Has anyone here used fine tuned Donut model in production? submitted by /u/shiva525 [link] [comments]

Link to github TSMixer is an unofficial PyTorch-based implementation of the TSMixer architecture as described TSMixer Paper. It leverages mixer layers for processing time series data, offering a robust approach for both standard and extended forecasting tasks. submitted by /u/Yossarian_1234 [link] [comments]

suppose we played 2 chess ai together and rewarded a win, and lets say we don't give a value to any piece ( although I suppose you can value the king as 1 million or something), so we basically let the ai determine its own value of each piece. than how much would AI value each piece? now obvously i know we would have to do that first, but it doesnt sound to hard or, would something like this be unplausible maybe it would take alot of time like decades or something submitted by /u/Michele_Awada [link] [comments]

submitted by /u/tusharkumar91 [link] [comments]

I am a systems engineering student and one of my projects on my resume is a neural network built without libraries (using linear algebra). I did this because I wanted to understand what was going on rather than just following a tutorial and plugging some pytorch functions together. Now with xmas break coming up, I want to explore AI/ML and I am particularly attracted to how that intersects with banking. Just finished an OA for goldman sachs here in Toronto and it has only solidified my yearn to work in finance. With that being said what resources should I read/use or potential project should I build that can scale in complexity and would both help me learn a ton and be attractive to banks on a resume. Something that I can make as simple or as complicated as I like and would teach me a ton. Most importanly, what resource/method should I learn via. Thanks a ton. Cheers. submitted by /u/Traditional_Sea6160 [link] [comments]

I was able to get my hands on a RTX 3080 12GB for $300. (This is not for gaming, strictly for ML.) I have a Dell Vostro 7620 laptop (i7 12700H, 40GB DDR5, RTX 3050 4GB dGPU) with an open TB4 port. I am purchasing a TH3P4G3 for ~$110 and a XPG Core Reactor 750 Gold for ~$95. Is there anything I'm missing or is there any way I can more efficiently spend my money (performance per $ spent)? submitted by /u/SMtheEIT [link] [comments]

Hi, I'm trying to make some videos myself and came across this and thought that voice changer isn't bad. Doesn't sound robotic and the machine learning used to produce that voice is better than Microsoft SAM. https://www.youtube.com/watch?v=tm9aVnl1_Tg But I can't find the source of the software anywhere. Anyone recognize this or have other open source voice changer recommendations? submitted by /u/SherbetTiger [link] [comments]

So basically, title. I haven't been able to run those locally. I have already trying cloning the repos and running all the commands but a lot of errors would pop up always. Could anyone post a short tutorial to install both things locally (WITH A GRADIO UI, FU*K RUNNING STUFF ON VS CODE). TLDR: I'm not smart enough to install those locally so it would be nice if someone posted a tutorial to install those link to styletts2 demo with gradio: https://huggingface.co/spaces/styletts2/styletts2 link to xtts 2 demo with gradio: https://huggingface.co/spaces/coqui/xtts submitted by /u/sadeliox [link] [comments]

I am developing a stochastic variational GP using gpytorch. I am performing CV over data collected from n people. When I investigate model fit the trained and predicted GP’s predictions are centered around around the mean of the data. Therefore more extreme values are not being fit. What, model architecture wise could be causing this, and what could possibly help? I have experimented with length scale adjustment with little success… I have built an optuna study to optimise the hyperparameters below but with with no success. • ⁠kernel • ⁠likelihood • ⁠variational strategy • ⁠variational distribution • ⁠learning rate • ⁠mean —> (Constant or Zero) • ⁠num inducing points submitted by /u/ConfusedLayer1 [link] [comments]

I hope my questions don't sound too stupid, but I'm new to data science and currently trying to delve deeper into fraud analytics and I'm very very confused. I'm doing this project where I was given a raw dataset of online transactions and told to define normal and anomalous behavior in my own terms. As for what I read, common evaluation metrics used for fraud detection include precision, recall, F1-score, ROC curve, or confusion matrix. But I can't get the true/false positive-negative without the historical/labeled data then? I googled "unsupervised evalution metric" and it shows Silhoutte Score, which I already use to determine the contamination level in my Isolation Forest model, but isn't this basically just model tuning/choosing the optimal parameter for anomaly threshold? Or is it okay to not evaluate a model? Or am I supposed to compare it with other unsupervised models and select the best one as a replacement for model evaluation like confusion matrix? Also, is there anything I need to do/explain with the PCA loading? I already got the optimal number of components/important features from the explained variance ratio and it directly marks the records as either fraud (1) or normal (0). It results in Normal (1113 records) and Fraud (10 records), is this common for fraud detection or is it a case of class imbalance that needs to be addressed/ resampled/ changed? submitted by /u/DecentPerson011 [link] [comments]

To provide context, I am currently in the final year of my engineering studies and am required to undertake a 4-6 month internship as my graduation project. My interest lies in the applications of AI, specifically in robotics, computer vision, and other engineering fields. Fortunately, I have secured a position in a prestigious institute. However, the proposed internship topic focuses on AI in Intrusion Detection systems, which does not align well with my interests. Upon researching the field, I've discovered that it is quite mature, and the internship would involve extensive research. Now, I find myself at a crossroads, debating whether to accept the offer and leverage the institute's network and resources (along with excellent living conditions) or to explore other opportunities. Unfortunately, securing a position in another prestigious institute seems unlikely, and my alternatives might involve local companies (which may be outdated) or universities abroad. It's worth mentioning that I am from North Africa. Any advice or insights on this matter would be greatly appreciated. submitted by /u/Dry_Curve_8260 [link] [comments]

Hi, I'm hoping someone can tell me what this problem is called so I can search for existing literature. I have a function f = f(theta,V) which is a physical model of a class of system (a transistor). Let's say f is scalar, theta is a ~100 element vector and V is a ~3 dimensional vector (x,y,z). I want to fit f to measurements of a transistor: find parameters theta such that f is close to the measured behavior in a certain V domain (for example a regular grid in [0,xmax] x [0,ymax] x [0, zmax]).* The standard solution is to use an optimizer like Newton-Raphson to find theta that minimizes the RMS error between f and the measurements of the system. This has problems like e.g. getting stuck in local minima because f is nonlinear. I would like to train a NN to take as input the measurements and produce theta as output. If the measurements were always taken at the same values of V, this would be relatively simple: I could generate my training data by randomly sampling thetas, giving [ f(theta,V_1), ..., f(theta, V_R) ] as one input point and theta as expected output. However, the domain where I want to fit the function will not always be the same: today I might want to fit f to measurements of transistor where V1 ranges from 0 to 5, tomorrow I'm fitting a different transistor where V1 ranges from 0 to 100. In principle, I could make the input [ V_1, f(theta,V_1), ..., V_N, f(theta,V_N) ]. But this feels like a very complex mapping that the NN would have to learn (correct me if I'm wrong). It'd have to find the relations between the points, and reorder them to extract something meaningful from them. Another possibility is to always measure the same Vs, just linearly scaled to the domain of interest, and to tell the NN what this scaling is. The input would be [ xmax, ymax, zmax, f(theta,scaledV_1), ..., f(theta, scaledV_R) ]. In this case, the NN doesn't have to reorder the measurements at runtime: they always have the same relation to each other, just with a different scale. The last possibility I've thought of is to just get this to work for a fixed domain, and then do fine-tuning when a new domain is needed. It's OK if it takes an hour for each new execution, that's still an improvement over existing solutions. Sorry for the long post, would be very interested to know what search terms I can use or any relevant literature you can recommend. Thanks! *If this all sounds too abstract, imagine I have samples of the function a*x2 +b*x+c and I want the network to output a,b,c based on the samples, but I'm not always sampling the function at the same values of x. submitted by /u/ignamv [link] [comments]

submitted by /u/moschles [link] [comments]

submitted by /u/ashvar [link] [comments]

I have developed Gymbo, a proof of concept for a Gradient-based Symbolic Execution Engine implemented from scratch. Symbolic is a method used to analyze a program and identify the inputs that trigger the execution of each program section. It works well when debugging the behavior of neural networks. For example, Gymbo allows you to find adversarial examples easily. It is interesting to see that the logic-based method effectively analyzes neural networks. Gymbo also offers easy-to-use Python API compatible with sklearn and PyTorch. I am looking forward to your feedback! submitted by /u/Living_Impression_37 [link] [comments]

I'm making an implementation of the softmax regression and I'm struggling to understand the nature behind the problem of increasing value of Cross-Entropy [1], along with an increasing accuracy (on the "iris" dataset): https://preview.redd.it/eo3654zwbw3c1.png?width=688&format=png&auto=webp&s=52916d56029e6e8aa1e9594bbcb02c7075009206 This is utmost confusing to me, because there's no class imbalance: https://preview.redd.it/rqs7i6jybw3c1.png?width=389&format=png&auto=webp&s=6ca668f1cc70aae7a125a6b9c48c511eadc6d562 I am not entirely sure whether the sample size of N = 112 is at fault. I will appreciate any help on the matter. Thank you in advance. ​ [1] submitted by /u/joshjson [link] [comments]

Hello people! Sharing the latest video from my ML YT channel discussing Transformers, how they work, and its interesting relationship with Inductive Bias when compared to other neural networks (like CNNs, RNNs, etc). Sharing a link her for those interested to check it out! Thanks. submitted by /u/AvvYaa [link] [comments]

The bitter lesson argues learning and search are the winning strategies since they scale with computing power, and will generally outperform techniques that rely on encoding human knowledge. What do you think of ToT and similar techniques given this? Are they good examples of expanding the power of models via search, or are they examples of attempting to force what we believe to be humanlike behavior? ​ For reference, I've seen two main tree-based approaches used in the research. One is MCTS based decoding. This seems more in line with traditional notions of search, in that you are searching through the outcome space of possible text and then selecting the best ones. Paper by meta using the PPO value function as the MCTS node evaluator Using MCTS to improve ability to write a successful program ​ However, there is also the more abstract CoT/ToT style trees being used, which are relying on the model to generate a tree of followup sequences for a given sequence. Tree of Thoughts: Deliberate Problem Solving with Large Language Models A core difference here is that the tree does not represent a search through possible outputs in the same way the MCTS decoding tree searches do. It's a search through possible reasoning chains, which ultimately are inputs to some evaluation method (often the model itself), to determine the optimal output. So you aren't just searching through the outcome space, but also through the "evidence space", both of which will be passed to the evaluator to select the appropriate outcome. edit: Unrelated, but you could in principle combine both of these search techniques, which would be cool but probably really expensive. For example, use MCTS decoding for each node in the ToT. I haven't seen anybody do this but if anybody has a link to a paper that would be cool. submitted by /u/30299578815310 [link] [comments]

Hey guys! I wanted to share this tool I recently made, https://visual-backend.com which lets you build a FastAPI backend for your ML model really quickly. It's essentially a GUI that lets you generate code & scaffolding for endpoint handlers, auth and even deploy to GCP in just one click. So to serve your ML model, all you need to do is load it and call the inference functions at each endpoint handler. Of course, for things like batch processing or job queues, there isn't functionality for that, but just curious to hear whether at the base level, such a tool might be useful for ya'll! I'd initially built this for full stack devs, but after talking to several ML engs/ data scientists, I realised that it might be helpful to some of ya'll who are looking to quickly bring your ML models to production without caring too much about infrastructure / software eng, so I'd love to hear whether you find this helpful and other thoughts u might have. Looking forward to it 🙂 submitted by /u/johnyeocx [link] [comments]

Since papers.labml.ai is offline, how do you guys track the recent trending papers or hot topics, especially on X. Any recommendation? submitted by /u/Historical-Tree9132 [link] [comments]

We have a reading club every Friday called Arxiv Dives where we go over the fundamentals of a lot of the state of the art techniques used in Machine Learning today. Last week we dove into the "Vision Transformers" Paper from 2021 where the Google Brain team benchmarked training large scale transformers against ResNets. Though it is not groundbreaking research as of this week, I think with the pace of AI it is important to dive deep into past work and what others have tried! It's nice to take a step back and review the fundamentals as well as keeping up with the latest and greatest. Posted the notes and recap here if anyone finds it helpful: https://blog.oxen.ai/arxiv-dives-vision-transformers-vit/ Also would love to have anyone join us live on Fridays! We've got a pretty consistent and fun group of 300+ engineers and researchers showing up. submitted by /u/FallMindless3563 [link] [comments]

Paper: https://arxiv.org/abs/2311.05241 Abstract: Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive datasets. Expanding upon this accomplishment, there has been a surge in research exploring the application of neural networks in other learning scenarios. One notable framework that has garnered significant attention is meta-learning. Often described as "learning to learn," meta-learning is a data-driven approach to optimize the learning algorithm. Other branches of interest are continual learning and online learning, both of which involve incrementally updating a model with streaming data. While these frameworks were initially developed independently, recent works have started investigating their combinations, proposing novel problem settings and learning algorithms. However, due to the elevated complexity and lack of unified terminology, discerning differences between the learning frameworks can be challenging even for experienced researchers. To facilitate a clear understanding, this paper provides a comprehensive survey that organizes various problem settings using consistent terminology and formal descriptions. By offering an overview of these learning paradigms, our work aims to foster further advancements in this promising area of research. https://preview.redd.it/pp2j7tz2dr3c1.png?width=1249&format=png&auto=webp&s=983e081c4b4feabddb3457ba74d94202495be4a5 submitted by /u/APaperADay [link] [comments]

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. And experienced data

For the two people in this subreddit that haven't read the bitter lesson yet, http://www.incompleteideas.net/IncIdeas/BitterLesson.html However, as someone who is interested in robot perception, planning, and learning, does this necessarily apply? I'm not too sure, especially in the context of robots in human (unstructured) environments whose policies cover much wider in scope than say factory or warehouse robots. Robots that have to deal with the stochastic and wildly differing nature of the real world, whose policies are robust to change in its environment. I can think of a few ideas where the bitter lesson may or may not be applicable. Hardware limitations. Although offloading compute to remote servers is definitely an option, how much can robots rely on this when interacting with the environment in real time? No feasible robot would be able to store billions of parameters even just for inference. Moore's law has been slowing down for some time now. Data. This is a big one in my opinion. What even constitutes good training data for training robot policies? Do we have enough of it? Of course we have good models and enough data for CV and language, and even Toyota's paper on using diffusion models for grasp poses looks promising, but a robot policy must put all of this together to accomplish multi-modal tasks. There isn't a huge corpus for multimodal task planning, such as how to break a task such as pouring a cup of water, into an HTN with multiple sub-tasks (grasp cup, pick, pour, etc.) The reason why I make this post is because I am unsure. I could see how an LLM could be the basis for a generalized robot policy that can accomplish multiple tasks, or more efficient architectures can allow for more compute to be available to robots. What are your thoughts? submitted by /u/n0ided_ [link] [comments]

Hey r/MachineLearning! I manually derived backpropagation steps, did some chained matrix multiplication optims, wrote all kernels in OpenAI's Triton language and did more maths and coding trickery to make QLoRA finetuning for Llama 5x faster on Unsloth: https://github.com/unslothai/unsloth! Some highlights: 5x faster (5 hours to 1 hour) Use 50% less memory With 0% loss in accuracy All locally on NVIDIA GPUs (Tesla T4, RTX 20/30/40, Ampere, Hopper) for free! QLoRA / LoRA is now 80% faster to train. On Slim Orca 518K examples on 2 Tesla T4 GPUs via DDP, Unsloth trains 4bit QLoRA on all layers in 260 hours VS Huggingface's original implementation of 1301 hours. Slim Orca 1301 hours to 260 hours You might (most likely not) remember me from Hyperlearn (https://github.com/danielhanchen/hyperlearn) which I launched a few years back to make ML algos 2000x faster via maths and coding tricks. I wrote up a blog post about all the manual hand derived backprop via https://unsloth.ai/introducing. I wrote a Google Colab for T4 for Alpaca: https://colab.research.google.com/drive/1oW55fBmwzCOrBVX66RcpptL3a99qWBxb?usp=sharing which finetunes Alpaca 2x faster on a single GPU. On Kaggle via 2 Tesla T4s on DDP: https://www.kaggle.com/danielhanchen/unsloth-laion-chip2-kaggle, finetune LAION's OIG 5x faster and Slim Orca 5x faster. You can install Unsloth all locally via: pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git" Currently we only support Pytorch 2.1 and Linux distros - more installation instructions via https://github.com/unslothai/unsloth/blob/main/README.md I hope to: Support other LLMs other than Llama style models (Mistral etc) Add sqrt gradient checkpointing to shave another 25% of memory usage. And other tricks! Thanks a bunch!! submitted by /u/danielhanchen [link] [comments]

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE)

As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities,

I've noticed a trend recently of authors adding more formalism than needed in some instances (e.g. a diagram/ image would have done the job fine). Is this such a thing as adding more mathematics than needed to make the paper look better or perhaps it's just constrained by the publisher (whatever format the paper must stick to in order to get published)? submitted by /u/Inquation [link] [comments]

We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. SageMaker provides

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. Although

Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated

As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that support multiple tenants—for data scientists internal to their organization and external customers. More and more companies are realizing the value of using FMs to generate

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide the best experience to end-users. However, some FMs don’t fully utilize

Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. As a fully managed service, you can scale your model deployments, minimize inference costs, and manage your models more effectively in production with reduced operational

Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization) and algorithms, customizing the training and validation data split ratio, and

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. Creating a resilient environment that can handle failures and environmental changes without losing days or weeks of model training progress is an operational challenge that requires you to

Digital publishers are continuously looking for ways to streamline and automate their media workflows to generate and publish new content as rapidly as they can, but without foregoing quality. Adding images to capture the essence of text can improve the reading experience. Machine learning techniques can help you discover such images. “A striking image is

The risks associated with generative AI have been well-publicized. Toxicity, bias, escaped PII, and hallucinations negatively impact an organization’s reputation and damage customer trust. Research shows that not only do risks for bias and toxicity transfer from pre-trained foundation models (FM) to task-specific generative AI services, but that tuning an FM for specific tasks, on

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler. With this integration, SageMaker Canvas provides customers with an end-to-end no-code workspace to prepare data, build and use ML and

In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with unprecedented proficiency. Their potential applications span from conversational agents to content generation and information retrieval, holding the promise of revolutionizing all industries. However, harnessing this potential while ensuring the responsible and

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. However, the increasing cost associated with training and fine-tuning these models poses a challenge for enterprises. This cost is primarily driven by the

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically run notebooks as jobs

The rapid growth of generative AI brings promising new innovation, and at the same time raises new challenges. These challenges include some that were common before generative AI, such as bias and explainability, and new ones unique to foundation models (FMs), including hallucination and toxicity. At AWS, we are committed to developing generative AI responsibly,

Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI. Customers worked closely with us to prioritize use cases,

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]