Hi everyone,

I’m currently working on my diploma study, and I need your help! My research focuses on autonomous vehicles and their impact on society. To gather insights, I’ve created a short survey that explores people’s opinions, expectations, and concerns about self-driving technology.

The survey only takes about 5-10 minutes to complete, and your responses will play a vital role in shaping my research.

Here’s the link to the survey: https://forms.gle/PvjPK2brohdwXiC69

I’d greatly appreciate it if you could spare a few minutes to participate. Your input means a lot, and it’ll help me complete this important step in my academic journey.

Feel free to share the survey with friends or communities who might be interested!

Thank you so much for your time and support!

I am looking for help buying a 3090 with a decent price. It's too expensive and I have to train a model which needs higher VRAM. Where can I look for a decent price for 3090.

Hello everyone I want to write a song recommendation algorithm , I am not sure how to proceed with this project really looking forward to some advice

I am trying to generate images of certain style and theme for my usecase. While working on this I realised it is not that straight forward thing to do. Generating an image according to your needs requires good understanding of Prompt Engineering, Lora/Dreambooth fine tuning, configuring IP-Adapters or ControlNets. And then there's a huge workload for figuring out the deployment (trade-off of different GPUs, different platforms like replicate, AWS, GCP etc.)

Then you get API offerings from OpenAI, StabilityAI, MidJourney. I was wondering if these API is really useful for custom usecase? Or does using API for specific task (specific style and theme) requires some workarounds?

Whats the best way to build your product for GenAI? Fine-tuning by your own or using APIs from renowned companies?

From what I remember, an epoch consists of "seeing all examples one more time". With never-ending data coming it, it feels like a dated notion. Are there any alternatives to it? The main scenario that I have in mind is "streaming data". Thanks!

hello guys , hope you are well , is their anyone who know or has idea on how to convert an image of interior (panorama) into 3D model using AI .

Any good ways to unlock Chegg answers for free on Reddit? I’m looking for the easiest way to access Chegg solutions for studying in 2024. After doing some research, there are a lot of options, but I want to find an alternative that's completely safe, easy to use, and doesn’t cost anything. I’ve spent a lot of time comparing different methods to get free access to Chegg answers, but I’m still unsure if I should even bother.

EDIT: Best Homeworkify Alternative: https://discord.gg/xCNQGya76q

Here are a few options I’ve found that seem promising:

Homework Unlocks: This seems to be my top pick after searching. The platform offers a way to earn free unlocks for Chegg without paying anything. It also supports other popular study services like Bartleby, Brainly, and Quizlet. Basically, all major study platforms are included, all for free.

Uploading Documents: A separate way to earn free access is by sharing your own study materials on certain platforms. After uploading helpful resources, you may be rewarded with credits or access to premium content.

Community Contributions: Some websites or communities value user feedback. Through using the platform, rating documents or providing answers, you can sometimes earn free access to premium content.

Now, I’d love to hear your thoughts. Here’s what I’m curious about:

• How can I access Chegg for free using Reddit?
• What is the best method to unlock Chegg answers in 2024?
• Best Chegg downloader or Homeworkify alternative?
• Best way to view Chegg solutions free?

I’d really appreciate your advice and experiences. Your advice will be super helpful for me and other students trying to find good ways to access study resources for free in 2024.

Hello All!! Are you curious about how AI and machine learning are transforming the world? Whether you're a beginner or looking to solidify your foundation,

We’ve got you covered! We are Biomed Bros, aiming to bring innovation in education. We teach AI in a simplified and conceptual manner.

Introducing '3 hour DL Masterclass', a 3-part video series breaking down the fundamentals of Deep Learning-no prior experience needed!

Video 1- A Masterclass on Fundamentals of Deep Learning

This video covers on the introduction to deep learning, the various tasks in DL, the hype behind DL and the practicality, the fundamental working of a neuron, construction of a neural network with their types.

Link- https://www.youtube.com/watch?v=0FFhMcu9u3o

Video 2- Easy 5-Step Guide to Backpropagation, Heart of Neural Nets

This video is the second part of Sairam Adithya's 'Deep Learning Masterclass.' It covers the five-step working principle of backpropagation, which is considered the heart of DL algorithms. It also covers some of the challenges in implementing deep learning.

Link- https://www.youtube.com/watch?v=EwE2m4rsvik

Video 3- All About CNN- The wizard of Image AI

This video covers on the fundamentals of convolution operation and the convolutional neural network, which is the forefather of Image DL. Some potential solutions to the challenges in implementing deep learning are covered in this video.

Link- https://www.youtube.com/watch?v=ljV_nEq5S7A

Don’t miss out! Deep learning is shaping the future of technology, and it all starts with understanding the basics. Ready to dive in?

Hey guys, I’m currently exploring research ideas in the field of NLP and LLMs, and I’d love to hear your suggestions for any interesting topics...

A few days ago, I created a repo adding initial ComfyUI support for TPUs/XLA devices, now you can use all of your devices within ComfyUI. Even though ComfyUI doesn't officially support using multiple devices. With this now you can! I haven't tested on GPUs, but Pytorch XLA should support it out of the box! Please if anyone has time, I would appreciate your help!

🔗 GitHub Repo: ComfyUI-TPU
💬 Join the Discord for help, discussions, and more: Isekai Creation Community

https://github.com/radna0/ComfyUI-TPU

A few days ago, I created a repo adding initial ComfyUI support for TPUs/XLA devices, now you can use all of your devices within ComfyUI. Even though ComfyUI doesn't officially support using multiple devices. With this now you can! I haven't tested on GPUs, but Pytorch XLA should support it out of the box! Please if anyone has time, I would appreciate your help!

🔗 GitHub Repo: ComfyUI-TPU
💬 Join the Discord for help, discussions, and more: Isekai Creation Community

https://github.com/radna0/ComfyUI-TPU

I have planned to use clip for searching purpose but how do I localize the image for extracting feature vector? What steps should i take? Considering I'm still ib learning phase of machine learning

I was training a model using pytorch, and when i was training it, loading the augmented images, were slower than doing backpropogation. The CPU was bottlenecking the training process, and there is no library for doing all the augmentation work on gpu, so i was thinking of making an image augmentation library which supports cuda for pytorch.

What are your thoughts?

The other day, I posted about building the cheapest API for SDXL at Isekai • Creation, a platform to make Generative AI accessible to everyone. You can join here: https://discord.com/invite/isekaicreation

What's new:

- Generate up to 256 images with SDXL at 512x512, or up to 64 images at 1024x1024.

- Use any model you like, support all models on huggingface.

- Stealth mode if you need to generate images privately

Right now, it’s completely free for anyone to use while we’re growing the platform and adding features.

The goal is simple: empower creators, researchers, and hobbyists to experiment, learn, and create without breaking the bank. Whether you’re into AI, animation, or just curious, join the journey. Let’s build something amazing together! Whatever you need, I believe there will be something for you!

https://discord.com/invite/isekaicreation

Hey Reddit, I’m Ryan! I’m working on DataScienceHive.com, a free platform for anyone who’s into data science, analytics, or engineering—or even just curious about it. My goal is to create structured learning paths using 100% free content and build a community where people can learn, collaborate, and work on real-world projects together.

The site is still in its early stages (I’m teaching myself web development along the way), so it’s not perfect yet. But we’ve already got an awesome and growing Discord community with 15+ active members who are sharing ideas, brainstorming learning paths, and shaping what this platform will become.

Here’s what I’m trying to build:

-A place to explore free, structured learning paths with curated open resources.

-Opportunities to work on real-world projects to apply what you’ve learned.

-A welcoming and collaborative community where beginners and pros can grow together.

I’d love your help to bring this vision to life. Whether you want to help test the site, share ideas, curate content for learning paths, or just hang out and chat, there’s a place for you here.

Jump into the Discord and join the conversation: https://discord.gg/NTr3jVZj

Whether you’re here to learn, teach, or connect, you’re invited. Let’s build something amazing together and make data science education accessible for everyone!

Hey everyone!
I just got accepted into a master's program in AI (Coursework), and also a bit nervous. I'm currently working as an app developer, but I want to prepare myself for the math side of things before I start.

Math has never been my strong suit (I’ve always been pretty average at it), and looking at the math for linear algebra reminds me of high school math, but I’m sure it’s more complex than that. I’m kind of nervous about what’s coming, and I really want to prepare so I’m not overwhelmed when my program starts.

I still remember when I tried to join a lab for AI in robotics. They told me I just needed "basic kinematics" to prepare—and then handed me problems on robotic hand kinematics! It was such a shock, and I don’t want to go through that again when I start my Master’s.

I know they’ll cover the foundations in the first semester, but I really want to be prepared ahead of time. Does anyone know of good websites or resources where I can practice linear algebra, statistics, and probability for machine learning? Ideally, something with key answers or explanations so I can learn effectively without feeling lost.

Does anyone have recommendations for sites, tools, or strategies that could help me prepare? Thanks in advance! 🙏

In sentencepiece, should i pass the text as it is , or is it okay if i split the text on basis of whitespaces and then train sentencepiece tokenizer?
for eg i love ml
----->['i','love','ml']
------> and pass this token to train sentencepiece?

Hi, I noticed that the noisy image (doesn't matter what the source is) doesn't look like it portrayed in the papers. In my case, a noisy image at step 100 have this diamond like colorful texture, where in the papers it looks like a noisy random colorful grid of pixels with no texture.

I am working in the VAE latent space like most models, and the picture of the 100th noisy step is after VAE decoding to see the visual results.

Is that a normal behavior ? Why it's portrayed differently ?

https://ibb.co/Fgzymfy

For context, I am a Bachelor student in Renewable Energy (basically electrical engineering) and I'm writing my graduation thesis on the use of AI in Renewables. This was an ambitious choice as I have no background in any programming language or statistics/data analysis.

Long story short, I messed around with ChatGPT and built a somewhat functioning LSTM model that does day-ahead forecasting of solar power generation. It's got some temporal features, and the sequence length is set to 168 hours. I managed to train the model and the evaluation says I've got a test loss of "0.000572" and test MAE of "0.008643". I'm yet to interpret what this says about the accuracy of my model but I figured that the best way to know quickly is to produce a graph comparing the actual power generated vs the predicted power.

This is where I ran into some issues. No matter how much ChatGPT and I try to troubleshoot the code, we just can't find a way to produce this graph. I think the issue lies with descaling the predictions, but the dimensions of the predicted dataset isn't the same as the data that that was originally scaled. I should also mention that I dropped some rows from the original dataset when performing preprocessing.

If anyone here has some time and is willing to help out an absolute novice, please reach out. I understand that I'm basically asking ChatGPT and random strangers to write my code, but at this point I just need this model to work so I can graduate 🥲. Thank you all in advance.

I don't know about yall, but managing GPU resources for ML workloads in Databricks is turning into my personal hell. 

😤 I'm part of the DevOps team of an ecommerce company, and the constant balancing between not wasting money on idle GPUs and not crashing performance during spikes is driving me nuts.

Here’s the situation: 

ML workloads are unpredictable. One day, you’re coasting with low demand, GPUs sitting there doing nothing, racking up costs. 

Then BAM 💥 – the next day, the workload spikes and you’re under-provisioned, and suddenly everyone’s models are crawling because we don’t have enough resources to keep up, this BTW happened to us just in the black friday.

So what do we do? We manually adjust cluster sizes, obviously. 

But I can’t spend every hour babysitting cluster metrics and guessing when a workload spike is coming and it’s boring BTW. 

Either we’re wasting money on idle resources, or we’re scrambling to scale up and throwing performance out the window. It’s a lose-lose situation.

What blows my mind is that there’s no real automated scaling solution for GPU resources that actually works for AI workloads. 

CPU scaling is fine, but GPUs? Nope. 

You’re on your own. Predicting demand in advance with no real tools to help is like trying to guess the weather a week from now.

I’ve seen some solutions out there, but most are either too complex or don’t fully solve the problem. 

I just want something simple: automated, real-time scaling that won’t blow up our budget OR our workload timelines. 

Is that too much to ask?!

Anyone else going through the same pain? 

How are you managing this without spending 24/7 tweaking clusters? 

Would love to hear if anyone's figured out a better way (or at least if you share the struggle).

I’ve set up a server where we can share prompts, AI-generated images, and have meaningful discussions about all things AI. We’ve also got some cool deals on tools and subscriptions if you’re interested.

If that sounds like your vibe, come hang out!

Join here 👉 https://discord.gg/h2HUMpKxhn

Hi, i am trying to finetuning Craft model in EasyOCR script. I want to use it to detect handwritten words.

I notice that there is a part in a yaml config file that is: do_not_care_label: ['###', '']

Since i only want to train and use the detection, do i have to train the it with correct word label? Can i just use random words or ### for the label instead?

https://preview.redd.it/swkiz49oc63e1.jpg?width=1024&format=pjpg&auto=webp&s=c21a3f7f1523e7004e5313468aefa61217662c12

Imagine an LLM designed to mimic both the #conscious and #unconscious mind:

1. The Conscious LLM – trained with structured, task-specific data to ensure logical and accurate responses.
2. The Unconscious LLM – trained randomly on diverse, loosely structured data, activated unpredictably during predictions to influence the final output.

This dual-LLM architecture introduces an element of serendipity, much like human intuition. The conscious LLM ensures precision, while the unconscious LLM brings creativity, spontaneity, and unexpected insights. Together, they generate solutions and ideas we might never think to ask for.

Applications range from artistic innovation and scientific discovery to business strategy, uncovering hidden connections and opening new avenues for exploration. It’s a step toward AI that doesn’t just reason but also imagines.

What would you build with an AI that thinks and dreams?

.

.

#AI #LLM #MachineLearning #ArtificialIntelligence #Innovation

Can anyone suggest me a free video course , from where I can learn about neural networks and deep learning in detail . I need that for my final semester research project

I am currently working on a binary segmentation task and have developed the training and validation loops shown below. I need assistance with the following points:

1. How can I calculate the IoU for each class after every epoch and display the IoU values for Class 1 and Class 2, along with the overall mIoU score?
2. Should I save the model based on the highest mIoU score or the lowest validation loss for better performance?

Your insights and suggestions would be greatly appreciated!

# Initialize lists to store loss values
train_losses = []
val_losses = []

# Training and validation loop
for epoch in range(n_eps):
    model.train()
    train_loss = 0.0

    # Training loop
    for images, masks in tqdm(train_loader):
        images, masks = images.to(device), masks.to(device)
        
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, masks)
        loss.backward()
        optimizer.step()
        
        train_loss += loss.item()

    avg_train_loss = train_loss / len(train_loader)
    train_losses.append(avg_train_loss)
    print(f"Epoch [{epoch+1}/{n_eps}], Train Loss: {avg_train_loss:.4f}")

    model.eval()
    val_loss = 0.0

    # Validation loop
    with torch.no_grad():
        for images, masks in val_loader:
            images, masks = images.to(device), masks.to(device)
            outputs = model(images)
            val_loss += criterion(outputs, masks).item()

    avg_val_loss = val_loss / len(val_loader)
    val_losses.append(avg_val_loss)
# Initialize lists to store loss values
train_losses = []
val_losses = []

# Training and validation loop
for epoch in range(n_eps):
    model.train()
    train_loss = 0.0

    # Training loop
    for images, masks in tqdm(train_loader):
        images, masks = images.to(device), masks.to(device)
        
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, masks)
        loss.backward()
        optimizer.step()
        
        train_loss += loss.item()

    avg_train_loss = train_loss / len(train_loader)
    train_losses.append(avg_train_loss)
    print(f"Epoch [{epoch+1}/{n_eps}], Train Loss: {avg_train_loss:.4f}")

    model.eval()
    val_loss = 0.0

    # Validation loop
    with torch.no_grad():
        for images, masks in val_loader:
            images, masks = images.to(device), masks.to(device)
            outputs = model(images)
            val_loss += criterion(outputs, masks).item()

    avg_val_loss = val_loss / len(val_loader)
    val_losses.append(avg_val_loss)
    print(f"Epoch [{epoch+1}/{n_eps}], Val Loss: {avg_val_loss:.4f}")

Hey everyone, I believe it is possible to add multiple layers as validation layers before the output layer of an LLM - like an additional CNN/LSTM/self nn. My question is what should I learn for this? I need a starting point. I know pytorch so that's not an issue. So the basic idea is the tokens with probability go through additional layers and then if needed they go back to the generation layers before it goes to the output layer. I have seen an instance of BERT being merged with a self nn which is probably the closest to an LLM. With multimodal I'm guessing that the additional layers are mostly preprocessing layers and not post generation layers.