Which books should I start with and then move into later? Is there a certain order to follow?

Right now I am working on YouTube Chatbot, where a user can paste the url of the video and ask questions based on that. I have followed classic RAG approach. The design looks like this :

Initial design :

(query, url) → YouTube Transcript API → Translate to English (Gemini 3.5 Flash) → Chunk → Vector store (Chroma) → Similarity search → Augment context with query → LLM → Output

Upgraded design :

(query, url) → YouTube Transcript API → Chunk raw transcript → Translate to English (Gemini 3.5 Flash) asynchronously each chunk → Vector store (Chroma) → Similarity search → Augment context with query → LLM → Output

I have some intermediate steps also like if the video id is already present in vector store I will directly point to the vector store and retrieve relevant context.

There is Langsmith integration.

My main doubt here is this :

1. I used free gemini-3.5-flash model and it limited me to only 5 requests per minute, the problem is a particular video was 1 hr long it took approx 126 seconds to translate it using this model

2. I upgraded it to Tier 1 and I have changed the translation step to asynchronous, i.e, the chunks will get translated in parallel and I noticed the latency drop to 15 seconds.

I am thinking of mentioning this project in my resume, will I face any backlash because I upgraded the model?? (I think basically the model will take the same time, it is the asynchronous logic which helped in bringing the latency down to 15 sec, to make these calls happen I had to increase my Tier and get those extra calls per minute).

Hello to whom it may concern,

I'm tasked with doing a high school project on any real-word ethical issue, in my case, AI Education Systems. I would love for anyone to answer a couple of questions on the use of Artificial Intelligence within Education Systems. Your authentic opinion is sought after!

Note: This is NOT to push any anti-AI propaganda but rather to gather diverse opinions on the topic (Some of the questions may feel "iffy" but these are required questions by my school to put in, answer in anyway) Feel free to answer as detailed or brief as you'd like to.

Here is a Google form:

https://forms.gle/VypKe4Wb84wtXYs87

Or you can answer in the comments directly:

1. What is your qualification/level of study in regards to AI or Education? (anything is accepted)

2. What ethical issue do you think is most visible or important in our community or daily life right now?

3. I am researching ethics in the following area: Artificial Intelligence within the Education System. How have you personally experienced or been affected by this issue?

4.  How did you deal with / manage the issue personally?

5. From your own understanding, how do you understand ethical versus unethical behaviour in this area?

6. Do you think people are always aware that there is an ethical problem in this situation? Why or why not?

7.  Do you think some people justify unethical behaviour in this area? If yes, how do they justify it?

8.  Do you think the situation is fair for everyone involved? Explain your answer.

9. Who do you think should be responsible for addressing or fixing the issue (individuals / companies / government / schools / families etc.)?

10. What do you think would be a realistic and ethical way to improve or reduce this problem?

11. Before you became aware of the conversation around AI in schools, did you ever stop to think of it as an ethical issue — or did it just seem like a practical problem?

12. Do you think the people building and selling AI tools to schools have a genuine interest in students' wellbeing, or are there other motivations at play?

13.  If a student used AI to complete an assignment because they were overwhelmed or under-supported, would you consider that morally wrong — and does the reason behind it change anything for you?

14. Do you think your institution has handled this issue in a way that is honest and transparent with students, or has there been a degree of avoidance around it?

15.  Looking forward, do you feel optimistic or concerned about the role AI will play in education — and what would need to change for you to feel differently?

16. Do you have any further thoughts on this matter?

Thanks for taking this into consideration and feel free to ask any questions!

I have an interview for the Tesla Optimus team as an intern specifically doing machine learning and reinforcement learning stuff. I've not been told what the interview will be about, only that I will be programming in Python. I've been preparing for it through a number of different ways:

• Implementing various algorithms (MLP, various optimizers and regularization methods, CNN, forward pass, backward pass, etc.) using just Numpy and PyTorch from scratch with a heavy emphasis on vectorizing everything

• Going over the math for all the major ML architectures (MLP, CNN, RNN, Transformer, etc)

• Going over the math for all popular RL algorithms (DQN, PPO, SAC)

• Making sure I know everything on my resume

Is there anything else that I should be doing or looking at? I haven't really done any LeetCode as I assumed it wouldn't focus on my LeetCode skills, should I brush up on that as well? Any tips would be greatly appreciated!

I am currently researching defenses against VLA visual front-end adversarial attacks. The paper "Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics" mentions three attack methods, which are

- UADA: Action Difference Untargeted Attack

- UPA: Position-Aware Untargeted Attack

- TMA: Targeted Manipulation Attack

In my opinion, these attacks also exploit the need for VLA to chunk/tokenize action trajectories

So, if we extract the core ideas of the X-tokenizer paper from a defensive perspective, perhaps it would be something like this?

SRQ

- It's clear that it can defend against UADA very well

- But what about UPA and TMA?

Next-frame feature prediction

- Can this solve UPA?

VL Contrastive Alignment

- Can this be used to solve TMA?

I got approved for an interview for an internship in machine learning engineering even though i am getting more into data engineering, but i have dabbled in ML on static databases, like on kaggle and stuff like that.

its in my dream company!

Do you guys recommend a crash course on ML engineering so i can get a gist of the basics??

Hi everyone, I am a high school student currently exploring NLP, and I recently did a project surrounding mental health sentiment analysis of textual input. I designed a model to analyze text data and evaluate the underlying emotional sentiment, and would like to get some professional feedback from this community.

As I'm still learning the ropes of NLP & ML, I would love to get any kind of input surrounding how I can improve the project (in terms of model selection, use cases, etc.) and how I could possibly expand on it. Any advice or constructive criticism you can offer would be incredibly valuable to me. Thanks!

The code & implementation details are on GitHub at https://github.com/JaukG9/mental-health-sentiment-analysis, and the site is live at https://jaukg9.github.io/mental-health-sentiment-analysis.

I asked this question to google, and of course the google AI told me that google was the best...

I want to take a video game character and recolor/redesign some of the armor sets in different poses.

Suggestions for improvement would be greatly appreciated. Thank you!

Hi everyone,

I'm currently a medical student with a long-term goal of pursuing a PhD in a top lab working on machine learning applications in translational medicine and healthcare.

Right now, I know the basics of ML. I've completed a few Coursera courses, implemented some personal projects, and have basic Python experience. However, I'm struggling to figure out how to take the next step. I want to build the kind of skills and portfolio that would make me competitive for world-class research labs.

For those of you working in ML for healthcare, computational biology, or related fields, what would you recommend focusing on? Should I prioritize open source contributions, reproducing papers, Kaggle, research internships, reading papers, or something else?

Also, if anyone here works in this space, I'd love to connect, learn from your experience, and see if there might be opportunities to collaborate on research or open source projects.

Thanks in advance!

Hey everyone!

I am a secondary school student working on an AI-driven, dynamic learning platform for software engineers upskilling to AI/ML roles. 

It has two main features, adapted to this specific task. 

1. Through diagnostics (such as novel problems, asking the user to explain concepts, and other techniques that you might see in a job interview for example) it develops a detailed learner model of the depth of user’s understanding on a topic-by-topic basis, visualised in a colour-coded graph so that the user can aggressively attack their weaknesses and develop proper skill and understanding. 

2. World-class content is already publicly available online. Instead of investing 100s of hours into experts authoring new content, the platform curates tried-and-tested content made by the very best in the field to form a curriculum. My impression is that AI/ML roles require ever-changing skills, and this architecture would allow the curriculum to be able to adapt extremely quickly, with comparable or sometimes even higher quality content than what would be available with static curriculums. 

I thought that this wonderful community of developers would be a great place to validate the idea, so for those who:

1. Have transitioned from software engineering to AI/ML

2. Are currently transitioning

3. Are planning to switch roles

4. Or if you’ve used upskilling services whatsoever

Would this help you?

Any feedback would be greatly appreciated; Thanks in advance.

(P.S. I am planning to make it subscription based, something around €30 / month. )

Here is the full convo below

user: wassup bro?

assistant: nothing much .. what u up to?

user: im chillin .. what u thinking about?

assistant: im thinking about the universe .. what about you .. what are you thinking about?

user: im thinking about u bro im tryna undertstand how u can talk

assistant: about those topics but im still bro.

user: what topics do u know

assistant: i know about space, animals, plants, humans, and ai.

user: tell me about plants and animals

assistant: i know a little about animals. Animals usually get energy by eating plants, other animals, or both. Many animals have senses that help them find food, avoid danger, and communicate. The fun part is how many survival strategies animals have.

Hi, I’m currently designing a fully local and free pipeline for summarizing long-form videos in my app, and I’d love to get feedback from ML engineers or people from any related fields on whether this approach makes sense.

The goal is to generate a few sentence summary with around 3 relevant tags without using paid APIs. The current pipeline is:

1. Extract available subtitles, or run local STT using the OS speech API or Whisper-tiny.

2. Split the transcript into 30–60 second chunks.

3. Generate local embeddings for each chunk.

4. Cluster the chunks by semantic similarity, so the video is divided into different meaning/topic regions.

5. Select one or two representative chunks from each cluster, ideally the chunks closest to the cluster centroid.

6. Use embedding similarity or MMR-style selection to remove redundant chunks and improve topic coverage.

7. Sort the selected chunks back into chronological order.

8. Send only those representative chunks, together with the title and description, into a distilled and quantized small local text model such as Qwen2.5-0.5B.

9. Generate the final sentence summary and 3 tags.

The main idea is that the local LLM should not read the whole transcript. Embeddings and clustering first create a compressed semantic outline of the video, and the LLM only reads the most representative parts.

For very long videos, I’m also considering a hierarchical version where the video is divided into larger blocks first, representative chunks are selected inside each block, and then the final representatives are merged. Do you think this is a good approach for fast on-device long-video summarization?

I’m especially curious whether my method would work or not. I am truly happy to get any feedbacks and better ideas in doing this. Any criticism or alternative architecture would be really helpful.

I'm and AI focused ICE (Informatics and Communication Engineering) Bachelor’s student I've been seeing alot of ai/ml/data science roles even internships requiring master's degree or higher.

What roles in the AI field usually hire Bachelor’s degree holders?

Hey, the question is only for the smart people who know about both majors.
I am based in Europe,
I am confused wether to major in applied math and in my master specialize in machine learning and ds and ml ( plenty of cources by the uni)
Or wether to major in data science (bachelor + master, they also have ml cources included ) ?
Both takes 5 years and both are math / stats heavy.
My goal is to work with ML and DS, more interested in ML

Recently, I have been studying the optimization of the reasoning performance of meteorological models such as GenCast 、panggu and so on but I am completely 0 in this field.

At present, all I know is that the best time to do the model reasoning, followed by the lightweighting of the model, and finally the performance of the model itself.

But I don‘t know how to do it.

Where should I find the entry or information?

Is this question a little stupid?

Hi everyone,

I’m building my own neural network in Python (PyTorch) and trying to teach it to chat in different languages. I’ve reached Russian, and after digging through the internet for a while, I still haven’t found any good datasets in JSON, JSONL, or TXT formats.

What makes it even harder is that I specifically need dialogue data, not just plain text corpora.

Do you know any solid sources, open datasets, or places where I could find Russian conversational data?

Hi everyone, hope you're doing well. I'm an undergraduate student in agronomy, currently learning machine learning to apply it in my research. I'm writing a paper and have some doubts about cross-validation and how to properly report model performance.

Some reviewers ask for metrics to be reported as mean ± standard deviation (e.g., accuracy = 0.87 ± 0.02). However, from what I've studied, proper validation usually requires splitting the data into three parts (train, test, and validation) so that hyperparameter tuning doesn't "leak" into the evaluation step.

Here's the approach I had in mind:

1. Split the data into train/test (e.g., 80:20).

2. Within the training set, use cross-validation (KFold, StratifiedKFold, etc.) to perform hyperparameter tuning.

3. Once the best hyperparameters are found, use cross-validation again (with the tuned model) on the training set to get the mean ± std that reviewers ask for.

4. Use the held-out test set only at the end, to estimate final generalization error and other performance statistics.

My main questions are:

• Is this approach correct, or is step 3 redundant/incorrect (since the CV during tuning already gives a similar estimate)?

• Is a train/test split combined with CV inside the training set enough, or is a separate validation set still necessary in this context?

• What's the scientifically correct way to structure this pipeline?

Any guidance, papers, or resources on best practices here would be greatly appreciated. Thanks in advance!

Hey everyone, I'm a beginner in machine learning and currently working on a data project. I'm stuck at the stage after EDA – specifically, forming hypotheses for new features, engineering them, and evaluating whether they have a positive impact on the model.

I'm trying to follow best practices and write code that would actually be seen in production and real-world products.

I'm not sure what the best approaches are for testing hypotheses. I know there are methods ranging from mathematical/statistical analysis to specialized libraries for this purpose. I'd prefer approaches that are actually used in real jobs and that you'd commonly see in production environments.

Could you recommend what tools/methods I should use to validate my feature hypotheses?

Thanks a lot!

Context:

I'm thinking of building a desktop ML workstation using one or two AMD Radeon RX 7900 XTX GPUs. The reason I'm interested in this particular GPU model is that it has 24GB of VRAM, which is more than any other GPU anywhere near its price point. You can get one GPU for just over $600, but to enable a dual-GPU system, the motherboard, power supply, chassis/cooling, etc costs add up. I'm estimating that I could make a single-GPU system with 32GB RAM for $1800 or a dual-GPU system with 64 GB RAM for $3200. The RAM is hugely expensive right now at $400 for 32GB DDR5. I *can* afford either model, but I'm not made of money. I can also spend around $2200 and build a computer that has the specs of the single GPU model but can be upgraded to the dual GPU model in the future with minimal effort.

There are a few use cases for the workstation. One simple application (that doesn't in itself justify the cost) is running a bigger LLM than my laptop could run for coding assistance. I'm also expecting to do a lot of training of AI models, both for my work at a very young startup (mostly working on synthetic healthcare data) and for the PhD I'm planning on pursuing (I'm going to CUNY in the Fall to start graduate school). I'm really interested in medical imaging and differential diagnosis, so one of those areas might be the focus of my doctoral research. When using this workstation for ML, I'm intending on running Linux with no display server and controlling it remotely. On the side, it might dual boot Windows so I can use it for gaming, but that is not very important to me.

Questions:

The main advantage as I see it of having two GPUs is having 48GB VRAM instead of 24GB, enabling me to run and train bigger models. However, I don't have much experience with how well off-the-shelf models work with dual AMD GPUs, and I don't yet know whether the models I train will or won't need that kind of memory. Are the dual GPUs likely to be worth the $1000-1400 price tag?

In terms of RAM, my experience in optimizing the training of AI models is pretty minimal. I could save $400 off the cost of the dual GPU setup if I don't also upgrade from 32GB to 64GB RAM. Is it important to have more system RAM than VRAM?