Sorry if this question is stupid but which would perform better for ML? I’d think the RTX but couldn’t find anything online. Except for the fact that m3 Max is close to the RTX in terms of FPS performance. submitted by /u/BukHunt [link] [comments]

Hello, ML enthusiasts! Exciting news for those seeking career opportunities in IT, Engineering, Healthcare, Life Sciences, and beyond! We have 250 open positions worldwide, covering various roles. Discover your dream job in the Machine Learning world. Explore the opportunities here: https://www.compunnel.com/job-search/ Don't miss the chance to advance your career and contribute to cutting-edge projects. Best of luck to all ML enthusiasts! Feel free to share with anyone interested. submitted by /u/digital-services-usa [link] [comments]

Do you know of any github repositories that either help with building a web search ai agent or that has a good one? github repositories that I saw so far but have not yet tried : langchain (the WebResearchRetriever and weblangchain for example (have not tried either) ) autogpt gpt-researcher [Edit: changed researchgpt to gpt-researcher] submitted by /u/No-Entertainer-802 [link] [comments]

Hi guys, I'm in final year in university and I have a final graduation project about Detecting wrong way vehicles. The system flow is that there will be a camera in front of vehicle that the vehicle will act the detector, but I have the problem that I didn't now what algorithm I could use to help me differentiating between wrong and right way vehicles. As is first I was developing the system as it will establish the camera in a RSU (road side unit) that the camera will be static so I could Use centroid tracking algorithm. But I don't find CCTV dataset that has Wrong way vehicles. I have now a dashcam dataset. So could someone help me from where I could start with the algorithm? Is there is something better than k-nearest nieghbour? submitted by /u/ibrahim1027 [link] [comments]

There are many embedding models out there that we can use to encode our data into vectors. Let us say I use the OpenAI embedding model to encode my article into vector database, and use it. Fast forward to 20 years later, for my ML app to work, I need to encode my query with same embedding model (the one today) in order to make results meaningful. But what if OpenAI is gone, and one cannot find the embedding model used in 20 years ago? Apparently this is just an example - my question is: when people encode their data using embedding, how do they keep a copy of the embedding model used, securely and reliably, for future inference needs, or this is not necessary? submitted by /u/kakasaid [link] [comments]

I’m currently a first year graduate from undergraduate college working full time as an ML engineer. My company offers to pay for a graduate degree and I’m really considering getting a masters. First question: for those who are ML or data scientists, what were your degrees in? Second question: how much of this will suck? Is taking two classes per semester reasonable? And are online universities considered okay? Third question: how much of a pay bump did you see after getting your masters? submitted by /u/Fluid-Pipe-2831 [link] [comments]

Hi guys, I’m in my third year of university and I’m currently working on my first research paper. The project explores the trade-offs in accuracy, interpretability and computational complexity if NAS created DNNs. Similar work has been done within the past 5 years, but they only explore creating and using computer vision benchmarks like NAS-Bench 201. These benchmarks create a search space of convolution networks but none only explore DNNs, RNNs at all. My work explores only DNNNs without using an existing benchmark and I wrote in the further work that developing a standard non-convolution benchmark can be done. I feel like my results can help research in this particular area since a lot of people value the use of DNNs. I want to submit it, but I would want to submit it at at least a medium-tier conference but I’m so insecure about my work. Any advice on how I should tackle this? Also, I did this project 100% on my own which is why I am even more embarrassed. Feel like it’s crappy and I’m wasting my time submitted by /u/waffleman221 [link] [comments]

I want to train a GAN model overnight for a project that's due tomorrow. I've trained it several times with only 30 epochs to play around with some of the hyperparameters. Now I'm wondering, is one of the following better (arbitrary numbers)? - 100 epochs with 300 batch size - 300 epochs with 100 batch size I'm leaning towards more epochs, smaller batch because the discriminator is updated after every batch, so that would allow for more updates to the discriminator. Would love to hear any opinions on this. Thanks! submitted by /u/tfren99 [link] [comments]

Interactive demo sample arXiv: https://arxiv.org/abs/2312.03414 GitHub: https://github.com/snu-mllab/Context-Memory Project page: https://janghyun1230.github.io/memory/ Summary: Our approach dynamically creates compressed memory of contexts during LLM interactions. Our approach only requires training a conditional LoRA for compression. We use a fully parallelized training strategy for recurrent compression procedures. We conduct evaluations on diverse applications: conversation, multi-task ICL, and personalization, achieving the performance level of a full context model with 5x smaller context memory space. https://preview.redd.it/5vmwxa7luy4c1.png?width=2162&format=png&auto=webp&s=0f717a1528688b6fd9b47f3c7e43be66b1b5c78b submitted by /u/janghyun1230 [link] [comments]

Hi all. I am trying to create a straightforward and concise ML curriculum. Any input or contribution is welcomed. The goal is to keep it to the point with the main curriculum while adding additional resources in separate sections. ​ Link: https://github.com/pytholic/Machine-Learning-Curriculum submitted by /u/rajahaseeb147 [link] [comments]

I read through the Google Gemini technical report yesterday. It was pretty vague and not that interesting but one section got me wondering. Section 3 "Training Infrastructure" mentioned all of the technical challenges they faced including dealing with "cosmic rays" and other rare failure modes that happen at the scale of thousands of chips. I haven't heard of GPU training having the same issues with cosmic rays and other large AI labs haven't mentioned how challenging training at this scale can be. So this has me wondering:-- Is Google just talking up their achievement and making it sound more difficult and advanced than it really is here?-- Are those challenges unique to Google's infra and TPUs?-- What are the real challenges at training at a large scale (i.e. 300B-1T parameters)? submitted by /u/cowzombi [link] [comments]

I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following: ​ https://preview.redd.it/4r96tp6lxx4c1.png?width=836&format=png&auto=webp&s=10f2f61cd4cea96f4f903cb2070835fc5d1df951 ​ https://preview.redd.it/32ler5vnxx4c1.png?width=622&format=png&auto=webp&s=dd00e53f43dd0afa058758a987901ee6789d2258 ​ https://preview.redd.it/sc96i4xoxx4c1.png?width=678&format=png&auto=webp&s=94d2ed279054363d3ed2b6beed65be89468582b0 So much faster than self-attention, and so much smoother, running at 6 epochs per second. I'm honestly gobsmacked. https://colab.research.google.com/drive/1g9qpeVcFa0ca0cnhmqusO4RZtQdh9umY?usp=sharing ​ Some loss graphs: Multihead attention without truncation(x is iterations in 10s, and y is loss) Multihead attention with truncation(x is iterations in 10s, and y is loss) Mamba loss graph(x is iterations in 10s, and y is loss) ​ ​ submitted by /u/ExaminationNo8522 [link] [comments]

When working with LLMs, I frequently experience token agony. Error: This model's maximum context length is 4097 but you are trying to push in all of War and Peace, you imbecile Perhaps you've experienced it too! The issue is particularly pronounced with retrieval augmented pipelines, since you have potentially quite a large set of documents which you could perhaps include in the prompt if only you knew how big it could be. I got tired of hacking around this headache, so I wrote flex-prompt to address it. I wish I didn't have to. Perhaps someone can point me to a better solution! But I couldn't find one, so alas, here it is. flex-prompt provides a basic layout and component model to help you describe how you want the pieces of your prompt to grow and shrink and a token-aware renderer which renders your prompt to fit your model's window. Github, Intro to flex prompt colab Quick examples You can just render(Flex(...)), and flex prompt will fit the prompt into the context window, and tell you how many tokens are left over for the response: from flex_prompt import render, Flex, Expect rendered = render( Flex([ "Given the text, answer the question.", "--Text--", WAR_AND_PEACE, "--End Text--", "Question: What's the title of this text?", "Answer:", Expect() ], join='\n'), model='text-davinci-002') # rendered.output is the string to send to the model # rendered.max_response_tokens is how many tokens you can # request in response without exceeding the model's context window print(rendered.output, rendered.max_response_tokens) More typically, you'll want to define a prompt which takes parameters. To do this, you can create a class (probably a dataclass) which derives Flexed: from flex_prompt import Flexed, Expect from dataclasses import dataclass @dataclass class Ask(Flexed): text: str question: str answer: str | Expect = Expect() instruct: str = "Given a text, answer the question." flex_join = '\n' # yielded items will be joined by newlines def content(self, _ctx): if self.instruct: yield 'Given the text, answer the question.' yield '' yield '-- Begin Text --' # note: we're using `Flex` here just to attach a flex_weight # to the text, telling the renderer we'd like more space for the # text than anything else. yield Flex([self.text], flex_weight=2) yield '-- End Text --' yield 'Question: ', self.question yield 'Answer: ', self.answer The renderer works much as you might expect. You can `yield` anything which you can pass to the top-level render function, including other components, creating a whole tree. Note that the component above can be used to render both the actual prompt and examples. Examples simply have an answer. This is useful for experimenting with different ways of structuring a prompt while ensuring that all the examples we present to the LLM are in the same format. LangChain and Haystack Integrations Flex prompt doesn't really care how you execute your prompt. For convenience, render(model=) does accept both LangChain and Haystack models: ask_tolstoy = Ask(text=WAR_AND_PEACE, question="Who wrote this?") # Using LangChain from langchain.llms import OpenAI lc_llm = OpenAI() rendering = render(ask_tolstoy, model=lc_llm) print(lc_llm(rendering.output, max_tokens=rendering.max_response_tokens)) # Using Haystack from haystack.nodes import PromptModel hs_llm = PromptModel(model_name_or_path='text-davinci-002', api_key=os.environ['OPENAI_API_KEY']) rendering = render(ask_tolstoy, model=hs_llm) print(hs_llm.invoke(rendering.output, max_tokens=rendering.max_response_tokens)) Is it worth it? As models grow larger and larger context windows, I've asked myself whether this is worth it. Won't context sizes eventually big enough to put in everything we might want without worry? One response: "everything I might want" is a very, very big set, plausibly bigger than any window size we're going to see soon. Another: being able to do this kind of token accounting is useful even if we don't completely fill context windows. For example, we might be able to augment our prompt with examples, documents, and tips. How much space should we allocate to each? The answer might well be model-dependent. How do we figure it out? Flex prompt's output, a Rendering object, actually holds the entire component tree. You can look through the object to see how many tokens were allocated to each child. This is currently very manual, but it does provide the bedrock infrastructure to e.g. run tests to discover the optimal balance of augmented data for a given prompt and model. Additionally, the right admixture (and for that matter, the right phrasing) may well be model-dependent. Flex prompt currently provides only very limited model-specific rendering (you can look at ctx.target, but it doesn't tell you much), but there's no reason that can't be significantly improved. At the extreme limit is prompt erasure, where we fine-tune a model to require no or minimal instructions/examples for a given set of prompts. Flex prompt can enable transitions like this with no changes to the pipelines themselves: you'd still use the same prompt components, they'd just render differently if the target is a fine-tuned model vs. a generic one. Status & Future Work Flex prompt is very much in early development. I would love to hear if and how people find it useful, and would love input and contributions! Some things I'd like to tackle in the future: Rendering message lists. Flex prompt currently only renders strings, though it's set up to be able to render any type of output. Message histories basically grow without bound, so supporting this seems like a no-brainer. Pagination. If your rendering overflows (as above, where we're trying to stuff the entirety of war and peace into a prompt), flex prompt will clip the offending pieces to fit. But there's currently no way to get "the next page". But the Rendering actually retains enough information to do this! It would be great to be able to call render(...).pages() to get the sequence of prompts as we "scroll" whatever has overflowed. This is medium-hanging fruit—a little tricky because we do have to descend the tree of renderings to find the exact one(s) which overflowed and then update only those. Token accounting. As mentioned above, you can currently grovel around in Rendering and look at the pieces of the prompt. This would be more useful if it were a little easier, e.g. if you could use rendering[Examples] to find all the parts rendered by the Examples component, or rendering['advice'] to find all the parts which are tagged (somehow) as "advice". The use case here is prompt optimization: discovering the optimal number or percentage of tokens to allot to each thing we might want to drop into the prompt. More integrations. Currently, flex prompt only supports OpenAI models. You can register your own target finders, but it would be great to have more support out of the box. This is mostly a matter of digging around and finding the tokenizers and window sizes for common models, and then writing the appropriate target finders. Contributions very welcome! Model tuning. As mentioned above, the rendering context could provide a mechanism for fetching model-specific parameters. The basic idea is that ctx[param] will evaluate param against the context, and then we can define some parameter types which load their model-specific values from gestures vaguely somewhere. Thanks for reading! Flex prompt Github Intro to flex prompt colab My website. shameless plug: I have a lot of engineering experience and a bit of machine learning experience and I am currently looking for a job submitted by /u/queerviolet [link] [comments]

I'm sure you all know that nothing in statistics is certain, it's all probabilities and degrees of confidence. Well, I'm finding business people simply just don't comprehend that. The amount of times I've had to explain that correlation =/= causation, or why aggregate metrics based on very small sample sizes aren't reliable is insane. Like I get it's not their job to know stats and data science, but at some point these things should be common sense, and I shouldn't have to waste half a meeting explaining it for the 30th time. And whenever I come back to them with some kind of result, I choose my words carefully, not to over promise, cause guess who's ass is on the line if I'm wrong. If I say "increased advertising appears to be correlated with increased sales", they hear "spend more money on advertising". They will then spend that money and if it doesn't work, I'm the one who apparently messed up. I've been working around it by both choosing my words carefully and creating documentation. Kind of like a CYA, if they don't heed my warnings and it blows up in their faces, at least I can point back to them saying I told them. For the former, it's in one ear out the other, are increasingly happening in meetings where there is no official transcript I can point back to. They don't listen to any of my warnings about over-concluding from my results. As to the former, well I was told recently after doing a sales analysis that "no one gives a fuck" about my methodology or results. Drop it from the write up. They just want broad conclusions and actions. In my mind, my job is to tell them what I found and let them draw their own conclusions. I get that data science and stats aren't their job, but at the same job, sales isn't mine, so why am I making conclusions for sales people about what they should do? IDK, I figure this is common for this field, so what do you guys do? submitted by /u/son_of_tv_c [link] [comments]

Hi, Is there a tool that indicates which parts of the input prompt impact the LLM's output the most? I do not care which LLM the tool is for if it exists. I guess it could be backtracked via the weights of each node in the neural network, but you guys are smarter than me so I'll listen to y'all. My use case is I have a prompt that slightly changes variation to variation. The output of the model is "Yes" or "No", so I want to see which parts of the prompt I change impact its response Best, A Reddit User submitted by /u/ToughOpening [link] [comments]

Are you aware of any public datasets that have at least 2 trials / samples / rows per subject? Could be in any domain. Preferably with > 100 subjects, and the tests not sampled years apart (but not dealbreakers). For instance, a large cohort of patients who have had ECG scans collected on 2 separate occasions. I am slowly working my way through the PhysioNet databases: https://physionet.org/about/database/ Most of course only have one scan per subject. submitted by /u/ZeApelido [link] [comments]

Hey ML folks! Found ICPRAI on aideadlin.es but don't know much about it. Any insights on the conference quality, review process, or personal experiences? Considering submitting a paper and looking for advice. Your thoughts are much appreciated! submitted by /u/SufficientAd3564 [link] [comments]

When I was first hired as a DS, I was working on data analysis, statistics, and experimental design aspects. Whenever I did any ML, it was always just in a Jupyter notebook environment and didn't seem to go anywhere beyond that. I want to delve deeper into some MLE/CS topics. for a variety of reasons. In the past year I have become more focused on putting ML models and data analysis into production. I want to be self sufficient. I don't like having to beg for help from a software engineer to make changes to the production environment. Can you suggest any beginner hands on tutorials on any of these topics: 1) Constructing python modules, including python requirements files, setup.py, etc. 2) deploying a module in a docker container 3) Constructing an API with #1 and #2? Not sure if this makes sense. 4) Other topics, such as Airflow, AWS, etc. submitted by /u/Dezireless [link] [comments]

When I was first hired as a DS, I was working on data analysis, statistics, and experimental design aspects. Whenever I did any ML, it was always just in a Jupyter notebook environment and didn't seem to go anywhere beyond that. I want to delve deeper into some MLE/CS topics. for a variety of reasons. In the past year I have become more focused on putting ML models and data analysis into production. I want to be self sufficient. I don't like having to beg for help from a software engineer to make changes to the production environment. Can you suggest any beginner hands on tutorials on any of these topics: 1) Constructing python modules, including python requirements files, setup.py, etc. 2) deploying a module in a docker container 3) Constructing an API with #1 and #2? Not sure if this makes sense. 4) Other topics, such as Airflow, AWS, etc. submitted by /u/Dezireless [link] [comments]

When I was first hired as a DS, I was working on data analysis, statistics, and experimental design aspects. Whenever I did any ML, it was always just in a Jupyter notebook environment and didn't seem to go anywhere beyond that. I want to delve deeper into some MLE/CS topics. for a variety of reasons. In the past year I have become more focused on putting ML models and data analysis into production. I want to be self sufficient. I don't like having to beg for help from a software engineer to make changes to the production environment. Can you suggest any beginner hands on tutorials on any of these topics: 1) Constructing python modules, including python requirements files, setup.py, etc. 2) deploying a module in a docker container 3) Constructing an API with #1 and #2? Not sure if this makes sense. 4) Other topics, such as Airflow, AWS, etc. submitted by /u/Dezireless [link] [comments]