www.deadlyblogger.com/NewRelease/glory.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Blockers.2018.BRRip.XviD.MP3-XVID_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/569.mkv #EXTINF:-1 group-title="FILM CAMPURAN",BLOOD IN BLOOD OUT/ VATOS LACOS http://www.deadlyblogger.com/NewRelease/blood.mp4 #EXTINF:-1 group-title="FILM 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DANCING (1987) http://www.deadlyblogger.com/NewRelease/dirtydancing1987.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",DIRTY DANCING HAVANA http://www.deadlyblogger.com/NewRelease/dirtydancing2004.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",DIRTY http://www.deadlyblogger.com/NewRelease/dirty1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Distorted.2018.WEB-DL.x264-FGT http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/589.mkv #EXTINF:-1 group-title="FILM CAMPURAN",DOGMA http://www.deadlyblogger.com/NewRelease/dogma1999.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",JANGAN MENACE http://www.deadlyblogger.com/NewRelease/menace.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",JANGAN KATAKAN IBU THE BABYSITTERS MATI http://www.deadlyblogger.com/NewRelease/babysitter1991.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Dora dan Kota Emas yang Hilang (2019) https://www.googleapis.com/drive/v3/files/1EUDzPiS24mzTflfh0BFtBmkglxZBEYE4?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 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Nikkitaseth / ProjectAlphaPYTHON CODE WALKTHROUGH Data Sourcing In order to run a discounted cash flow model (DCF), I needed data, so I found a free API that provided us with everything I needed. I wrote a code that saved every financial statement of every company in a separate text file. In this code, I asked to ping the API’s URL for every ticker, open a text file for one of the financial statements for one company ticker, dump all the data found by the code into this file, and close it. This process was repeated for every company in our company list and every statement I have a code for. By doing so I Ire able to store the data for every company locally and did not need to ping the API every time I ran our code. Once all the financial data for each company was stored in form of a balance sheet, income statement, cash flow statement, and company profile text file, I needed to pick out specific items required for our DCF model. Thus, I defined the functions that selected all required items from the respective financial statements of each company and assigned them to a variable using utils.py. Discounted Cash Flow Model First of all, I needed to import the functions I defined in utils.py before defining the DCF model function, which would run for every company in our list. Next, I ensured to have 5 consecutive years of past data to compute the average. Thus, the first few lines of code checked whether the last year on record was 2019 from which point I would go back 5 years; if the last year was 2018, this would be taken as the first data entry from which I would go back 5 years. The second part mentioned above is important because companies file their 10-K, i.e. their annual report, at different times throughout the year so there may be companies that already filed their reports while others had not. After this step, five-year averages of every item’s percentage of revenue Ire calculated as Ill as the average revenue growth over the same period. These items included EBIT, depreciation & amortization, capital expenditures, and the change in net working capital. Once that was done, there Ire only three variables missing before calculating free cash flows for the next few years: a discount or hurdle rate; industry-specific perpetual growth rates; and a tax rate. After these three variables Ire set up, the next step was to calculate the free cash flows to the firm (fcff) for the next 5 years and determine the terminal value at the end of the period using the growth rate for the corresponding industry. For the former, I use a loop to calculate the fcff for all the year, discount it, and add it to one variable called fcffpv. Once the terminal value was calculated, these two additional numbers captured the enterprise value of the firm. Since I Ire interested in the equity value, I subtracted debt and add cash, which left us with the equity value. In one final step, I divided this value by the number of shares to end up with an intrinsic value per share. After calculating the intrinsic value per share, I compared it to the current share price with two additions. First, I added a buffer to minimize our downside risk for inaccuracy in calculations, which is called the margin of safety. Here, the intrinsic value should at least be 115% of the current share price. I also set an upper limit at 130% to ensure I would not include companies with extraordinarily high valuations, compared to their current price. If the share price calculated fell within this window, I added its ticker to a dataframe, which was the last step in the function. As such, the DCF function would run for every company and provide a dataframe with the tickers of all those companies that Ire undervalued at the time and fell within the 115% - 130% range. Portfolio Optimization The dataframe with the tickers of all the undervalued companies that was previously created has now become the portfolio, which I converted into a list and used as the source for further optimization that is about to come. Some general inputs for the rest of the code Ire the start and end date of the data I requested for optimization, as Ill as the risk-free rate and the number of simulations I wanted to run our optimizations for. Now that the general framework has been created, it is time to choose some conditioning variables to measure the performance of investment in one sector or across a combination of some/all sectors, respectively. Project Alpha uses the following conditioning variables to optimize its portfolios: • Sharpe Ratio: It measures the performance of an investment compared to the risk-free asset, i.e. the 10-year Treasury Bond, after adjusting for its risk factor or standard deviation. The Sharpe ratio would be given a higher Iight for investors who have a higher risk tolerance. In terms of code, I used the bt package to retrieve the data betIen the predetermined start and end date for the companies in our ticker list. This data was then used to find the portfolio with the highest Sharpe ratio. For that, random Iights Ire assigned to each company and the ratio was computed. After running the number of simulations previously determined, the Iights with the highest Sharpe ratio will be located using loc() and labeled ‘sharpe_portfolio’ which is a dataframe containing the excess return, the volatility, Sharpe ratio, as Ill as the Iights for every company. I also located the portfolio with the loIst volatility, put it in a dataframe called ‘min_volatility_port’ which has the same attributes. The rest of the code of this segment simply created a picture with all the portfolios generated, displaying the efficient frontier and highlighting the portfolio with the highest Sharpe ratio and loIst volatility. • Value at Risk (VaR): VaR was chosen as a diagnostic tool to assess the model. In our case, it basically indicated the percentage of time in which a loss greater than 1% would occur over a period of 5 years. Its limitation is that although it measures how bad the best of the bad is, it does not measure how bad it can get, meaning the worst of the worst. In regards to the code, I first requested the adjusted closing for the companies in our ticker list in the determined time horizon. I then retrieved the Iights from our Sharpe portfolio, set the number of days I wanted to simulate as Ill as the cutoff, before calculating the returns of every company in every period; here: daily. Thereafter, I created a new variable called ‘sigma’, which was be a copy of our return variable, in order to ensure the right format and type for our Monte Carlo loop. The simulation is pretty straight forward, as it measures how many runs the returns fall within 1% or outside of it. I then Iighed the resulting returns by the Iight of the company in the portfolio and whenever the portfolio return was outside the set boundary, it would count as a ‘bad simulation’. Once that is done, the number of bad simulations was divided by the total number of simulations to end up with a percentage of how many simulations were bad, which equals our VaR • Treynor Ratio: For the investors that already have a perfectly diversified portfolio and would like to add more assets to it, there would be a higher Iight on the Treynor ratio. It basically uses beta as a risk factor because it carries the risk relative to the market, instead of standard deviation as in Sharpe, meaning only systematic or non-diversifiable risk. For the code, I first calculated the portfolio’s beta. For that, I defined a function ‘beta’ that reads the beta of every company and returns it. The next step is to run a loop that would enter the beta of every company in our ticker list into a new dataframe. After setting the index equal to the tickers and transposing the Sharpe portfolio Iights, I can concat the two thus resulting in two columns: one is the beta of every company and the second is the corresponding Iight in the portfolio. I then created a third column as the product of columns one and two. The sum of all entries in that column is the portfolio beta, which was then used as the denominator for the ratio. The nominator was already calculated as ‘Excess Return’ in the Sharpe portfolio. • Sortino Ratio: The Sortino ratio measures only the downside risk (downside deviation or semi-deviation) by measuring returns against a minimum acceptable return, 𝜏. It is surprising to know that most of the industry ignores the total number of periods taken and just calculates the downside deviation by choosing the periods with downside risk, which results in misleading results. Project Alpha uses all the periods to calculate the same, so as to have an advantage over those robo-advisors/financial advisors that do not follow this process. The alpha in the future would be generated by going long on companies with high correct Sortino and low incorrect Sortino as they are undervalued, and shorting those with low correct Sortino and high incorrect Sortino as these are overvalued. The Sortino ratio would be given more Iight for investors who are more risk averse. This part of the code started with retrieving the data for our benchmark, the S&P 500, for the period and the calculating the average daily and annual return. After that, I calculate the portfolio returns, ‘returns[“Returns”]’, by adding the products of every company’s Iight times its return, which gave us the portfolio return for every period. From here, I calculated the downside risk by comparing the portfolio return in every period to the daily average return of our benchmark in a for loop. Before I did that, I defined a new variable called ‘semi’, which is a data series and will be filled with whatever comes out of the loop every single time. If the portfolio return minus the average daily return of the benchmark was greater than 0 – meaning the portfolio earned more than the average of the S&P500 – the value for the period was set to 0 and added to the semi data series. If it is 0, which is extremely unlikely, but whatever, it would also be 0. If it is less than 0, hoIver, which indicates underperformance, I would square the portfolio return, which already gives us the semi variance I need for our next step. From here, I can simply take the square root of the average of the ‘semi’ data series to get the daily downside risk and multiplying it by the square root of 252, which gives us the annual number. After that, I have all the numbers to calculate the Sortino ratio. • Information Ratio: The information ratio measures the portfolio returns compared to the returns of a benchmark index, i.e. S&P500, after adjusting for its additional risk. It only looks at the excess return of the portfolio over the benchmark and the volatility or risk associated with it. I already have all the inputs I need to calculate his ratio. Thus, I simply created a new dataframe with the portfolio returns of every period and the benchmark returns of every period. To find the excess return, i.e. the nominator, I simply subtracted the latter from the former and assigned it to a new variable, which I called ‘excess_return’. The nominator would be the average return of the portfolio minus the average return of the benchmark, and the denominator would be the standard deviation of the ‘excess_return’ series. Finally, I printed short sentences with the results for every conditioning variable just described as an output in the console.
zhelev / Python FsapiPython implementation of the Frontier Silicon API
SpaceFrontiers / MCPMCP Server for Space Frontiers API
FutureSpeakAI / Agent FridayAgent Friday is the world's first fully encrypted, fully local AI operating system, built on Asimov's cLaws (an evolution of OpenClaw). If your hardware cannot support fully local processing, Agent Friday users can also bring their own API keys for cloud access to frontier models.. 🌠🔭
zhelev / Python AfsapiAsynchronous python implementation of the Frontier Silicon API
flammy / Fsapi RemoteExample Code how to use the fsapi (Frontier Silicon API for PHP)
kimbtech / WiFi RadioAPIAPI on host wifiradiofrontier.com for Silicone Frontier radios.
DeaTh-G / Rangers ApiA very rudimentary modding api for Sonic Frontiers.
dirkclemens / Fsapi2mqttFrontier Silicon API to MQTT Gateway for Python3
