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Hot data science related posts every hour. Chat: https://telegram.me/r_channels Contacts: @lgyanf
D Self-Promotion Thread
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/r/MachineLearning
https://redd.it/1l16j5k
Free Course Hero Unlocker 2025: What’s Actually Working Right Now?
Unlock Course Hero Docs Without Paying – Safe & Tested Methods
Hey friends 👋
If you’ve been scouring the internet for a working Course Hero unlocker, you’re not alone. I’ve been deep in the trenches trying different tools, reading Reddit threads, and testing what actually works in 2025 to get free Course Hero unlocks.
Some methods are outdated, others are sketchy—but a few are still solid, and I wanted to share what I found (and hear from others too!).
🔍 Top Working Methods to Unlock Course Hero in 2025:
1. 📥 Course Hero Unlocker via Discord
This is the one that stood out the most. A Discord server where you can get free unlocks for Course Hero, Chegg, Scribd, Brainly, Numerade, etc. No payment, just follow the instructions (usually involves upvoting or interacting).
✅ Free unlocks
✅ Fast response
✅ Covers multiple platforms
✅ Active community
2. 📤 Upload Docs to Course Hero
If you’ve got notes or study guides from past classes, upload 8 original files and get 5 unlocks free. You also get a shot at their $3,000 scholarship.
Good if you’ve already got files saved. Not instant, but legit.
3. ⭐ Rate Other Course Hero Docs
This is a low-effort option:
Rate 5 documents → Get 1 unlock
Repeat as needed. It works fine, but isn’t great if you need more than 1 or 2 unlocks quickly.
💬 Still Wondering:
Has anyone used the Discord Course Hero unlocker recently?
Are there any Course Hero downloader tools that are real (and not just fake popups)?
What’s the safest way to view or download a Course Hero PDF for free?
Any risks I should watch for when using third-party tools?
💡 Final Thoughts:
If you’re looking for the fastest and easiest Course Hero unlocker in 2025, I’d say check out the Discord server above. It’s free, responsive, and works for a bunch of sites. If you prefer official methods, uploading docs or rating content still works—but can be slow.
Let’s crowdsource the best options. Share what’s worked for you 👇 so we can all study smarter (and cheaper) this year 🙌
/r/deeplearning
https://redd.it/1lhpjqs
[P] Research Scientists + Engineers for Generative AI at NVIDIA
We’re hiring senior and principal research scientists to shape the future of generative AI at NVIDIA.
We're looking for builders with deep experience in LLMs and/or multimodal models. You’ll work on **training and deploying frontier-scale models**, designing next-gen model architectures, optimizing training stacks, and helping us **push the frontier of AI performance**.
We’re a tight-knit team with high standards, strong research instincts, and a bias for shipping.
Open roles:
* [**Senior Software Engineer, GenAI**](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Senior-Software-Engineer--Generative-AI_JR1997674)
* [**Principal GenAI Software Engineer**](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Principal-Generative-AI-Software-Engineer_JR1997454)
What we value:
* Deep understanding of transformer architectures, distributed training and optimization
* Using the scientific method for conducting methodical training experiments
* Data curation for pre-training and post-training
* Experience working with LLMs and/or large multimodal models
* A builder mindset — clean code, fast iterations, deep thinking
This is a rare opportunity to **help shape NVIDIA’s genAI stack from the ground up**. We work closely with software, optimization, deployment, and many other research teams, and have massive scale and resources behind us.
Feel free apply directly through the links.
/r/MachineLearning
https://redd.it/1lcmxeb
R The Illusion of Thinking | Apple Machine Learning Research
Research Publication
[The Illusion of Thinking | Apple Machine Learning Research](https://macro.com/app/pdf/a6623497-ee26-47e2-a610-bf9a05d56217/md/82e9944f-b4a6-4bf7-8b6f-6402b73d5d87)
Quick Run-Down
The Complexity Cliff: Reasoning models don't gradually degrade—they catastrophically fail. Beyond specific complexity thresholds, even the most advanced models (Claude 3.5, DeepSeek-R1, o3-mini) plummet from near-perfect accuracy to complete failure. The sharp discontinuity suggests these systems lack true compositional reasoning; they're pattern-matching within their training distribution rather than building genuine logical structures.
The Inference Paradox: When compute is held constant, a striking pattern emerges across three complexity regimes. Simple problems expose reasoning models as wasteful—standard LLMs achieve better results with fewer tokens. Only at medium complexity do reasoning models justify their computational overhead. At high complexity, all approaches fail equally, revealing that more "thinking" tokens can't overcome fundamental architectural limitations. The implication: current reasoning approaches may be solving the wrong problem.
The Giving-Up Phenomenon: Perhaps the study's most puzzling finding: as problems approach critical difficulty, reasoning models reduce their thinking effort—well before hitting token limits. The self-limiting behavior suggests these models possess some implicit awareness of their own limitations, abandoning deeper exploration when problems exceed their capabilities. The models appear to "know" when they don't know, but lack the tools to push beyond.
The Overthinking Trap: Examining reasoning traces reveals a troubling pattern. On simple problems, models find correct answers quickly but continue exploring dead ends—computational waste masquerading as thoroughness. Medium-complexity problems show productive exploration eventually yielding solutions. But complex problems trigger endless, fruitless wandering. The progression from overthinking to productive search to complete breakdown maps the boundaries of what these models truly understand versus what they merely approximate.
The Execution Failure: The Tower of Hanoi experiments deliver a sobering verdict: even with step-by-step algorithms provided, models fail at the same complexity points. The challenge isn't search—the challenge is execution. These systems struggle with the mechanical application of logical rules, suggesting their "reasoning" is more associative than algorithmic. The finding challenges the narrative that these models have learned generalizable reasoning procedures; instead, they appear to have memorized reasoning patterns that break down under novel demands.
/r/MachineLearning
https://redd.it/1l7ofw0
D Monthly Who's Hiring and Who wants to be Hired?
For Job Postings please use this template
>Hiring: [Location\], Salary:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] and [Brief overview, what you're looking for\]
For Those looking for jobs please use this template
>Want to be Hired: [Location\], Salary Expectation:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] Resume: [Link to resume\] and [Brief overview, what you're looking for\]
​
Please remember that this community is geared towards those with experience.
/r/MachineLearning
https://redd.it/1kzmd2e
Detecting Rooftop Solar Panels in Satellite Imagery Using Mask R-CNN (TensorFlow)
/r/computervision
https://redd.it/1ky43yb
AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery | Google DeepMind White Paper
**Research Paper:**
* **Blog Post**: [AlphaEvolve: A Gemini-Powered Coding Agent for Designing Advanced Algorithms](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/)
* **White Paper**: [AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery | Google DeepMind White Paper](https://macro.com/app/pdf/35647fcc-191e-4cdf-8d64-e8d862140dfa)
**Main Findings:**
* **Matrix Multiplication Breakthrough:** AlphaEvolve revolutionizes matrix multiplication algorithms by discovering new tensor decompositions that achieve lower ranks than previously known solutions, including surpassing Strassen's 56-year-old algorithm for 4×4 matrices. The approach uniquely combines LLM-guided code generation with automated evaluation to explore the vast algorithmic design space, yielding mathematically provable improvements with significant implications for computational efficiency.
* **Mathematical Discovery Engine:** Mathematical discovery becomes systematized through AlphaEvolve's application across dozens of open problems, yielding improvements on approximately 20% of challenges attempted. The system's success spans diverse branches of mathematics, creating better bounds for autocorrelation inequalities, refining uncertainty principles, improving the Erdős minimum overlap problem, and enhancing sphere packing arrangements in high-dimensional spaces.
* **Data Center Optimization:** Google's data center resource utilization gains measurable improvements through AlphaEvolve's development of a scheduling heuristic that recovers 0.7% of fleet-wide compute resources. The deployed solution stands out not only for performance but also for interpretability and debuggability—factors that led engineers to choose AlphaEvolve over less transparent deep reinforcement learning approaches for mission-critical infrastructure.
* **AI Model Training Acceleration:** Training large models like Gemini becomes more efficient through AlphaEvolve's automated optimization of tiling strategies for matrix multiplication kernels, reducing overall training time by approximately 1%. The automation represents a dramatic acceleration of the development cycle, transforming months of specialized engineering effort into days of automated experimentation while simultaneously producing superior results that serve real production workloads.
* **Hardware-Compiler Co-optimization:** Hardware and compiler stack optimization benefit from AlphaEvolve's ability to directly refine RTL circuit designs and transform compiler-generated intermediate representations. The resulting improvements include simplified arithmetic circuits for TPUs and substantial speedups for transformer attention mechanisms (32% kernel improvement and 15% preprocessing gains), demonstrating how AI-guided evolution can optimize systems across different abstraction levels of the computing stack.
/r/computervision
https://redd.it/1krnp0n
Strange phenomenon with trainning yolov5s
/r/deeplearning
https://redd.it/1kl8m50
AI Workstation for €15,000–€20,000 – 4× RTX 4090 Worth It?
Hey everyone,
I'm currently planning to build a high-end system for AI/ML purposes with a budget of around €15,000 to €20,000. The goal is to get maximum AI compute power locally (LLMs, deep learning, inference, maybe some light fine-tuning), without relying on the cloud.
Here’s the configuration I had in mind:
CPU: AMD Threadripper PRO 7965WX (24 cores, 48 threads)
Motherboard: ASUS Pro WS WRX90E-SAGE SE (sTR5, 7× PCIe 5.0 x16)
RAM: 512 GB ECC DDR5
GPU: 4× NVIDIA RTX 4090 (24 GB GDDR6X each)
Storage: 2× 8TB Seagate Exos
PSU: Corsair AX1600i
I have about 3 months of time to complete the project, so I’m not in a rush and open to waiting for upcoming hardware.
Now, here are my main questions:
1. Does this setup make sense in terms of performance for the budget, or are there better ways to maximize AI performance locally?
2. Would you recommend waiting for 2× RTX 6000 Ada / Blackwell models if long-term stability and future-proofing are priorities?
3. Is 4× RTX 4090 with proper software (Ray, DDP, vLLM, etc.) realistically usable, or will I run into major bottlenecks?
4. Has anyone built a similar system and has experience with thermals or GPU spacing
5. I’d really appreciate any input, suggestions, or feedback from others who’ve done similar builds.
Thanks a lot 🙏
/r/deeplearning
https://redd.it/1kgsazv
How to go about finding the horizon line in the sea?
/r/computervision
https://redd.it/1kcijbh
Such loss curves make me feel good
/r/deeplearning
https://redd.it/1k9oub0
Do I have a chance at ML (CV) PhD?
So I have been thinking for a few months about doing a phd in 3DCV, inverse rendering and ML. I know it is super competitive these days when I see people getting into top schools already have CVPR / ECCV papers. My profile is nowhere close to them however I do have 2 years of research experience (as RA during MS in a good public school in the US) in computer vision and physics as well as my masters thesis/project revolves around SOTA 3D object detection + robotics (perception sim to real). I recently submitted it to IROS (fingers crossed). Did some good CV internships and work as a software engineer at FAANG now.
But again seeing the profiles that get into top schools makes me shit my pants. They have so many papers (even first authored) already. Do I have a chance?
/r/computervision
https://redd.it/1k5kqql
Trying to build computer vision to track ultimate frisbee players… what tools should I use?
https://redd.it/1k0bi9b
@datascientology
I built a biomedical GNN + LLM pipeline (XplainMD) for explainable multi-link prediction
https://redd.it/1jvuz3b
@datascientology
ML Data Linguist Interview - Coding
Hello all, first post here. I'm having a second set of interviews next week for an Amazon ML Data Linguist position after having a successful first phone interview last week. I'll start right away with the problem: I do not know how to code. I made that very clear in the first phone interview but I was still passed on to this next set of interviews, so I must have done/said something right. Anyway, I've done research into how these interviews typically go, and how much knowledge of each section one should have to prepare for these interviews, but I'm just psyching myself out and not feeling very prepared at all.
My question in its simplest form would be: is it possible to get this position with my lack of coding knowledge/skills?
I figured this subreddit would be filled with people with that expertise and wanted to ask advice from professionals, some of whom might be employed in the very position I'm applying for. I really value this opportunity in terms of both my career and my life and can only hope it goes well from here on out. Thanks!
/r/LanguageTechnology
https://redd.it/1jpruz4
The Company Banned By LinkedIn For Being Too Good At Getting Jobs
/r/deeplearning
https://redd.it/1liyd5d
Is applied NLP expertise still relevant in LLM Era?
In the era of LLM, does your company still train NLP models from scratch? Fine-tuning the pre-trained models (e.g: BERT) still counted as from scratch.
Or most of the use cases already can be solved by just calling LLM APIAI Agent/MCP/host your LLM by yourself?
Given the accuracy, I believe LLM already give you good baseline for common NLP use cases. You can tailor the needs by giving a good prompts based on your needs.
However, the current LLM solutions still far away from the perfect due to model hallucinations, system reliability (e.g: high latency), and the cost of using this tech still considered as high.
For the cost, it's still debatable as the business owners can choose whether to hire NLP experts or subscribe to these LLM APIs and let software engineer to integrate the solutions.
Assuming the LLM is getting better overtime, does applied NLP expertise still relevant in industries/markets?
NB: NLP expertise here as someone who can train the NLP model from scratch
/r/LanguageTechnology
https://redd.it/1lcps10
D The effectiveness of single latent parameter autoencoders: an interesting observation
During one of my experiments, I reduced the latent dimension of my autoencoder to 1, which yielded surprisingly good reconstructions of the input data. (See example below)
Reconstruction \(blue\) of input data \(orange\) with dim\(Z\) = 1
I was surprised by this. The first suspicion was that the autoencoder had entered one of its failure modes: ie, it was indexing data and "memorizing" it somehow. But a quick sweep across the latent space reveals that the singular latent parameter was capturing features in the data in a smooth and meaningful way. (See gif below) I thought this was a somewhat interesting observation!
Reconstructed data with latent parameter z taking values from -10 to 4. The real\/encoded values of z have mean = -0.59 and std = 0.30.
/r/MachineLearning
https://redd.it/1la6plp
Perception Engineer C++
Hi! I have a technical interview coming up for an entry level perception engineering with C++ for an autonomous ground vehicle company (operating on rugged terrain). I have a solid understanding of the concepts and feel like I can answer many of the technical questions well, I’m mainly worried about the coding aspect. The invite says the interview is about an hour long and states it’s a “coding/technical challenge” but that is all the information I have. Does anyone have any suggestions as to what I should be expecting for the coding section? If it’s not leetcode style questions could I use PCL and OpenCV to solve the problems? Any advice would be a massive help.
/r/computervision
https://redd.it/1l6hlc1
300k+ active software jobs mapped across big tech, AI labs, and unicorn startup
I realized many roles are only posted on internal career pages and never appear on classic job boards.
So I built an AI script that scrapes listings from 70k+ corporate websites.
Then I wrote an ML matching script that filters only the jobs most aligned with your CV, and yes, it actually works.
You can try it here (for free).
(If you’re still skeptical but curious to test it, you can just upload a CV with fake personal information, those fields aren’t used in the matching anyway.)
/r/deeplearning
https://redd.it/1l0xium
I built an open-source face anti-spoofing Model EasyShield – feedback needed !
/r/computervision
https://redd.it/1kvhpos
Motion Capture System with Pose Detection and Ball Tracking
/r/computervision
https://redd.it/1knslv0
Undergraduate Thesis in NLP; need ideas
I'm a rising senior in my university and I was really interested in doing an undergraduate thesis since I plan on attending grad school for ML. I'm looking for ideas that could be interesting and manageable as an undergraduate CS student. So far I was thinking of 2 ideas:
1. Can cognates from a related high resource language be used during pre training to boost performance on a low resource language model? (I'm also open to any ideas with LRLs).
2. Creating a Twitter bot that detects climate change misinformation in real time, and then automatically generates concise replies with evidence-based facts.
However, I'm really open to other ideas in NLP that you guys think would be cool. I would slightly prefer a focus on LRLs because my advisor specializes in that, but I'm open to anything.
Any advice is appreciated, thank you!
/r/LanguageTechnology
https://redd.it/1khwzu1
D Self-Promotion Thread
Please post your personal projects, startups, product placements, collaboration needs, blogs etc.
Please mention the payment and pricing requirements for products and services.
Please do not post link shorteners, link aggregator websites , or auto-subscribe links.
\--
Any abuse of trust will lead to bans.
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.
\--
Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.
/r/MachineLearning
https://redd.it/1kcq3du
Announcing Intel® Geti™ is available now!
/r/computervision
https://redd.it/1kauo4c
Looking for research group
Hey everyone,
I recently published a paper on a new optimizer I’ve been working on called AlphaGrad: [https://arxiv.org/abs/2504.16020](https://arxiv.org/abs/2504.16020) . I’m planning to follow it up with a second paper that includes more experiments, better benchmarks, and a new evolved version of the optimizer.
I did the first version entirely on my own time, but for this next round I’d really love to collaborate. If you’re someone looking to get involved in ML research—whether you’re part of a group or just working solo—I’m open to co-authorship. It’d be awesome to get some fresh perspectives and also speed up the engineering and testing side of things.
A few quick highlights about AlphaGrad:
* It introduces a new update rule using L2 normalization and a smooth tanh transformation
* Performed on par with Adam in off-policy RL environments and outperformed it in on-policy ones (tested on CleanRL)
* I’m currently testing it on GPT2-124M with some promising results that look close to Adam’s behavior
* Also tested it on smaller regression datasets where it did slightly better; now expanding to CIFAR, ResNet, and MNIST
* Targeting to finish up and submit the next paper within the next 2–3 weeks
If this sounds interesting and you’d like to help out or just learn more, feel free to reach out.
/r/deeplearning
https://redd.it/1k72ecv
D Monthly Who's Hiring and Who wants to be Hired?
For Job Postings please use this template
>Hiring: [Location\], Salary:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] and [Brief overview, what you're looking for\]
For Those looking for jobs please use this template
>Want to be Hired: [Location\], Salary Expectation:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] Resume: [Link to resume\] and [Brief overview, what you're looking for\]
​
Please remember that this community is geared towards those with experience.
/r/MachineLearning
https://redd.it/1jnt4sp
have some unused compute, giving it away for free!
I have 4 A100s, waiting to go brrrr 🔥 ..... I have some unused compute, so if anyone has any passion project, and the only hinderance is compute, hmu let's get you rolling.
just ask these questions to yourself before:-
\- can your experiment show some preliminary signals in let's say 100 hours of A100s?
\- is this something new? or recreation of some known results? (i would prefer the former)
\- how is this going to make world a better place?
i don't expect you to write more than 2 lines for each of them.
/r/deeplearning
https://redd.it/1jypq8n
Interspeech 2025 Author Review Phase (April 4th)
Just a heads-up that the Author Review phase for Interspeech 2025 starts!!!
Wishing the best to everyone!
Share your experiences or thoughts below — how are your reviews looking? Any surprises?
Let’s support each other through this final stretch!
/r/LanguageTechnology
https://redd.it/1jrh6q8
Part 2: Fork and Maintenance of YOLOX - An Update!
Hi all!
After my post regarding YOLOX: https://www.reddit.com/r/computervision/comments/1izuh6k/should\_i\_fork\_and\_maintain\_yolox\_and\_keep\_it/ a few folks and I have decided to do it!
Here it is: https://github.com/pixeltable/pixeltable-yolox.
I've already engaged with a couple of people from the previous thread who reached out over DMs. If you'd like to get involved, my DMs are open, and you can directly submit an issue, comment, or start a discussion on the repo.
So far, it contains the following changes to the base YOLOX repo:
`pip install`able with all versions of Python (3.9+)
New YoloxProcessor class to simplify inference
Refactored CLI for training and evaluation
Improved test coverage
The following are planned:
CI with regular testing and updates
Typed for use with mypy
This fork will be maintained for the foreseeable future under the Apache-2.0 license.
Installpip install pixeltable-yolox
Inferenceimport requestsfrom PIL import Imagefrom yolox.models import Yolox, YoloxProcessorurl = "https://raw.githubusercontent.com/pixeltable/pixeltable-yolox/main/tests/data/000000000001.jpg"image = Image.open(requests.get(url, stream=True).raw)model = Yolox.from_pretrained("yolox_s")processor = YoloxProcessor("yolox_s")tensor = processor([image])output = model(tensor)result = processor.postprocess([image], output)
See more in the repo!
/r/computervision
https://redd.it/1jp0o48
