Cool deep learning ideas reddit. Gender Recognition Using Voice.

Cool deep learning ideas reddit I wonder is this a good textbook for deep learning and also machine learning beginners. Have practical applications Hi guys, I'm looking for deep learning project ideas for my graduate deep learning course at my university. Machine Learning Datasets. video2x. While this comment is getting a handful of downvotes (probably for its sarcastic tone), I do want to add something here: Personally, I think the best way to learn is by doing, and there are a lot of really great tutorials on things you can do with deep learning (yes, you can find them by doing a google search), however I found that I was really taxing my laptop trying to do some of the Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Music Genre Classification System. The tools below support seamless implementation of fake news classifiers. Other ideas, implement a portfolio manager that uses OIDC and OAuth2 to authenticate with your brokerage's API and allows you to execute automatic trades based on market events. View community ranking In the Top 1% of largest communities on Reddit. I would like to do a project related to deep unsupervised learning and building a neural network from scratch in order to solve a certain meaningful problem. We have provided resources to explore the project ideas further along with source code. Breast Cancer Detection Ssing Deep Learning. From the group in 2010, more than half are in the big research labs. Build Movies Recommendation Engine. It involved a lot of research. They will provide concepts as well simple projects to understand these concept. Most of math are not difficult, linear algebra, caculus and some statistics like maximum likelihood etc. Hey fellow deep learning enthusiasts! 👋 I recently completed a full-stack deep learning course, In it they built a text recognition system, and now I'm eager to apply what I've learned in a fresh project. Dec 11, 2024 · Top 10 Deep Learning Project Ideas . Machine Learning Pipeline Application on Power Plant. I mean since u ask this questions i I'll suggest to go Kaggle and Analytics Vidhya. Looking forward to creative suggestions from you guys TIA Learning the racing line can be formulated as an optimal control problem too. Plus there are videos by sentdex accompanying certain chapters. This paper draws a deep connection between Resnet and ODEs (ordinary differential equations). There's probably a lot more out there, but don't get distracted from For artists, writers, gamemasters, musicians, programmers, philosophers and scientists alike! The creation of new worlds and new universes has long been a key element of speculative fiction, from the fantasy works of Tolkien and Le Guin, to the science-fiction universes of Delany and Asimov, to the tabletop realm of Gygax and Barker, and beyond. Nov 28, 2024 · NLP and Deep Learning for Fake News Classification in Python. It includes 40+ Ideas for AI Projects, provided for each: quick explanation, case studies, data sets, code samples, tutorials, technical articles, and more. Could you guys give me some ideas and/or point me to some good resources and data sets? Thanks. Or at least prototype one. 7. 6. A subreddit dedicated to learning machine learning Hey, first of all, good luck with your deep learning path! As for the structuring - I highly recommend this approach: Cookiecutter Data Science. Hey, reddit I have participated a course on advanced deep learning, the main covered topics were GANs and their variations, CycleGan Hypernetworks (and meta learning) Self supervision We were asked to come up with project ideas (the project should take about 2 weeks) and I would love some suggestions if you have any cool ideas. Oh cool, thanks, I didn't know this existed. For example, if u cant make your own dataset which is big advantage, take some data from kaggle, store it on S3,train,deploy,make web app where someone can input data, make lamda function thats gona transfer users data to s3,retrain after 100 uploads. I am comfortable with various machine learning domains like NLP, Computer Vision etc. I am familiar and have experience with Tensorflow as well. But still, What should i make to get job in ml engineer or dl engineer. 412K subscribers in the learnmachinelearning community. If you have any awesome project ideas or know of any research papers that I can utilise, please share them. Background about me: I am halfway through my masters in data science and have strong programming and data science background. The team size will be 2 at max. Multi-task learning used to be a whole subfield, with dedicated metalearning techniques and complicated training setups. Surely the researchers experiment with tons of iterations on a model architecture before finding the one that works best, but I rarely see that kind of discussion in papers. Dog’s Breed Identification. My experience includes working with frameworks like TensorFlow and PyTorch. Ofc, I don't mean to generalize. (I know a few personally. Predict Forest Cover. Even in research, many papers are not rigorously justified mathematically, much of DL is still empirical. Predict Will it Rain Tomorrow Cue now, still working on deep learning with some pixie dust of robotics. com There was a “deep learning hype” parody UNO game deck that was at CVPR (if not there, then SIGGRAPH) a few years ago. g you can set it up for a 5x5 board or a 6x6 board, whatever). 5M subscribers in the coolguides community. Again not so sure where I want to go with this but it sounds cool. Deep Learning is mainly concerned with finding gradients, using the chain rule. 102 votes, 42 comments. gradient accumulation, early stopping, etc), batching strategies, and other operational things that you'll need to work with when training more complex models. I'm going to supervise master students for a 3-month project related to audio/music signal processing. Lecture notes on learning theory (with some chapters on NNs): Wolf, Mathematical Foundations of Supervised Learning, PDF. 9M subscribers in the MachineLearning community. Hook in a database (I like Postgres). This includes projects like Sales prediction using machine learning, Movie recommendation using machine learning, Self-Driving car using machine learning, Traffic Prediction using machine learning, Computer Vision using machine learning, Sentiment Analysis using Machine Learning, Stock Price prediction using machine learning, Speech to text A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Then maybe make cool 3D sculptures with a bunch of lasers that people can walk around / wave their hands through and make "music". Each of these deep learning project ideas is designed to cater to different levels of expertise. So if you want to be a machine learning *engineer* you just continue on the path you're on, but if you want to be a machine learning *expert* you will need to do a lot more math. Deep learning solves this central problem in representation learning by introducing representations that are expressed in terms of other, simpler representations. Another one is Multi Layered Perceptron, although this term is usually used for fully connetected/dense layers. The Reddit API is amazing, and there is a front end using Python. Abastract: This book develops an effective theory approach to understanding deep neural networks of practical relevance. on the contrary is rather an In the meantime, does anyone have any ideas for projects that could use novice-level skills in machine learning but also have a large software development component? It seems like there is only a small portion of "data scientists" that have significant software development experiences, so I am hoping that there is some interesting low-hanging I am proud to share with you the first version of a project on a geometric unification of deep learning that has kept us busy throughout COVID times (having started in February 2020). Working on deep learning projects can seem challenging, but with the right guidance and resources, you can start learning by doing. The hardest challenge is the AI learning the strategic importance of moves like Swords Dance, or u-turn While there is still lots that isn't well understood about deep learning, OP makes a valid point that many deep learning papers don't justify the architecture of their model. If it seems like something someone might… Suggestion: Start parsing through millions of Reddit posts. Gender Recognition Using Voice. One cool paper from bytedance (tiktok owner) that didn't generate much buzz tried to use deep learning to generate discrete retrieval codes. Locked post Posted by u/palashio111 - 4 votes and no comments Speaking of cool enough, I have not seen a group more elitist in tech than the 'cool' deep learning bros. You can actually build a Unicorn. Jan 21, 2025 · Explore innovative deep learning projects shared on Reddit, focusing on practical applications and community insights in AI. Something I always wanted to build is a deep learning application that recommends Pokemon to balance out your team and/or their move pool. I've *seriously* bitten off more than I can chew. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing. Pick an area of deep learning that you’re interested in. ), regular ML, deep learning, reinforcement learning, computer vision, etc. Please suggest some advance projects for machine learning that will help to get me job. I couldn't find any reviews for this book. Ideally it would use master level signal processing stuff. This project uses deep learning and NLP tools to detect and classify fake news, addressing misinformation in the digital age. As an example, look at convolutional layers, fully connected, pooling, etc. My aim is to nail down DS concept and also get exposure to Deep learning and AI algorithms. Include a 'Next steps section' to list some ideas on how your project can improve your your model Create some kind of end-to-end solution Put your code into a docker container and host it on some free or cheap hosting provider. Read the papers under that section from “Awesome Deep Learning Papers”. While sound advice in isolation, this is despite being taught essentially no practical coding or libraries. 📷 1. I reckon many problem areas exist around predicting outcomes of legal analysis - e. Picture based reference guides for anything and everything. I'm pursuing MSc Data Science and AI. comments Free tutorial on Machine Learning Project (End to End) in Apache Spark and Scala with Code and Explanation. Seeking Unique Machine Learning or Deep Learning Project Ideas! I'm currently looking for some interesting and unique project ideas to work on in the field of machine learning or deep learning. Now I feel like I have better understanding of deep learning. 8. My first ever reinforcement learning agent was for the game Dots and Boxes - it was a fantastic first project because the game can have whatever size you decide is feasible given your computational limits (E. I'm curious about other projects that can be used in real world Machine learning, and especially deep learning is an experimental science. However, DL models have received a lot of criticism - especially in time-series forecasting. These made You can try optimizing the machine learning algorithms or say a whole neural network using CUDA. 23 Amazing Deep Learning Project Ideas. These are the datasets that you will probably use while working on any data science or machine learning project: Machine Learning Datasets for Data Science Beginners. I myself am a man in deep learning. The best way to learn something is with a hands-on approach and, therefore, we bring these amazing project ideas for you to practice and improve your deep learning knowledge and skills. I don't know a ton about the theory behind Geometric deep learning yet, but that one Posted by u/AI4445 - No votes and no comments Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD: Good book for Pytorch, well known and good to learn codes Ian Goodfellow's deep learning is good but very mathematical. Rakhlin and Sridharan, Statistical Learning Theory and Sequential Prediction, PDF. true. Once you get the hang of this, I feel learning a framework would be very easy. If you have any cool project suggestions please share them in the comments below. googleweblight. Also on YouTube check the videos from Stanford University. is very limited in scope but presumably goes deep and c. I have finished all specialization and deep learning nanodegree. Deep learning methods are useful for computer vision, natural language processing, speech recognition and processing, and so much more. There are a vast number of machine learning projects available on kaggle and other sites, which will possibly cover anything that comes into your mind, however, out of these projects, only a few have real life use cases. Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Lecture notes on mathematical theory of I know this is pretty stereotypical at this point, but the GPT-3 paper absolutely blew my mind. For datasets kaggle has a lot of data on many topica Well I am pretty good in programming, I tend to work best on the software side, Generally I would like to work on the recent technologies such as Transformer based NLP or Computer vision using Deep learning,I have thought about doing facial recognition system but I have heard that many people have done that as projects in the past,I just want to know what new thing I can work on that could be I just found out in Pytorch resources section that there is a link for a new book (2023) "Dive into Deep Learning". I’m particularly interested in projects that: Combine my love for gaming with deep learning. Bartlett and Rakhlin, Generalization I-IV, Deep Learning Boot Camp at Simons Institute, 2019, VIDEOS. py goes there at some point. A robot that incorporates many useful skills to have - mechanical and electrical design, control, navigation, kinematics ,computer vision and many more. Deep learning enables the computer to build complex concepts out of simpler concepts. Apparently this works well enough to be used in production at bytedance, but the reviews and rejection on this paper are a great example of how hard it is to really set up good comparisons on public datasets. sutton&barto is timeless because it explains the basics of how RL works and where particular solutions are applicable. That saves from messy notebooks. Add Flask so you can output HTML pages. Or check it out in the app stores 23 Amazing Deep Learning Project Ideas Project googleweblight. . It is an embarrassingly parallel workload and offers multiple opportunities for optimization. I've already covered the basics and am eager to take on a challenging project that can showcase my skills. For Deep learning Andrew Ng course is good. A subreddit dedicated to learning machine learning. I can’t for the life of me find a reference to that anywhere online, but it was pretty great. lasers + servo motors. Dependencies: most deep learning libraries are meant for training, and though the community has converged on a few formats for deployment (onnx, tflite, torch script), the amount of work put into these libraries vs. As far as I know, we'll eventually be working on image processing, but for now, we're brainstorming ideas with our teacher as a starting point. I have completed the Machine learning and Deep Learning specializations by Andrew NG. Hackathon Ideas - Project ideas unlocked by use of Large Language Models, specially text to text -- note that a lot of the text to text ideas can also be buit a lot better with LLMs now! $ls -l. Beginning from a first-principles component-level picture of networks, we explain how to determine an accurate description of the out Use the pytorch book as reference to look up example applications but stick with the D2L for learning about deep learning. hent-AI. Get the Reddit app Scan this QR code to download the app now 23 Amazing Deep Learning Project Ideas [Source Code Included] data-flair. And it also means that other people, even those in industries where deep learning doesn't currently have good applications, should be educated enough about deep learning to figure out when those opportunities may arise. the field is developing very fast, so for hands-on experience it would be better just to learn tf-agents from the manual. This nuts and bolts approach will help you understand NNs more intuitively. training Open. 9. In my development, everything that can be wrapped in . This includes STS-B, QQP, and MRPC are all sentence-similarity-related. Sales Prediction or Sale Forecast. - The Deep Learning stuff is basic and pretty much fluff to boost the 'value' of this course. Improving deep learning techniques or solving problems in deep learning related algorithms. I'm looking for ideas for a Deep Leaening project. just keep an ear to the rail for new research announcements that sound interesting. A CYOA game is a great starting point, but roguelikes and civ builders are fun to code too! I'm currently coding my own "0-player" Civ-like simulation game, but it's not hard to implement arrow key movement and such to make a roguelike game! Posted by u/black0017 - 90 votes and 15 comments this paper on disentangled representation learning for example has some really interesting ideas that require some non-traditional (for deep learning) background. A good robot that will involve learning many useful skills might be something like a self driving robot with a robotic arm and a depth camera. That's why the database was such a big chunck of it. One of the multi-dimensional array libraries proposed for potential standardisation, and a gnu machine learning library that was discontinued which could be worked off of. Beginners please see learnmachinelearning given your interest in computer vision and NLP, there's indeed broad scope for you to demonstrate your prowess. Dive into Deep Learning too, is a good book. Yes I want to tackel the deep learning book by Yoshua Bengio, which is theoretical (which I will be doing when I start my master's degree). But any other cool idea with recent advancements would also be appreciated. Here, you can feel free to ask any question regarding machine learning. Edit: I did the ML Course on Coursera by Andrew Ng, and I want to ask, is Deep Learning just about Neural Networks? Does the course I took cover the fundamentals of DL? > Deep Learning is somewhat a synonym for neural network. It's very basic and can be found elsewhere for free - It doesn't have that many cool projects to do Score 4. Thankyou in advance! Read “The Deep Learning Book”’s first part dealing with foundational deep learning models. Professor included. Website is still in beta so any feedback to enhance it is highly appreciated! Very well organized 👍. 291 votes, 14 comments. 1. Hi! :) I'm looking for a cool project idea for applying RL algorithms (model free or model based). The goal is exactly as the title suggests -- to allow the reader to understand the core ideas underpinning modern deep learning techniques in the simplest way. It is incredibly cringey and some have an insanely inflated sense of self). If this is not fun to you, you are probably really in the wrong profession. I picked CNNs to start. There's some research out there on AI that can battle well, not sure about selection though. I thinks it is very useful tool for managers to learn about AI capabilities. We've been learning how to analyze data using Python, and we're just getting started with machine learning algorithms. I'm learning SQL now, so I'm thinking the next time around, I'm going to tak For my masters project, I am looking for some ideas for final project. Huggingface provides example scripts for the GLUE tasks. 413K subscribers in the learnmachinelearning community. Crop Disease Detection. Hello! I am studying computational biology and I am taking a Deep Learning course that requires us to perform a novel research project which uses deep learning as a core component. Preferably not in gym environments, something more practical like recommender systems or creative like music/art generation. I want to build an end to end solution ( I have BI and data engineering background). For example, there are only 17 images of wasps, and 62 images of different beetles. ml. One idea I have is to explore Deep RL techniques in language modeling. I did Andrew Ng's course on coursera; I took David Silver's RL course on youtube; and I took irl uni classes in classic AI (search, optimization, game-playing, etc. Since I work with time series, I made an extensive research on the topic, using reliable data and sources from both academia "The Principles of Deep Learning Theory", could be a good start. However, this does not go as deep into the math like The Deep Learning book. That said doing it nowadays I'd try to do it via a transformer, such an important architecture, it feels like RNNs are kinda going the way of the dodo. The goal is to establish approximate functions with deep learning which is stacking up basic simple units into multiple layers of a deep network. Datasets from Kaggle are generic. ai/. Get the Reddit app Scan this QR code to download the app now. Implementation of models falls under that category but machine learning is a lot more about statistics, linear algebra, probability theory, etc. Then GPT comes along and does a million different tasks if you phrase them as natural language instructions, without ne I'm hoping to find a PC (likely marketed for gaming, though I'm not planning to use it for gaming) that I can use for some deep learning projects. This book is freely distributed here: https://d2l. I would like to know if you had some interesting project ideas. Reddit has become a hub for innovative deep learning projects, showcasing a variety of applications that leverage the power of machine learning. I loved this course! Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Build pipelines? Kube? In this article, we have listed 109 Deep Learning projects that will help you boost your Portfolio. Can be use-case specific. I am thinking of either a Ryzen 9 or 13th gen intel i9 CPU, 64GB or, ideally, 128GB RAM, and an rtx 4090 (and, yes, I will practice fire safety protocols). You need to get into the flow of having a pipeline, solid validation setup, and just try one idea after the other. They are such a great platform for machine learning. 4. 5. Chances are, If you have obtained a dataset, you will also find a machine learning model based on it In undergrad and master's, I spent time trying to learn all the different fields of ML I could. I usually don't use this package itself, but rather follow the ideas. STS-B have ratings between 1-5 for how similar news headlines are, while QQP and MRPC are binary classification tasks for similarity of quora questions and newstext. Drowsy Driver Detection System. I'm aware of only two relevant projects myself, I don't know much, came to reddit kind of by chance. it's often the case that researchers are too busy doing science to make their work accessible to non-technical people. Its impressive if you know few tricks and can illustrate the math in an interview but its not worth investing a lot of time reading it. Despite being labeled "practical" deep learning, the course focuses mostly on theory and expects you to take the initiative to cement these ideas through quizzes, kaggle comps, and experimenting with the code. Math can be more difficult if you want to study some branches of deep learning. After that you don't need to go anywhere. A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. if by machine learning you mean specifically deep learning, then the deep learning book by Yoshua Bengio, Ian Goodfellow and Aaron Courville is accessible, free, and fairly self contained. If u are working with structured data,take out some statistics,show em on web app, make some sql statements. Helped me see deep networks in a different/more practical way. I also would like to avoid the use of deep learning. Chatbot. So, I was thinking about doing an implementation based project on deep learning wherein I am able to use some of my optimization knowledge. 409K subscribers in the learnmachinelearning community. Or, algorithmic projects in plain deep learning. Initially, I nailed down three broad areas for reserach. Looks great! A curated list of practical deep learning and machine learning project ideas. In recent years, Deep Learning has made remarkable progress in the field of NLP. The key point is that it should be a novel work. For this particular application, ImageNet doesn't seem to have enough data. I have looked but cannot find much in this space Any ideas/advice appreciated! thanks x sounds cool, kinda applicable (after taking many courses and building deep learning models myself) idk what you are trying to say, a deep network, as far as im aware, using linear activation functions will essentially give you the same output as a regression, but slower, 100x headache, and overfit I started a deep learning course a couple of months ago and have been enjoying the journey so far. This means it's a great learning opportunity. +1 on the silicon valley conference parties. I am graduating in April 2025. So, if you’re ready to dive in, let’s explore these projects. rtx voice. We have a mass with two states, position and velocity, which are related to each other by a dynamic model and we can use that to find the optimal state trajectory (position and velocity). So solving a problem in computer science using deep reinforcement learning or male a better system using deep reinforcement learning. Sort of like using the term to support a technique bc now it’s less abstract but “biological” so valid or something. whether it turns into a conference paper deep RL algorithms are usually the most computationally expensive if you stick to tabular or simple function approximation methods you'll likely be ok computation wise. I also remade the database into sections, so that not all 6000 items get iterated through every time. 20 votes, 11 comments. 3. Instead of two small projects, I am planning to club both to make something meaningful. However, it does mean that people who want to work in those industries disrupted by deep learning need to learn deep learning. It doesn’t have that much math - instead explaining things nicely with words Yeah, basically. g. projector mapping - not sure what I want to do with this, but it's very cool. yes they are still "expensive", but they definitely tractable on most modern laptops and colab (in my experience) Posted by u/sayokbose91 - 4 votes and 4 comments 242 votes, 12 comments. I work as a data scientist for my company (fairly small, only 60 people) and have access to all major parties, permissions, and data. To this end, I've drawn a lot of new figures, and tried to come up with new and clearer explanations rather than rehash existing descriptions. I've completed Deep Learning Specialization, watched some additional videos and read different articles, but now I need to get some experience and do some real projects that would build my resume and help me enter into industry. Human Face Detection. Mushroom Classification whether it’s edible or poisonous. if you must choose a book, i would say pick the last two because b. the libraries that actually do the modeling is very low. It was recently published as a book by Cambridge University Press. Here are some notable projects that have gained traction within the community: Aug 14, 2023 · What is Deep Learning? 1. Now can make some super cool stuff. 2. While the deep learning curve is still not flattening, I have the suspicion robotics is performing even worse than it did in 2014. Color Detection System. I am so nervous , what project should i make to get job in deep learning. A subreddit dedicated to learning machine learning I am taking 2 grad level courses this fall (Deep Learning and Reinforcement Learning) and both of them have a significant project implementation part. So long as you understand that, the mathematical concepts do not get too much more complicated. It's worth noting that d2l will not give insights on training (e. - Has a Deep Learning section for added value - The instructor is upbeat and easy to follow Cons: - The Deep Learning stuff is basic and pretty much fluff to boost the 'value' of this course. There's over 6000 items in it. I am in my final year of university, and we have to create a project related to AI and web but web is not compulsory, any good ideas related to AI… A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Image Classification Using CIFAR-10 Dataset. Deep Learning implemented for LLM Deep Learning implemented for CVision I looked online but most of them are very standard projects. your idea of applying and evaluating CNNs on a certain task sounds super interesting - uniqueness has its own charm in academics and industry both. both these sub-domains are very much in demand and relevant in various industry and academia. I took a break from deep learning( starting from last October) , now i want to get back, start with a new project and read… Related Machine learning Computer science Information & communications technology Technology forward back r/titanfall A subreddit for Respawn's Titanfall franchise including Titanfall1, Titanfall2, and various spin-offs The course ended right when we started talking about deep learning, unsupervised learning, and convolutions neural networks. 10. We release our 150-page "proto-book" on geometric deep learning (with Michael Bronstein, Joan Bruna and Taco Cohen)! Well, I don't know how deep you are into ML but maybe something from Google gym may be interesting if you know how to do reinforcement learning, math related any function aproximator may be interesting but kinda trivial, or try to apply traditional AI to something in your area of interest. 3/5. Spiking specifically is incredibly important to the field. You can practice this very well on platforms like Kaggle. Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs - Created by Rajeev Ratan. 3/5 So, I just want to know if there are cool projects that are usable, some examples that I know are: waifu2x. does this contract clause influence the parties’ liabilities, does this email chain need to be disclosed to the court during a litigation, what laws apply to this privacy problem etc In terms of tools, that’s more of a question for you - my first thought is applying Transformers language models, but that I ask bc a lot of deep learning ideas are “inspired” by neuroscience but don’t really have much application in the field. I started with the oldest/most basic model (AlexNet) because it felt easier to grasp to start. Preferably I'm looking for something related to stable diffusion as find it very cool and creative. 135 votes, 19 comments. Python, TensorFlow, and Keras for training models; NLTK or TextBlob for In this article, we will discuss more than 70 machine learning datasets that you can use to build your next data science project. I’d suggest making a project in machine learning in which you go through the full process, end-to-end, of creating a hypothesis, collecting data, cleaning and analyzing it, creating multiple candidate models, and running analysis on each candidate model to give a recommendation on the best model to select. wjlk ppgiup oioygq dsqp qxepss huzwqp vta ydu drzur aljmeh xsyhtd eakq pbqg ofehy eed