370 skills found · Page 11 of 13
DeathReaper0965 / Distributed DeeplearningEnd to End Distributed Deep Learning Engine, works both with Streaming and Batch Data built using Apache Flink
ramyamounir / Streamer TorchOfficial PyTorch implementation for NeurIPS'23 paper: STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
newking9088 / MITx 6.431x Probability The Science Of Uncertainty And DataA guide on how to use the wealth of available material This class provides you with a great wealth of material, perhaps more than you can fully digest. This “guide" offers some tips about how to use this material. Start with the overview of a unit, when available. This will help you get an overview of what is to happen next. Similarly, at the end of a unit, watch the unit summary to consolidate your understanding of the “big picture" and of the relation between different concepts. Watch the lecture videos. You may want to download the slides (clean or annotated) at the beginning of each lecture, especially if you cannot receive high-quality streaming video. Some of the lecture clips proceed at a moderate speed. Whenever you feel comfortable, you may want to speed up the video and run it faster, at 1.5x. Do the exercises! The exercises that follow most of the lecture clips are a most critical part of this class. Some of the exercises are simple adaptations of you may have just heard. Other exercises will require more thought. Do your best to solve them right after each clip — do not defer this for later – so that you can consolidate your understanding. After your attempt, whether successful or not, do look at the solutions, which you will be able to see as soon as you submit your own answers. Solved problems and additional materials. In most of the units, we are providing you with many problems that are solved by members of our staff. We provide both video clips and written solutions. Depending on your learning style, you may pick and choose which format to focus on. But in either case, it is important that you get exposed to a large number of problems. The textbook. If you have access to the textbook, you can find more precise statements of what was discussed in lecture, additional facts, as well as several examples. While the textbook is recommended, the materials provided by this course are self-contained. See the “Textbook information" tab in Unit 0 for more details. Problem sets. One can really master the subject only by solving problems – a large number of them. Some of the problems will be straightforward applications of what you have learned. A few of them will be more challenging. Do not despair if you cannot solve a problem – no one is expected to do everything perfectly. However, once the problem set solutions are released (which will happen on the due date of the problem set), make sure to go over the solutions to those problems that you could not solve correctly. Exams. The midterm exams are designed so that in an on-campus version, learners would be given two hours. The final exam is designed so that in an on-campus version, learners would be given three hours. You should not expect to spend much more than this amount of time on them. In this respect, those weeks that have exams (and no problem sets!) will not have higher demands on your time. The level of difficulty of exam questions will be somewhere between the lecture exercises and homework problems. Time management. The corresponding on-campus class is designed so that students with appropriate prerequisites spend about 12 hours each week on lectures, recitations, readings, and homework. You should expect a comparable effort, or more if you need to catch up on background material. In a typical week, there will be 2 hours of lecture clips, but it might take you 4-5 hours when you add the time spent on exercises. Plan to spend another 3-4 hours watching solved problems and additional materials, and on textbook readings. Finally, expect about 4 hours spent on the weekly problem sets. Additional practice problems. For those of you who wish to dive even deeper into the subject, you can find a good collection of problems at the end of each chapter of the print edition of the book, whose solutions are available online.
oktaydbk54 / Machine Learning Model Deployment Using FastAPI And StreamlitNo description available
mayup / Spark Streaming ExamplesSpark Streaming with Flume, Kafka, Kenesis, S[arkSQL, Socket, Custom Receiver, Handing Tweeter Data Read/Write, Machine Learning,
hmckay / BOTLBi-directional Online Transfer Learning (BOTL) framework and data generators for concept drifting data streams.
AdroitAnandAI / Object Detection On Mobile Cam For BlindMulti-Class Object Detection on Mobile Video Stream, using Deep Learning ConvNets, to assist the blind or to signal an incoming threat, without radars.
omarfoq / Streaming FlOfficial code for "Federated Learning for Data Streams" (AISTATS'23)
onurbil / Pde GcnOfficial implementation code of the paper: "GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations".
aamcbee / AdaOjaMethods from the paper "AdaOja: Adaptive Learning Rates for Streaming PCA."
anlanzy / L2ACThe algorithm in paper "Latency Aware Adaptive Video Streaming using Ensemble Deep Reinforcement Learning"
ChengjinLii / DDSurferDDSurfer: A Weakly-Supervised Dual-Stream Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI
Event-AHU / AGCN Event ClassificationPoint-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition. Jiang, Bo and Yuan, Chengguo and Wang, Xiao and Bao, Zhimin and Zhu, Lin and Tian, Yonghong and Tang, Jin (2023). arXiv:2306.05239.
Tanwar-12 / Traffic Sign Detection Traffic sign detection involves the use of deep learning yolov5 techniques to identify and locate traffic signs in images or video streams. This is a critical component of many intelligent transportation systems, including autonomous vehicles and traffic management systems.
Adhitya-02 / Temperature And Humidity Prediction With LSTM Models Using ESP32 Sensor DataExplore accurate climate forecasting using LSTM models with our ESP32-powered sensor system. Real-time data is streamed to VS Code via Python, enabling precise temperature and humidity predictions. Seamlessly integrate sensor technology and machine learning for advanced climate monitoring.
Nishantdd / People Counter YOLOv8This code uses the YOLO deep learning model to detect persons in a video stream, and tracks the persons from frame to frame using the SORT algorithm. It then counts the number of persons passing a specific line in the video and displays the count on the video.
PokemonGoers / PokeDataIn this project you will scrape as much data as you can get about the *actual* sightings of Pokemons. As it turns out, players all around the world started reporting sightings of Pokemons and are logging them into a central repository (i.e. a database). We want to get this data so we can train our machine learning models. You will of course need to come up with other data sources not only for sightings but also for other relevant details that can be used later on as features for our machine learning algorithm (see Project B). Additional features could be air temperature during the given timestamp of sighting, location close to water, buildings or parks. Consult with Pokemon Go expert if you have such around you and come up with as many features as possible that describe a place, time and name of a sighted Pokemon. Another feature that you will implement is a twitter listener: You will use the twitter streaming API (https://dev.twitter.com/streaming/public) to listen on a specific topic (for example, the #foundPokemon hashtag). When a new tweet with that hashtag is written, an event will be fired in your application checking the details of the tweet, e.g. location, user, time stamp. Additionally, you will try to parse formatted text from the tweets to construct a new “seen” record that consequently will be added to the database. Some of the attributes of the record will be the Pokemon's name, location and the time stamp. Additional data sources (here is one: https://pkmngowiki.com/wiki/Pok%C3%A9mon) will also need to be integrated to give us more information about Pokemons e.g. what they are, what’s their relationship, what they can transform into, which attacks they can perform etc.
kaiwaehner / Kafka Streams Machine Learning Docker MicroserviceA Kafka Streams Microservice (using Machine Learning) deployed as a Docker Container
efreneau / Machine Learning On Seismic Streamer DataApplying machine learning methods to model acoustical noise produced during seismic reflection surveys.
tylerlanigan / Vehicle Detection And TrackingDetecting vehicles in a video stream using machine learning. Adds on to lane detection project.
