Hello everyone! I am a business analytics graduate soon, and I want to expand on my skills in data science with an online masters from University of Pittsburgh. I want to fast track my career in the best way possible. The course names are listed in the image in case you cant find it in the link. I have done a lot of research on masters programs and so far, this is the best I have gotten so far in terms of my chance at being admitted with my GPA and major. So my question/advice seeking is, whether anyone knows of good programs a person with my profile can get into. Also, Does the fact that it’s called “Master of Science” instead of “Masters of Science in Data Science” matter? Profile: Major: Information Systems and Business Analytics Minor: Data Science GPA: 3.0 Thank you! submitted by /u/LifeIsAJoke7 [link] [comments]

Has anyone successfully been able to schedule a Google Colab Python notebook to run on its own? I know Databricks has that functionality…. Just stumped with Colab. YouTube has yet to be helpful. submitted by /u/Uncle_Cheeto [link] [comments]

My friend implemented the method of Multihead Mixture of Experts in this arxiv paper https://arxiv.org/pdf/2404.15045 and he wanted me to share it with you! https://github.com/lhallee/Multi_Head_Mixture_of_Experts__MH-MOE Try it out. Let me know what you think and I will pass it on to him. submitted by /u/Prudent_Student2839 [link] [comments]

Hello guys, I've been working on a sequential labelling using DNA sequences as inputs. Lately there have been 2 foundation models released HyenaDNA (Based on Hyena operator) and Caduceus (based on mamba), I used both pretrained and from scratch models and performances are terrible even with pretrained ones. Does anyone have experience with this type of models, and what are the potential causes for performance drop ? I am literally getting zero performance for the minority class ? Does mamba deal poorly with class imbalance ? submitted by /u/blooming17 [link] [comments]

This is a small drug toxicity prediction GNN model I wrote/trained repo: https://github.com/Null-byte-00/toxicity-prediction-gnn submitted by /u/Soroush_ra [link] [comments]

I have a use case to use function calling within my application, I am confused whether to choose OpenAI function calling or use Bedrock Agents coupled with Lambda functions for this, which is the best approach? Or help me to choose between these two. submitted by /u/raman_boom [link] [comments]

ML is very good at solving a niche set of problems, but most of the technical nuances are lost on tech bros and managers. What are some problems you have been told to solve which would be impossible (no data, useless data, unrealistic expectations) or a misapplication of ML (can you have this LLM do all of out accounting). submitted by /u/LanchestersLaw [link] [comments]

Does anyone know what datasets are out there for causal inference? I’d like to explore methods in the doubly robust ML literature, and I’d like to compensate my learning by working on some datasets and learn the econML software. Does anyone know of any datasets, specifically in the context of marketing/pricing/advertising that would be good sources to apply causal inference techniques? I’m open to other datasets as well. submitted by /u/Direct-Touch469 [link] [comments]

Does anyone know what datasets are out there for causal inference? I’d like to explore methods in the doubly robust ML literature, and I’d like to compensate my learning by working on some datasets and learn the econML software. Does anyone know of any datasets, specifically in the context of marketing/pricing/advertising that would be good sources to apply causal inference techniques? I’m open to other datasets as well. submitted by /u/Direct-Touch469 [link] [comments]

Dreambooth the MusicGen model suite on small consumer GPUs, in a matter of minutes, using this repository: https://github.com/ylacombe/musicgen-dreamboothing The aim of this project is to provide tools to easily fine-tune and dreambooth the MusicGen model suite, with little data and to leverage a series of optimizations and tricks to reduce resource consumption, thanks to LoRA adaptors. For example, the model can be fine-tuned on a particular music genre or artist to give a checkpoint that generates in that given style. The aim is also to easily share and build on these trained checkpoints, Specifically, this involves: using as few data and resources as possible. We're talking fine-tuning with as little as 15mn on an A100 and as little as 10GB to 16GB of GPU utilization. easily share and build models thanks to the Hugging Face Hub. optionally, generate automatic music descriptions optionally, training MusicGen in a Dreambooth-like fashion, where one key-word triggers generation in a particular style Wandb example of what the training run looks like here. submitted by /u/Sufficient-Tennis189 [link] [comments]

Paper: https://arxiv.org/abs/2402.11131 Abstract: Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of downstream tasks grows, these draft models add significant complexity to inference systems. We propose Speculative Streaming, a single-model speculative decoding method that fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. Speculative Streaming speeds up decoding by 1.8 - 3.1X in a diverse set of tasks, such as Summarization, Structured Queries, and Meaning Representation, without sacrificing generation quality. Additionally, Speculative Streaming is parameter-efficient. It achieves on-par/higher speed-ups than Medusa-style architectures while using ~10000X fewer extra parameters, making it well-suited for resource-constrained devices. submitted by /u/SeawaterFlows [link] [comments]

Paper: https://arxiv.org/abs/2404.08698 Abstract: While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by allowing the simultaneous generation of multiple tokens. ANPD incorporates a two-stage approach: it begins with a rapid drafting phase that employs an N-gram module, which adapts based on the current interactive context, followed by a verification phase, during which the original LLM assesses and confirms the proposed tokens. Consequently, ANPD preserves the integrity of the LLM's original output while enhancing processing speed. We further leverage a multi-level architecture for the N-gram module to enhance the precision of the initial draft, consequently reducing inference latency. ANPD eliminates the need for retraining or extra GPU memory, making it an efficient and plug-and-play enhancement. In our experiments, models such as LLaMA and its fine-tuned variants have shown speed improvements up to 3.67x, validating the effectiveness of our proposed ANPD. submitted by /u/SeawaterFlows [link] [comments]

I am wondering if anyone has encountered this question in job applications or interviews and what the best answers might be? Honestly, besides being adequately compensated, I am motivated by challenges that allow me to learn, a supportive environment, and a clear direction for growth. What would be your answers? submitted by /u/Curious-Fig-9882 [link] [comments]

I just wanted to remind or introduce newcomers to this paper. I think this discussion should be re-opened since many people here actually do influence the trends of the field. https://arxiv.org/pdf/1807.03341 On a personal note (feel free to skip): Specifically, I want to point out the issue of "Mathiness", as it seems like this problem got way out of hand and most best papers of conferences suffer from it (one of the most important ML papers tried to be mathy and introduced a big mistake, I believe other papers have bigger issues but no one bothers to check it). So here are my personal points to academics and researchers: We (I think most will relate), practitioners, do not need equations to know what recall is and clearly don't want to read difficult-to-understand versions of what linear regression is, it just makes your paper unuseful. If you don't want to waste our time, please put it in the appendix or completely remove it. Reviewers, please don't get impressed by unnecessary math, if it's complicated and does nothing useful, who cares? Also, it might be flawed anyway and you will probably not catch it. submitted by /u/pyepyepie [link] [comments]

In this video about Times Series, https://www.youtube.com/watch?v=0zpg9ODE6Ww, does anyone have an idea about the Python package used to create the animated plots showed at the 34th minute of the video ? Thank for your help. submitted by /u/SatieGonzales [link] [comments]

Following the review phase(old post), creating a thread for others like me waiting for the decision. All the best! submitted by /u/PaganPasta [link] [comments]

It seems to me that thanks to the residual path the gradient that flows to each layer is the same regardless of the transformer layer/block. Example: ProjectionAndCost(X + L1(X) + L2(X + L1(X)) + L3(X + L1(X) + L2(X + L1(X))) ...) Since the input to ProjectionAndCost is just sum of outputs from all layers and initial embeddings then the gradient that comes to the layer L1 is the same as the gradient that comes to L2 or L3. So we could: first train only L1: ProjectionAndCost(X + L1(X)) freeze L1, include L2 and train: ProjectionAndCost(X + L1(X) + L2(X + L1(X))) freeze L1 and L2, include L3 and train: ProjectionAndCost(X + L1(X) + L2(X + L1(X)) + L3(X + L1(X) + L2(X + L1(X)))) .. and so on We can't train first L2 then L1, because the input to L2 depends on L1, but we could train lower layers first then gradually add and train deeper layers. Is there any problem with that approach? submitted by /u/kiockete [link] [comments]

So there's this relatively new "BigDL" project" (https://bigdl.readthedocs.io/en/latest/ ), which is for Intel CPUs and Intel GPUs, but there's no mention anywhere of it working for NVIDIA GPUs. Is there any equivalent library for NVIDIA GPUs on a spark cluster? submitted by /u/PepperGrind [link] [comments]

Hi. Here is the new's question. The biggest concern is the rate of generation. I want to generate about 5 seconds of voice in about 100ms. I want to know which model performs best(SOTA) under those conditions. Which model is best for me? I think "styletts2" is best. If you have any relevant experience or know any other information, I would really appreciate your help. Thank you ! submitted by /u/hwk06023 [link] [comments]

Hi, does anyone know of a French L2 GEC dataset (that was published at a conference)? submitted by /u/R-e-v-e-r-i-e- [link] [comments]