What is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

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What is the Best Machine Learning Algorithms for Imbalanced Datasets?

In machine learning, imbalanced datasets are those where one class heavily outnumbers the others. This can be due to the nature of the problem or simply because more data is available for one class than the others. Either way, imbalanced datasets can pose a challenge for machine learning algorithms. In this blog post, we’ll take a look at which machine learning algorithms are best suited for imbalanced datasets and why they tend to perform better than others.

 For example, in a binary classification problem, if there are 100 observations, and only 10 of them are positive (the rest are negatives), then we say that the dataset is imbalanced. The ratio of positive to negative cases is 1:10. 

What is the Best Machine Learning Algorithms for Imbalanced Datasets
What is the Best Machine Learning Algorithms for Imbalanced Datasets

There are a few reasons why some machine learning algorithms tend to perform better on imbalanced datasets than others. First, certain algorithms are designed to handle imbalanced datasets. Second, some algorithms are more robust to outliers, which can be more common in imbalanced datasets. And third, some algorithms are better able to learn from a limited amount of data, which can be an issue when one class is heavily outnumbered by the others.

Some of the best machine learning algorithms for imbalanced datasets include:

Support Vector Machines (SVMs),
Decision Trees,
Random Forests,
– Naive Bayes Classifiers,
k-Nearest Neighbors (kNN),

Of these, SVMs tend to be the most popular choice as they are specifically designed to handle imbalanced datasets. SVMs work by finding a hyperplane that maximizes the margin between the two classes. This helps to reduce overfitting and improve generalization. Decision trees and random forests are also popular choices as they are less sensitive to outliers than other algorithms such as linear regression. Naive Bayes classifiers are another good choice as they are able to learn from a limited amount of data. kNN is also a good choice as it is not sensitive to outliers and is able to learn from a limited amount of data. However, it can be computationally intensive for large datasets.

There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms tend to perform better on imbalanced datasets than unsupervised algorithms. In this blog post, we will discuss why this is so and look at some examples.

Supervised Algorithms
Supervised algorithms are those where the target variable is known. In other words, we have training data where the correct answers are already given. The algorithm then learns from this data and is able to generalize to new data. Some examples of supervised algorithms are regression and classification.

Unsupervised Algorithms
Unsupervised algorithms are those where the target variable is not known. With unsupervised algorithms, we only have input data, without any corresponding output labels. The algorithm has to learn from the data itself without any guidance. Some examples of unsupervised algorithms are clustering and dimensionality reduction.

Why Supervised Algorithms Perform Better on Imbalanced Datasets
The reason why supervised algorithms perform better on imbalanced datasets is because they can learn from the training data which cases are more important. With unsupervised algorithms, all data points are treated equally, regardless of whether they are in the minority or majority class.

For example, in a binary classification problem with an imbalanced dataset, let’s say that we want to predict whether a customer will default on their loan payment or not. We have a training dataset of 1000 customers, out of which only 100 (10%) have defaulted on their loan in the past.

If we use a supervised algorithm like logistic regression, the algorithm will learn from the training data that defaulting on a loan is rare (since only 10% of cases in the training data are Positive). This means that it will be more likely to predict correctly that a new customer will not default on their loan (since this is the majority class in the training data).
However, if we use an unsupervised algorithm like k-means clustering, all data points will be treated equally since there is no target variable to guide the algorithm. This means that it might incorrectly cluster together customers who have defaulted on their loans with those who haven’t since there is no guidance provided by a target variable.

Conclusion:
In conclusion, supervised machine learning algorithms tend to perform better on imbalanced datasets than unsupervised machine learning algorithms because they can learn from the training data which cases are more important. 

Some machine learning algorithms tend to perform better on highly imbalanced datasets because they are designed to deal with imbalance or because they can learn from both classes simultaneously. If you are working with a highly imbalanced dataset, then you should consider using one of these algorithms.

Thanks for reading!

How are machine learning techniques being used to address unstructured data challenges?

Machine learning techniques are being used to address unstructured data challenges in a number of ways:

  1. Natural language processing (NLP): NLP algorithms can be used to extract meaningful information from unstructured text data, such as emails, documents, and social media posts. NLP algorithms can be trained to classify text data, identify key terms and concepts, and extract structured data from unstructured text.
  2. Image recognition: Machine learning algorithms can be used to analyze and classify images, enabling the automatic identification and classification of objects, people, and other elements in images. This can be useful for tasks such as image tagging and search, as well as for applications such as security and surveillance.
  3. Audio and speech recognition: Machine learning algorithms can be used to analyze and classify audio data, enabling the automatic transcription and translation of spoken language. This can be useful for tasks such as speech-to-text transcription, as well as for applications such as call center automation and language translation.
  4. Video analysis: Machine learning algorithms can be used to analyze and classify video data, enabling the automatic detection and classification of objects, people, and other elements in video. This can be useful for tasks such as video tagging and search, as well as for applications such as security and surveillance.

Overall, machine learning techniques are being used in a wide range of applications to extract meaningful information from unstructured data, and to enable the automatic classification and analysis of data in a variety of formats.

How is AI and machine learning impacting application development today?

Artificial intelligence (AI) and machine learning are having a significant impact on application development today in a number of ways:

  1. Enabling new capabilities: AI and machine learning algorithms can be used to enable applications to perform tasks that would be difficult or impossible for humans to do. For example, AI-powered applications can be used to analyze and classify large amounts of data, or to automate complex decision-making processes.
  2. Improving performance: AI and machine learning algorithms can be used to optimize the performance of applications, making them faster, more efficient, and more accurate. For example, machine learning algorithms can be used to improve the accuracy of predictive models, or to optimize the performance of search algorithms.
  3. Streamlining development: AI and machine learning algorithms can be used to automate various aspects of application development, such as testing, debugging, and deployment. This can help to streamline the development process and reduce the time and resources needed to build and maintain applications.
  4. Enhancing user experiences: AI and machine learning algorithms can be used to enhance the user experience of applications, by providing personalized recommendations, recommendations, or by enabling applications to anticipate and respond to the needs and preferences of users.

Overall, AI and machine learning are having a significant impact on application development today, and they are likely to continue to shape the way applications are built and used in the future.

How will advancements in artificial intelligence and machine learning shape the future of work and society?

Advancements in artificial intelligence (AI) and machine learning are likely to shape the future of work and society in a number of ways. Some potential impacts include:

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  1. Automation: AI and machine learning algorithms can be used to automate tasks that are currently performed by humans, such as data entry, customer service, and manufacturing. This could lead to changes in the types of jobs that are available and the skills that are in demand, as well as to increased productivity and efficiency.
  2. Job displacement: While automation may create new job opportunities, it could also lead to job displacement, particularly for workers in industries that are more susceptible to automation. This could lead to social and economic challenges, including unemployment and income inequality.
  3. Increased efficiency: AI and machine learning algorithms can be used to optimize and streamline business processes, leading to increased efficiency and productivity. This could lead to economic growth and innovation, and could also help to reduce costs for businesses and consumers.
  4. Enhanced decision-making: AI and machine learning algorithms can be used to analyze large amounts of data and make more informed and accurate decisions. This could lead to improved outcomes in fields such as healthcare, finance, and education, and could also help to reduce bias and improve fairness.

Overall, the impact of AI and machine learning on the future of work and society is likely to be significant and complex, with both potential benefits and challenges. It will be important to consider and address these impacts as these technologies continue to advance and become more widely adopted.

  • [N] The 77 French legal codes are now available via Hugging Face's Datasets library with daily updates
    by /u/louisbrulenaudet (Machine Learning) on March 28, 2024 at 7:37 am

    This groundwork enables ecosystem players to consider deploying RAG solutions in real time without having to configure data retrieval systems. Link to Louis Brulé-Naudet's Hugging Face profile ```python import concurrent.futures import logging from datasets from tqdm import tqdm def dataset_loader( name:str, streaming:bool=True ) -> datasets.Dataset: """ Helper function to load a single dataset in parallel. Parameters ---------- name : str Name of the dataset to be loaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- dataset : datasets.Dataset Loaded dataset object. Raises ------ Exception If an error occurs during dataset loading. """ try: return datasets.load_dataset( name, split="train", streaming=streaming ) except Exception as exc: logging.error(f"Error loading dataset {name}: {exc}") return None def load_datasets( req:list, streaming:bool=True ) -> list: """ Downloads datasets specified in a list and creates a list of loaded datasets. Parameters ---------- req : list A list containing the names of datasets to be downloaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- datasets_list : list A list containing loaded datasets as per the requested names provided in 'req'. Raises ------ Exception If an error occurs during dataset loading or processing. Examples -------- >>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False) """ datasets_list = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_dataset = {executor.submit(dataset_loader, name): name for name in req} for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)): name = future_to_dataset[future] try: dataset = future.result() if dataset: datasets_list.append(dataset) except Exception as exc: logging.error(f"Error processing dataset {name}: {exc}") return datasets_list req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", "louisbrulenaudet/code-assurances", "louisbrulenaudet/code-aviation-civile", "louisbrulenaudet/code-cinema-image-animee", "louisbrulenaudet/code-civil", "louisbrulenaudet/code-commande-publique", "louisbrulenaudet/code-commerce", "louisbrulenaudet/code-communes", "louisbrulenaudet/code-communes-nouvelle-caledonie", "louisbrulenaudet/code-consommation", "louisbrulenaudet/code-construction-habitation", "louisbrulenaudet/code-defense", "louisbrulenaudet/code-deontologie-architectes", "louisbrulenaudet/code-disciplinaire-penal-marine-marchande", "louisbrulenaudet/code-domaine-etat", "louisbrulenaudet/code-domaine-etat-collectivites-mayotte", "louisbrulenaudet/code-domaine-public-fluvial-navigation-interieure", "louisbrulenaudet/code-douanes", "louisbrulenaudet/code-douanes-mayotte", "louisbrulenaudet/code-education", "louisbrulenaudet/code-electoral", "louisbrulenaudet/code-energie", "louisbrulenaudet/code-entree-sejour-etrangers-droit-asile", "louisbrulenaudet/code-environnement", "louisbrulenaudet/code-expropriation-utilite-publique", "louisbrulenaudet/code-famille-aide-sociale", "louisbrulenaudet/code-forestier-nouveau", "louisbrulenaudet/code-fonction-publique", "louisbrulenaudet/code-propriete-personnes-publiques", "louisbrulenaudet/code-collectivites-territoriales", "louisbrulenaudet/code-impots", "louisbrulenaudet/code-impots-annexe-i", "louisbrulenaudet/code-impots-annexe-ii", "louisbrulenaudet/code-impots-annexe-iii", "louisbrulenaudet/code-impots-annexe-iv", "louisbrulenaudet/code-impositions-biens-services", "louisbrulenaudet/code-instruments-monetaires-medailles", "louisbrulenaudet/code-juridictions-financieres", "louisbrulenaudet/code-justice-administrative", "louisbrulenaudet/code-justice-militaire-nouveau", "louisbrulenaudet/code-justice-penale-mineurs", "louisbrulenaudet/code-legion-honneur-medaille-militaire-ordre-national-merite", "louisbrulenaudet/livre-procedures-fiscales", "louisbrulenaudet/code-minier", "louisbrulenaudet/code-minier-nouveau", "louisbrulenaudet/code-monetaire-financier", "louisbrulenaudet/code-mutualite", "louisbrulenaudet/code-organisation-judiciaire", "louisbrulenaudet/code-patrimoine", "louisbrulenaudet/code-penal", "louisbrulenaudet/code-penitentiaire", "louisbrulenaudet/code-pensions-civiles-militaires-retraite", "louisbrulenaudet/code-pensions-retraite-marins-francais-commerce-peche-plaisance", "louisbrulenaudet/code-pensions-militaires-invalidite-victimes-guerre", "louisbrulenaudet/code-ports-maritimes", "louisbrulenaudet/code-postes-communications-electroniques", "louisbrulenaudet/code-procedure-civile", "louisbrulenaudet/code-procedure-penale", "louisbrulenaudet/code-procedures-civiles-execution", "louisbrulenaudet/code-propriete-intellectuelle", "louisbrulenaudet/code-recherche", "louisbrulenaudet/code-relations-public-administration", "louisbrulenaudet/code-route", "louisbrulenaudet/code-rural-ancien", "louisbrulenaudet/code-rural-peche-maritime", "louisbrulenaudet/code-sante-publique", "louisbrulenaudet/code-securite-interieure", "louisbrulenaudet/code-securite-sociale", "louisbrulenaudet/code-service-national", "louisbrulenaudet/code-sport", "louisbrulenaudet/code-tourisme", "louisbrulenaudet/code-transports", "louisbrulenaudet/code-travail", "louisbrulenaudet/code-travail-maritime", "louisbrulenaudet/code-urbanisme", "louisbrulenaudet/code-voirie-routiere" ] dataset = load_datasets( req=req, streaming=True ) ``` submitted by /u/louisbrulenaudet [link] [comments]

  • [D] Help finding an AI website
    by /u/vertigondriac (Machine Learning) on March 28, 2024 at 5:12 am

    There's a website posted here in r/ML where it's a website that compiles all of the best products suggested by each subreddit, for example, earphones, the AI website will list and rank the top models and brands of the best and reviewed products made by Redditors. I can't find the website for the life of me. submitted by /u/vertigondriac [link] [comments]

  • [D] What are some of the big tech company sponsored ML research websites that you are aware of for constantly keeping up with the ML research and workings behind their products, like Apple Machine Learning Research (https://machinelearning.apple.com/) or Tesla's AI day videos?
    by /u/pontiac_RN (Machine Learning) on March 28, 2024 at 5:08 am

    It would be great if there were a bundle of such sources or if you have a go to place where you keep up to date with all the new research going on. submitted by /u/pontiac_RN [link] [comments]

  • [R] Paper (NAACL 2024): why LLMs cannot be used for everyday fact checking, on the reversal problem, on the solution to the reversal problem, and a lot more
    by /u/payam_ka (Machine Learning) on March 28, 2024 at 2:55 am

    You can find the paper here: https://arxiv.org/abs/2403.18671 ​ Here is the list of things that you can find in the paper: - We reveal that large commercial language models cannot be used for every day fact checking tasks. - We argue that evaluating the fact checking pipeline across websites does not fully demonstrate model transferability, and instead, propose a straightforward way to repurpose existing datasets for the task. - We empirically show that when the fact checking pipeline is trained on out-of-domain genre of claims is not as competitive as being trained on in-domain genre of claims. - We propose a novel adversarial method for the claim retriever. - We report that language models (including the large models), are unable to infer the premise, given a hypothesis, even if they are trained on the premise to predict the correctness of the hypothesis (if it holds). - We use the finding above to propose a straightforward augmentation method to enhance the performance of claim reader in the fact checking pipeline. ​ Fun fact about our paper: our paper along another at the same time were submitted to ICLR 2024. Both, our paper and the other paper, which I mentioned, reported the reversal problem in LLMs. But our paper also proposed a solution to the problem. Furthermore! We did all these in only one section of our paper, and we offered a lot more than this in the other sections. But what was the outcome!?? Our paper was about to get rejected (we withdrew it to avoid it), and the other paper was easily got accepted :)))) #broken_system submitted by /u/payam_ka [link] [comments]

  • [D] Machine Learning On The Edge
    by /u/TheLastMate (Machine Learning) on March 28, 2024 at 2:29 am

    Hi guys, I found it today in my drawer. I forgot I had it and have never used it. Then it came to mind how is the current state of ML on the edge and are your predictions for the near future. We usually see big advances and news on big models but not much on applications on device. submitted by /u/TheLastMate [link] [comments]

  • [D] Are data structures and leetcode needed for Machine Learning Researcher/Engineer jobs and interviews?
    by /u/deadknxght (Machine Learning) on March 27, 2024 at 11:22 pm

    Title submitted by /u/deadknxght [link] [comments]

  • [P] deit3-jax: A codebase for training ViTs on TPUs
    by /u/affjljoo3581 (Machine Learning) on March 27, 2024 at 9:54 pm

    Hey all, I have written a codebase to train ViTs by following DeiT and DeiT-III recipes. As they are strong baselines to train vanilla ViTs, it is necessary to reproduce to adopt to the variant research. However, the original repository is implemented in PyTorch, it is impossible to run on TPUs. Therefore I re-implemented the simple ViT training codebase with DeiT and DeiT-III training recipes. Here is my repository: https://github.com/affjljoo3581/deit3-jax. I used Jax/Flax and webdataset to build a TPU-friendly training environment. Below are the reproduction results: DeiT Reproduction Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model T/16 in1k 224 300 2h 40m 73.1% 72.2% config log ckpt S/16 in1k 224 300 2h 43m 79.68% 79.8% config log ckpt B/16 in1k 224 300 4h 40m 81.46% 81.8% config log ckpt DeiT-III on ImageNet-1k Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model S/16 in1k 224 400 2h 38m 80.7% 80.4% config log ckpt S/16 in1k 224 800 5h 19m 81.44% 81.4% config log ckpt B/16 in1k 192 → 224 400 4h 42m 83.6% 83.5% pt / ft pt / ft pt / ft B/16 in1k 192 → 224 800 9h 28m 83.91% 83.8% pt / ft pt / ft pt / ft L/16 in1k 192 → 224 400 14h 10m 84.62% 84.5% pt / ft pt / ft pt / ft L/16 in1k 192 → 224 800 - - 84.9% pt / ft - - H/14 in1k 154 → 224 400 19h 10m 85.12% 85.1% pt / ft pt / ft pt / ft H/14 in1k 154 → 224 800 - - 85.2% pt / ft - - DeiT-III on ImageNet-21k Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model S/16 in21k 224 90 7h 30m 83.04% 82.6% pt / ft pt / ft pt / ft S/16 in21k 224 240 20h 6m 83.39% 83.1% pt / ft pt / ft pt / ft B/16 in21k 224 90 12h 12m 85.35% 85.2% pt / ft pt / ft pt / ft B/16 in21k 224 240 33h 9m 85.68% 85.7% pt / ft pt / ft pt / ft L/16 in21k 224 90 37h 13m 86.83% 86.8% pt / ft pt / ft pt / ft L/16 in21k 224 240 - - 87% pt / ft - - H/14 in21k 126 → 224 90 35h 51m 86.78% 87.2% pt / ft pt / ft pt / ft H/14 in21k 126 → 224 240 - - - pt / ft - - I trained all models on TPU v4-64 Pod slice, provided by the TRC program. I uploaded the checkpoints to the huggingface hub and you can also see the training logs on wandb. For more details, please check out my repository. submitted by /u/affjljoo3581 [link] [comments]

  • [D] Dataloading from external disk
    by /u/bkffadia (Machine Learning) on March 27, 2024 at 6:17 pm

    Hey there, I am training a deep lesrning model using a dataset of 400Go in an external SSD disk and I noticed that training is very slow, any tricks to make dataloading faster ? PS : I have to use the external disk submitted by /u/bkffadia [link] [comments]

  • [D] How do you measure performance of AI copilot/assistant?
    by /u/n2parko (Machine Learning) on March 27, 2024 at 5:38 pm

    Curious to hear from those that are building and deploying products with AI copilots. How are you tracking the interactions? And are you feeding the interaction back into the model for retraining? Put together a how-to to do this with an OS Copilot (Vercel AI SDK) and Segment and would love any feedback to improve the spec: https://segment.com/blog/instrumenting-user-insights-for-your-ai-copilot/ submitted by /u/n2parko [link] [comments]

  • [D] What is the state-of-the-art for 1D signal cleanup?
    by /u/XmintMusic (Machine Learning) on March 27, 2024 at 4:52 pm

    I have the following problem. Imagine I have a 'supervised' dataset of 1D curves with inputs and outputs, where the input is a modulated noisy signal and the output is the cleaned desired signal. Is there a consensus in the machine learning community on how to tackle this simple problem? Have you ever worked on anything similar? What algorithm did you end up using? Example: https://imgur.com/JYgkXEe submitted by /u/XmintMusic [link] [comments]

  • [D] State of the art TTS
    by /u/Zireaone (Machine Learning) on March 27, 2024 at 3:04 pm

    State of the art Tts question Hey! I'm currently working on a project and I'd like to implement speech using TTS, I tried many things and I can't seem to find something that fits my needs, I haven't worked on TTS for a while now so I was wondering if maybe they were newer technologies I could use. Here is what I'm looking for : I need to be be quite fast and without too many sound artifacts (I tried bark and while the possibility of manipulating emotion is quite remarkable the generated voice is full of artifacts and noise) It'd be a bonus if I could stream the audio and pipe it through other things, I'd like to apply an RVC Model on top of it (live) Another 'nice to have' is to have some controls over the emotions or tone of the voice. I tried these so far (either myself or through demos) : TORTOISETTS and EDGETTS seem to have a nice voice quality but are relatively monotone. Bark as I said is very good at emotions and controls but lots of artifacts in the voice, if I have time I'd try to apply postprocessing but idk to what extent it can help OpenAI models don't have much emotions IMO Same as eleven labs I used Uber duck in the past but it seems a lot of fun functionalities disappeared. If you have any advice, suggestion or if you think I should try somethings further feel free to reply! I also want to thanks everyone in advance! Have a nice day! submitted by /u/Zireaone [link] [comments]

  • [D] Data cleaning for classification model
    by /u/fardin__khan (Machine Learning) on March 27, 2024 at 2:42 pm

    Currently working on a classification model, which entails data cleaning. We've got 8000 images categorized into 3 classes. After removing duplicates and corrupted images, what else should we consider? submitted by /u/fardin__khan [link] [comments]

  • [D] Seeking guidance/advice
    by /u/qheeeee (Machine Learning) on March 27, 2024 at 2:14 pm

    Hi, I've finished Andrew Ng's course on Coursera. I think I've got the basics. I've started learning ML for my master's thesis. I want to develop a method to estimate scope 3 emissions. I studied business and I do not have any python background except for a 6-month data analytics bootcamp. I've got the data needed for my thesis, but when I try to work on it, I'm not sure what I'm doing, and ofc a sh*t ton of bugs and errors. Do I need to just keep trying to push through and learn through the experience by working on my thesis or do I need to study more? I've been considering to by a book <\Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow> by Aurelien Geron. Any guidance/recommendation would be much appreciated! submitted by /u/qheeeee [link] [comments]

  • [P] Insta Face Swap
    by /u/abdullahozmntr (Machine Learning) on March 27, 2024 at 2:03 pm

    ComfyUI node repo: https://github.com/abdozmantar/ComfyUI-InstaSwap Standalone repo: https://github.com/abdozmantar/Standalone-InstaSwap ​ ​ https://i.redd.it/9d4ti20fvvqc1.gif submitted by /u/abdullahozmntr [link] [comments]

  • [D] Seeking Advice
    by /u/MD24IB (Machine Learning) on March 27, 2024 at 1:45 pm

    I'm currently pursuing my undergraduate degree in robotics engineering and have been immersing myself in concepts related to machine learning, deep learning, and computer vision, both modern and traditional. With strong programming skills and a habit of regularly reading research papers, I'm eager to understand the job landscape in my field and pursue a Phd. Are there ample opportunities available? What can I expect in terms of salaries and future prospects? Additionally, I'm curious about the comparative job market between natural language processing (NLP) and computer vision. Given my background and interests, what areas or skills should I focus on learning to enhance my career prospects? Thanks in advance for your time and advice. submitted by /u/MD24IB [link] [comments]

  • [N] Introducing DBRX: A New Standard for Open LLM
    by /u/artificial_intelect (Machine Learning) on March 27, 2024 at 1:35 pm

    https://x.com/vitaliychiley/status/1772958872891752868?s=20 Shill disclaimer: I was the pretraining lead for the project DBRX deets: 16 Experts (12B params per single expert; top_k=4 routing) 36B active params (132B total params) trained for 12T tokens 32k sequence length training submitted by /u/artificial_intelect [link] [comments]

  • [D] Seeking Advice: Transitioning to Low-Level Implementations in AIoT Systems - Where to Start?
    by /u/MaTwickenham (Machine Learning) on March 27, 2024 at 1:20 pm

    Hello everyone, I'm a prospective graduate student who will be starting my studies in September this year, specializing in AIoT (Artificial Intelligence of Things) Systems. Recently, I've been reading papers from journals like INFOCOM and SIGCOMM, and I've noticed that they mostly focus on relatively low-level aspects of operating systems, including GPU/CPU scheduling, optimization of deep learning model inference, operator optimization, cross-platform migration, and deployment. I find it challenging to grasp the implementation details of these works at the code level. When I looked at the implementations of these works uploaded on GitHub, I found it relatively difficult to understand. My primary programming languages are Java and Python. During my undergraduate studies, I gained proficiency in implementing engineering projects and ideas using Python, especially in the fields of deep learning and machine learning. However, I lack experience and familiarity with C/C++ (many of the aforementioned works are based on C/C++). Therefore, I would like to ask for advice from senior professionals and friends on which areas of knowledge I should focus on. Do I need to learn CUDA programming, operating system programming, or other directions? Any recommended learning paths would be greatly appreciated. PS: Recently, I have started studying the MIT 6.S081 Operating System Engineering course. Thank you all sincerely for your advice. submitted by /u/MaTwickenham [link] [comments]

  • [P] Run AI & ML workflows locally from your Mac desktop
    by /u/creatorai (Machine Learning) on March 27, 2024 at 1:08 pm

    Hi all - I wanted to share an app I’ve been working on with a small team over the past year that I thought this community would be interested in. Odyssey is a completely native Mac app for creating remarkable art, getting work done, and automating repetitive tasks with the power of AI and machine learning models. We just made a major feature update and added the ability to create your own Widgets. Odyssey Widgets are fully interactive mini applications that live in their own windows or panels and are driven by a workflow. This means you can take a workflow you create with Odyssey and add it directly to your desktop. So, as an example, you could generate an image, chat with locally run chatbot, run bulk image processing, etc. straight from your desktop without even opening the Odyssey app. Widgets can be built with Odyssey and triggered from the Odyssey logo in your Mac’s menu. https://i.redd.it/8s9s6i0clvqc1.gif We're in public beta but here's a full list of everything Odyssey supports: Image generation and processing Run Stable Diffusion 1.5, SDXL, SDXL Lightning, and SDXL Turbo locally or connect your Stable Diffusion API key Add custom models & LoRAs ControlNet support including canny edges, pose detection, depth estimation, and QR Code Monster Inpainting and outpainting Super resolution models (Best Buddy GAN, Ultrasharp 4x, Remacri, and ESRGAN) Multiple image segmentation models Erase objects Dozens of image processing nodes including aspect ratio, resizing, and extracting dominant colors Custom image transitions for powerful slideshows Large language models and math equations Run Llama2 locally or connect your ChatGPT API key Supports both chatbot mode and instructions mode Solver node for word problems and math nodes for complex equations Lots of updates coming here in the next few weeks Automation and batch workflows Batch image and text nodes support hundreds of images and lines of text at once Remove backgrounds, upscale, change aspect ratios, and run dozens of image processors in bulk Private, customizable, and shareable No images, chats, or inputs are stored or accessible by the Odyssey team Completely private and secure. The only tracking is anonymized usage data to help us improve Odyssey Process your own data entirely locally No internet connection required to run local models Use your own API keys for ChatGPT and Stable Diffusion Easily save and share custom workflows What’s coming soon: Custom LLMs & more text processing nodes - we are adding support for bringing in custom LLMs, document uploads, and more Batch text and workflow automation - we are building in document upload, batch text support, and an integration with Apple shortcuts Plug-in support - we are opening up the Odyssey to 3P developers. If you’re interested, please reach out - would love to learn more from you as we work on building this out Feel free to reach out to [john@odysseyapp.io](mailto:john@odysseyapp.io) if you have any questions or feedback. submitted by /u/creatorai [link] [comments]

  • [P] Hybrid-Net: Real-time audio source separation, generate lyrics, chords, beat.
    by /u/CheekProfessional146 (Machine Learning) on March 27, 2024 at 12:11 pm

    Project: https://github.com/DoMusic/Hybrid-Net A transformer-based hybrid multimodal model, various transformer models address different problems in the field of music information retrieval, these models generate corresponding information dependencies that mutually influence each other. An AI-powered multimodal project focused on music, generate chords, beats, lyrics, melody, and tabs for any song. submitted by /u/CheekProfessional146 [link] [comments]

  • [P] Visualize RAG Data
    by /u/DocBrownMS (Machine Learning) on March 27, 2024 at 10:29 am

    Hey all, I've recently published a tutorial at Towards Data Science that explores a somewhat overlooked aspect of Retrieval-Augmented Generation (RAG) systems: the visualization of documents and questions in the embedding space: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 While much of the focus in RAG discussions tends to be on the algorithms and data processing, I believe that visualization can help to explore the data and to gain insights into problematic subgroups within the data. This might be interesting for some of you, although I'm aware that not everyone is keen on this kind of visualization. I believe it can add a unique dimension to understanding RAG systems. submitted by /u/DocBrownMS [link] [comments]

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