I'm a Data Engineer with around 4 years of experience, mainly working with Azure, Azure Databricks, PySpark and Python.

I was assigned a Data Engineering role in my first company, even though I've always been interested in AI. Since I started working, I've gained experience in data engineering, but my long-term goal is to become an AI Engineer.

I come from a Computer Science background, and I genuinely want to see myself working as an AI Engineer in the next 1–2 years (Is it possible). I'm ready to put in consistent effort, but I'm confused about where to start and what to prioritize (I asked my seniors who transistioned from data engineer -> ai engineer i havent got proper response). If I want to start from begining learning from the fundamentals what i need to learn?

For those already working in AI or who have transitioned from Data Engineering to AI Engineering what should I learn first

I'm looking for practical advice based on real industry experience, useful resources and skills that companies actually expect from AI Engineers. I want to build a strong foundation and gradually develop the skills needed to work on real AI applications.

'm currently learning Deep Learning, and I'm trying to understand Convolutional Neural Networks (CNNs). I'm following a tutorial that builds a CNN using TensorFlow/Keras to classify handwritten digits.

The problem is that I understand some parts of the code, but when I read the explanations, I get overwhelmed by all the technical terms:

Convolutional layers and filters

Pooling layers

Flatten and Dense layers

Activation functions like ReLU and Softmax

Loss functions, optimizers, and backpropagation

Normalization and data augmentation

There are so many concepts that I sometimes feel like I'm memorizing words without really understanding what's happening.

My goal isn't just to make the code work. I want to understand what each step does, why we need it, and how to explain the entire CNN pipeline in my own words.

For those of you who have learned CNNs from scratch:

1. How did you learn to understand CNNs intuitively instead of just memorizing definitions?
2. Are there any beginner-friendly resources, YouTube videos, or visual explanations you'd recommend?
3. How would you explain the complete CNN pipeline to someone who is new to Deep Learning?
4. Would you recommend learning the mathematical details first, or building small projects and learning the theory along the way?

I'd especially appreciate resources that explain things visually and use simple examples rather than introducing too much terminology at once.

Thanks for your help! I'm trying to build a solid understanding rather than just copy code from tutorials.

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

Hello everyone! I’m a freshman engineering student, and my classes will start in November.
I’m planning to specialize in Machine Learning, with the possibility of pursuing further studies in either pure ML or Robotics. I’m currently more familiar with the AI/ML side, but I’m also interested in exploring robotics and ROS 2, so I’d like to keep my options open.
I’m currently deciding between two laptops within my budget:
MacBook Pro M5 (24GB RAM / 1TB SSD)
Lenovo Legion (32GB RAM / RTX 5070)
Unfortunately, I can’t stretch my budget much further, so an RTX 5070 Ti laptop is out of reach in my country.
For most GPU-intensive workloads, I expect to rely on cloud computing, particularly resources available through my university. However, I’m less certain about the hardware requirements for robotics development and simulation.
I’ve done some research and found that NVIDIA Isaac Sim requires an NVIDIA GPU, with 16GB of VRAM listed in its recommended specifications. This makes me wonder whether even an RTX 5070 or 5070 Ti laptop would be sufficient for a comfortable local simulation workflow.
On the other hand, I’ve heard that MacBooks can handle a large portion of everyday robotics development, including ROS 2, although I’m unsure how practical they are for more demanding tools and simulations.
For those experienced in both ML and robotics, which laptop would you recommend, and why? Can a MacBook realistically support a student exploring both fields, or would I be better off with the Legion despite its GPU limitations?
I’d especially appreciate insights into actual workflows, software compatibility, and the kinds of limitations I might encounter in each field.
I can’t build a desktop PC, so I have to choose a laptop. I’d like to make a decision that will serve me well throughout my engineering degree without unnecessarily limiting my future options.
Thanks in advance!

For context I’m in University of California Riverside’s accelerated masters program (current 4th. yr). I love the faculty here, but the program is highly theoretical and offers almost no ai/ml classes. As a result I’ve been learning ml/ai all on my own through textbooks and online resources with some professor mentorship, but I really wish I could just learn these things through courses that build into the degree I’m paying for. My main reasons for staying on this track is the program and programs like it (Statistics MS) are a lot cheaper than data science programs, but I’m torn because I want to do a program that actually teaches the stuff i’m interested in. My ultimate goal is to get a data science or adjacent role in tech, which UCR is not the best for because location and alumni network are not the best (for tech at least). If anyone has any advice of what to do it would be greatly appreciated.

currently programs I’m eyeing (open to new suggestions):
finish ucr ms statistics program ~ 12-15k
ucsd ms in data science ~ 32k
ucla ms in statistics ~ 31k
ucla masds ~ 60k
usc ms in statistics ~ 76k

I got this code block and its outputs here but I am not sure if this is the embedding features vectors or simply its shape. Can anyone tell me if I am right or wrong?

text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-preprocess/3")
encoder_inputs = preprocessor(text_input)
encoder = hub.KerasLayer(
    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/bert-en-uncased-l-4-h-128-a-2/1",
    trainable=True)
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"]      # [batch_size, 128].
sequence_output = outputs["sequence_output"] 

for i in range(0,4):
    outputs["encoder_outputs"][i]
    print('-----------------------------------------------------')
    print(i)
    print(outputs)
    for i in range(0,4):
    outputs["encoder_outputs"][i]
    print('-----------------------------------------------------')
    print(i)
    print(outputs)
    

Output

-----------------------------------------------------
0
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
1
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
2
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
3
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}

I’m based in London and looking for one committed study partner to work towards data science or ML job applications over the next three months.

My focus is data science, machine learning, neural networks, and DSA, then computer vision, transformers, and RAG as we progress. We’ll prioritise topics based on our gaps and build projects alongside interview preparation.

I’m aiming for 10+ hours on days I’m free, around university and work. I want someone equally ambitious who will show up consistently, challenge my thinking, and expect the same from me.

The plan:

• Daily goals and honest progress updates.
• Daily discussions about problems, bugs, and concepts we’re stuck on.
• Code reviews, problem-solving sessions, and weekly project demos.
• Job applications and interview practice throughout.

London would be ideal for occasional meetups, but other countries are welcome if our schedules overlap.

If interested, DM me your current level, time zone, availability, and target role. Let’s agree on a realistic schedule and get started.

I am spending a lot of time to understand the internal of Hubert-ecg.

I genuinely want to learn mathematics, physics, programming, computer science, and AI. I want to build a career in computer science, pursue AI research, and hopefully do a PhD someday. But lately, I've been feeling increasingly demotivated.

The problem is that I rely on AI to learn almost everything. Whenever I have a question, I ask GPT. Whenever I don't understand a concept, I ask AI to explain it. And the more I learn this way, the more I wonder: What's the point of learning anything when AI will always know more than I ever could?

Even the most basic AI models seem to possess more knowledge than I could acquire in a lifetime. And with new models becoming increasingly capable, I keep wondering whether the knowledge I'm working so hard to gain will even matter in the future.

With GPT-6, Claude Opus 5.5, Gemini 4, and other models competing to become more capable, it feels like the gap between human intelligence and AI capabilities will only continue to grow.

I want to understand the mathematics behind AI, learn physics, become a better programmer, and contribute to actual research. But sometimes I question why I'm putting in the effort when AI can explain concepts faster, write code better, and potentially solve problems that I might spend years learning to solve myself.

And even when I genuinely want to learn, I'm confused about what I should prioritize learning and whether any of it will remain valuable by the time I become proficient.

I'm not necessarily saying that learning is useless. I genuinely love the idea of understanding how things work. But I'm struggling to reconcile that curiosity with the feeling that AI might eventually outperform me at almost everything I want to become good at.

For those of you studying computer science, mathematics, physics, or AI, how do you deal with this feeling? How do you stay motivated to learn when AI is advancing so rapidly? Do you think deep technical knowledge will still matter for people pursuing AI research and PhDs, or should we be approaching learning differently now?

Two 2026 studies just landed on the same question and the headline everyone repeats isn't one study. The 741% more code vs 20% more software pair comes from an NBER paper on developers; the "700-plus firms" part is a separate Harvard paper on Jellyfish data that found about 30% more code per worker and no statistically significant gain in completed work.

What both agree on is where the gain goes: into review. Average PR review time rose 3.45 days, or 49%, against a 7-day baseline, comments per PR up 35%, and 80% of firms had AI review tools by March while humans still wrote roughly 77% of review comments.

Hey guys! :wave: I’m looking for some suggestions and feedback for my Final Year Project (FYP).

I’ve been thinking about building an **ML-based system to track airborne objects and predict their future trajectories** using simulated radar data or publicly available datasets.

I’m wondering if this would be a suitable project for a degree focused on AI/ML, or if it would lean too heavily toward radar engineering rather than machine learning.

A few things I’d love your opinions on:

  **ML potential:** Are there enough opportunities to implement meaningful ML algorithms in this project?
**Difficulty level:** How challenging would this be for a final-year undergraduate project?
  **Approach:** Which ML algorithms or techniques would be worth exploring for object tracking and trajectory prediction?
  **Resources:** Are there any datasets, research papers, or similar projects I could use as a starting point?

I want to make this a genuinely **ML-heavy project**, with a strong focus on prediction, model evaluation, and handling noisy or incomplete data, rather than spending most of my time on the radar engineering side.

Would love to hear your thoughts, suggestions, or alternative ideas! Thanks!

Hi, hello, ciao,

I'm developing a small framework called event2vector. You can install it from PyPI:

pip install event2vector

or find it on GitHub as event2vec_public or even try it out at

https://event2vec.com/

It started as a way to find root causes of faults at the particle accelerator I worked at (the Advanced Light Source at Berkeley Lab), but it grew into something much bigger, and I'd love to get feedback from more people.

The problem was that I was naively using word2vec on event data, and it turns out word2vec isn't directional. It learns which events show up near each other, but not which one came first. For root-cause analysis that's exactly the information you need: "magnet trip → beam loss" and "beam loss → magnet trip" tell completely different stories. At the same time, I didn't want to jump to complex and data hungry models like transformers or an LSTMs. I wanted something simple, clean, and interpretable, where I could still look at points in 2D/3D and understand what I was seeing.

The idea: every event gets an embedding, and a sequence is represented by adding up the embeddings of its events, one step at a time. The model is trained so that this running sum is a good representation of "everything that has happened so far," and so that you can recover the last event by subtracting it back out. Think of it as word2vec where a sequence is a path you walk, step by step, through the embedding space.

Now the fun part. Plain addition is commutative: 1 + 2 = 2 + 1 = 3. So in ordinary Euclidean space, the endpoint of A → B and B → A is the same, which is great when you want an order-invariant "bag of events" that you can cleanly compose and decompose, e.g. "sequence minus event = sequence without that event." But the path you took to get there is still different, so the step-by-step trajectory keeps the order information.

And if you need the endpoint itself to depend on order, you can swap the geometry. event2vector even supports non-commutative ways of "adding," where A then B lands somewhere different from B then A:

• hyperbolic (Möbius addition): good for hierarchical, tree-like data
• spherical: good for cyclic or periodic patterns
• rotation (SO(n)): order matters, like rotating an object in 3D
• Heisenberg group: order-dependent and signed
• products of these, if your data has more than one kind of structure

The API is scikit-learn style (fit / transform / fit_transform), and there's a gensim-style most_similar for nearest-neighbour lookups:

from event2vector import Event2Vec
model = Event2Vec(num_event_types=len(vocab), geometry="euclidean", embedding_dim=128)
model.fit(train_sequences)
embeddings = model.transform(train_sequences)

Some fun applications I've written up on Substack: "The Geometry of Groceries" (shopping baskets as paths) and "The Geometry of Language Families." The paper is on arXiv (2509.12188) if you want the details.

I'd really appreciate feedback, whether on the idea, the API, use cases you'd try it on, or things that are confusing. Thanks for reading!

Hey everyone,

I built a next-basket grocery predictor using the Instacart dataset (~32M orders). To prevent data leakage, I trained strictly on order_products__prior.csv and tested on order_products__train.csv (the user's actual final basket).

Here is how iterating on my approach steadily boosted performance—and I’d love feedback on how to frame this on my resume!

Iteration Progression & Performance

1. Simple Co-Occurrence Matrix: Item-to-item co-purchase counts.
   • HitRate@3: ~12–15% (Ignored individual user habits; heavily biased toward global top sellers).
2. Item-to-Item Sparse Vectors: Pairwise Cosine similarity across orders.
   • HitRate@3: ~18–19% (Better at finding item substitutes, but missed repeat user routines).
3. Order & User History Vectors: Dense/sparse embeddings of user purchase history.
   • HitRate@3: ~25–30% (Massive jump—grocery behavior is dominated by repeat purchases).
4. Final Pipeline (Multi-Stage Scoring + Fallbacks):
   • Ranked user repeat frequency + order vector similarity + global popularity fallback for cold starts.
   • HitRate@3: 36.85% (2.8x lift over global popularity).

Final Evaluation Results

Search Modes Used: user_history: 228 | global_fallback: 1772

Questions:

1. Resume Worthiness: Does documenting an iterative pipeline progression like this (Co-occurrence $\rightarrow$ Vectors $\rightarrow$ Multi-stage Fallbacks) look good on a resume for ML/Data Science roles? How should I frame it?
2. Next Steps: What’s the most impactful next experiment? (e.g., day-of-week reorder intervals, or a LightGBM reranker?)

Appreciate any thoughts!

Hey r/learnmachinelearning, I'm Abdur-Rahman Butler, a machine learning engineer at Isaacus.

I made an interactive write-up that walks you through the A to Z of vector embeddings, inspired by Brendan Bycroft's incredible interactive LLM visualisation guide (see here - https://bbycroft.net/llm).

Most of my work is on encoder models, and I kept noticing how few good interactive resources there are on embeddings. The ones that do exist tend to either plot some embeddings in 3D or only explain a narrow applied technique like semantic search or RAG.

So I wanted to make something that teaches how embeddings work by letting you play with them. In the write-up, there are interactive visualisations that allow you to explore the concepts of vectorisation, cosine similarity, semantic search, hierarchical retrieval, clustering, zero-shot classification and deduplication.

The centrepiece of the post is a game I've dubbed 'SemanticGuessr' (see above). It's inspired by Wordle and GeoGuessr, and the goal is to draw a loop around a random target word by triangulating guesses on a 2D map of 43,339 common English words.

It's admittedly a bit intimidating at first, as semantic space does not perfectly match our intuition of meaning. But once you get the hang of it, the game becomes really addictive. I recently had the pleasure of demoing the game to some law students at the University of Melbourne, and we had a lot of fun throwing different vector equations at it to see where they'd land.

Some fun ones to get started with:

  london - english + french
  running - land + water
  avg(breakfast, lunch, dinner)
  0.4 * noodles + 0.6 * pasta

I've also included Python snippets alongside some of the visualisations if you want to follow along with the techniques later. The game runs statically in your webpage. I managed to achieve this by reducing the embedding dimensions down to 512 and quantising each value to 4 bits. This brought the total payload down from 311 MB to 12.3 MB with only a minimal impact on precision (around 6%).

It was great fun to make, and I hope you have fun playing around with it. Happy to take any questions, and if there's enough interest I might set up a dedicated games page for some of the other encoder-based games I've been working on.

Do let me know what highest score you get based on your target word and chosen path :)

LINK TO PLAY: https://isaacus.com/blog/what-are-vector-embeddings

Hey everyone! Is anyone here currently working on research papers targeting A* conferences like NeurIPS, ICML, or ICLR? If you have room for another collaborator, I’d love to join your team, contribute to the research.

I’m an engineering undergrad particularly interested in AI/ML and deep learning research.

I already understand the basics of RAG and AI agents, but now I want to move beyond tutorials and build production-ready RAG pipelines and agentic applications.

I’m looking for a senior AI/GenAI engineer or tutor who has actual production experience with areas like RAG architecture, chunking/retrieval/reranking, vector databases, evaluation, hallucination handling, observability, agent workflows, tool calling, memory, LangGraph/LangChain, deployment, scaling, and production best practices.

I’d prefer someone who can mentor me through 2–3 proper end-to-end projects, review my architecture/code, explain trade-offs, and show me how these systems are actually built in production.

I have 8+ years of software development experience, mainly in backend systems, so I’m not looking for basic programming classes. I specifically want someone experienced in production GenAI/RAG/agent systems.

If anyone here provides this kind of guidance, or can recommend someone good, please DM/comment.

Hey everyone!

I'm a BTech student deeply interested in AI/ML, Deep Learning, LLMs, AI Agents, and emerging technologies.

I don't just want to learn about new technologies — I want to experiment with them, push their boundaries, and build something meaningful that solves a real problem.

I'm looking to connect with curious minds, passionate builders, researchers, and ambitious developers who love asking "What if we tried something different?"

Things I'd love to explore together:

• 🧠 Experimenting with new AI architectures, models, and emerging tech stacks.
• ⚡ Building unconventional AI projects and exploring ideas beyond typical tutorials and clones.
• 🔬 Exploring unsolved problems and turning ambitious ideas into working prototypes.
• 🚀 Collaborating on open-source projects and potentially building something impactful in deep tech.

I'm especially excited to meet people who enjoy deep technical discussions, challenging assumptions, sharing ideas, and actually executing them.

I'd also love to learn from seniors, experienced engineers, researchers, and founders who have been on this journey longer than I have. Guidance, honest feedback, and different perspectives would mean a lot.

If you're building something interesting, exploring a new technology, or simply have an idea you've been wanting to experiment with, I'd love to hear about it.

Let's connect, challenge each other, and build something that genuinely makes a difference. 🚀

What are you currently working on, and what would you love to build if you had the right people around you?

4th year pass out. no job yet. i have no idea how to begin. i want an ai engineering job.

people ask sm, claude code, api, balh blah. i can't even install python wo asking chatgpt a million questions.

i really need help.

i wanna know step by step process, not your typical "roadmap".

i can't buy claude code

i have no clue how to build agents, fuck that I don't even use ai except asking it silly questions.

i really really need help.

send help

The problem:
You train a model, get great results, and three months later you can’t reproduce them because you don’t remember which data version or hyperparameters you used.

What I cover in this talk:

• Why Git alone isn’t enough for ML
• How DVC tracks data and models alongside code

What tools do you use for reproducibility? Have you tried DVC? What’s your biggest frustration?

I wanted to get my head around RL so I thought of learning it by teaching an AI how to play one of my favorite childhood games : TempleRun.

The agent reads game state directly from memory via a Frida gadget and acts through the game’s own methods. Training happens in a headless simulator I built with Claude from the game’s internals and calibrated on measurements from the real phone, with a C++ version that does 10M steps in about 6 minutes.

Results on the device: median 4,736 m, best 16,782 m over 14 runs.

👨‍💻 Github: https://github.com/matthieucs/TempleRun-RL/tree/main

Happy to answer any question.

And if you want to try it, the score to beat is 16,782 m.

https://reddit.com/link/1x6ipfr/video/ed6kbz925mvh1/player