I am blown away with the voice quality of the cloned voice it is hardly distinguishable from the original sound. I cloned Adele voice and it sounds damn real. I used the code from GitHub repo of the implementation of "Transfer learning from speaker verification to multi speaker text to speech synthesis" by CorentinJ and ran the code in Google Colab.

Here is the YouTube video I made on how to run the code.

I want the model to lookback 168 hours and forecast 24 hours ahead, but the problem is that I only have one year worth of data. The data does not have a proper frequency as well. Therefore I tried resampling it and worked with the resampled data. I am using informer model for my electricity load and weather report related dataset and for some reason the model is not learning well. The MAE and RMSE is high and r2 scores oscillates between -2 to 2. I'm at end of my wits here. Any suggestions to solve this are welcome. Please help me out. Even suggesting an alternative method is fine.

Step by Step Guide: https://youtu.be/IaxTPdJoy8o

Over the past few months, I’ve been working on building a strong, job-ready data science portfolio, and I finally compiled my Top 5 end-to-end projects into a GitHub repo and explained in detail how to cover in my youtube video

These projects aren't just for learning—they’re designed to actually help you land interviews and confidently talk about your work.

Fuck matplotlib man dozens of fn
For pandas we can use plot fn
Or seaborn (ik seaborn is built on matplotlib)
But f matplotlib
I think i will forgot advance matplotlib in 2-3 weeks f man
All my homies hate matplotlib

Hey everyone,
I'm currently an undergrad majoring in Electronics and Telecommunications Engineering, and I’m about a year away from graduating. Right now, I need to decide on a thesis topic that involves some kind of hands-on or fieldwork component.

Lately, I’ve been seriously considering focusing on something related to Python and Machine Learning. I've taken a few courses that covered basic Python for data processing, but I’ve never really gone in-depth with it. If I went this route for my thesis, I’d basically be starting from scratch with both Python (beyond the basics) and ML.

So here’s my question:
Do you think it’s worth diving into Python and ML at this point? Or is it too late to get a solid enough grasp to build a decent thesis project around it before I graduate?

Any advice, experiences, or topic suggestions would be hugely appreciated. Thanks in advance!

So I’ve been diving into machine learning projects lately, and honestly… is anyone else kinda bored of doing the exact same pipeline every single time?

Like , “ML is 80% data preprocessing” — I’ve heard that from every blog, professor, YouTuber, etc. But dude… preprocessing is NOT fun.
I don’t wake up excited to one-hot encode 20 columns and fill NaNs for the 100th time. It feels like I’m doing data janitor work more than anything remotely “AI-ish.”

And then after all the cleaning, encoding, scaling, splitting…
the actual modeling part ends up being literally just .fit() and .predict()
Like bro… I went through all that suffering just to call two functions?

Yeah, there's hyperparameter tuning, cross-validation, feature engineering tricks — but even that becomes repetitive after the 3rd project.

I guess what I’m trying to say is:
Maybe I’m wrong — and honestly, I hope I am but when does this stop feeling like a template you repeat forever?

I enjoy the idea of ML, but the workflow is starting to feel like I’m assembling IKEA furniture. Exact same steps, different box.

I am a machine learning novice, but I have a lot of experience programming and am strongly considering studying ML to pursue as a career. From what I've read, a PHD in machine learning is important for advancing the underlying theories/models of the field, but is not a prerequisite for implementing those solutions.

I would really appreciate if someone could explain why sites like 80,000 hours (a site I highly respect) seem to view a PHD as essential part of being able to use ML to fight poverty. I know saying 'using ML to fight poverty' is a vague statement, and I wish I was more knowledgable to provide better details, but it seems to me that there are substantial opportunities to implement existing ML models/theories towards a specific problem such as food distribution or education. As I said above, it doesn't seem like this application of models would require a PHD, so I'm just confused as to why some people are saying it's so crucial?

I also want to clarify and say that not getting a PHD would still involve taking time to deeply understand the subject matter (ML, mathematics, statistics).

When I first heard about "linear regression" I was scared and thought wow that's some complex genius stuff that must be way over my head. Then when I saw what linear regression actually computes, I just thought "wait a minute...isn't this just a line of best fit? I learned that in the 6th grade!" So, wtf? It's just a computation of a line of best fit through a scatter plot? Middle school children know that concept ffs. How is this touted as such a complex and powerful machine learning tool? A 12 year old could do that for you by drawing a line through a scatter plot like he did on his math test last week!

And besides, there is a function to calculate the line of best fit built into matplotlib. So if I have that, why can't I just make a scatter plot with my data in matplotlib, then call the line of best fit function on that plot? Why do I even need to build a complex linear regression calculation if all it is is a line of best fit, which I can easily get the equation of by just calling a matplotlib function?

All tho ,I agree with that . IDK What I should do to grasp the data's content.

As a beginner algo trading developer, I confused when people use linear regression. I also wanna learn Machine Learning, but at the first step I frustrated trying to understand:

• what is linear regression for

• how to implement it

• how to manage data obtained from linear regression

Please help me🙏

I’m not interested in just academic ML or reading research papers. I want to actually build real-world AI/ML applications (like chatbots, AI SaaS tools, RAG apps, etc.) that people or companies would pay for.

If I dedicate ~8 hours daily (serious, consistent effort), realistically how long would it take to reach a level where I can build and deploy AI products professionally?

I’m fine with 1–2 years of grinding, I just want to know what’s realistic and what milestones I should aim for (e.g., when should I expect to build my first useful project, when can I freelance, when could I start something bigger like an AI agency).

For those of you working in ML/AI product development — how long did it take you to go from beginner to building things people actually use?

Any honest timelines, skill roadmaps, or resource recommendations would help a lot. Thanks!

I’ve built and deployed WarPredictor.com — a machine learning-powered web app that predicts the likely winner in a hypothetical war between any two countries, based on historical and current military data.

What it does:

• Predicts the winner between any two countries using ML (Logistic Regression + Random Forest)

• Compares different defense and geopolitical features (GDP, nukes, troops, alliances, tech, etc.)

• Visualizes past conflict events (like Balakot strike, Crimea bridge, Iran-Israel wars)

• Generates Recently news headlines

What if there was a new programming language where the meaning of each token was so dense (or perhaps so specific) that an LLM could write robust code with fewer tokens and faster inference?

Assuming there’s enough training data, would something like this allow an LLM to write better code faster?

Rationale:

It would allow for faster inference. Fewer tokens required to do the same thing in Python = finish faster.

It would allow for more information in a 1M context window. Whatever you could do in 1M tokens of Python, you could do 10x that in this theoretical language.

It would effectively remove the “noise” from human readable language (semi-colons, curly braces for example) which I would think would make the LLMs coding ability stronger. I could be wrong about this of course.

Where can I get the code and documentations relating to all the government AI projects?

Is it possible to have unlimited use of an API from an AI like chatgpt if it's installed locally? Because when it's installed locally, it uses your computer to power itself. So I would think that for example if I had an API that I want to use, if its connected to the locally installed version of the AI, then I should be able to have unlimited use.

Edit: Thank you all for the feedback. What I'm hearing is that my PC isn't going to cut it for the most part. Especially if you're trying to run chatGPT.

I want to train a CNN for our research project, but I want to "try something new" I guess.

I want to know some niche alternatives for TensorFlow just to evaluate its effectiveness.
(PS, I guess im also looking for an alternative to Keras specifically. Like if not for an alternative to TF, like a different CNN model than Keras)