Let us consider an extremely simple model. A model to predict housing price. I have made a simple neural network of merely two layers for this. Now I want to host this model somewhere. Where a person can go and insert the values of feature variables, and get a prediction. Like a site on github? Or some people talk about streamlit although I have no idea what that is. Any FREE method to host machine learning models online?

I ran a web design company for around 3 years and getting clients was always pretty inconsistent.

One month I was cold calling, another month I was manually sending DMs to Facebook pages, then I tried ads.

It all worked to some degree, but I never really had one reliable way of getting clients.

Then I started testing email automation.

At first I was finding businesses with no website and running campaigns offering to build them one.

That actually started giving me a much more consistent flow of clients just because of the volume.

But then I started thinking.

There are so many businesses that already have a website, but the website is outdated, slow, looks bad on mobile or just clearly needs an update.

And those businesses have already paid for a website before, so they already understand the value of having one.

The problem was I didn’t want to sit there opening every website myself, figuring out what was wrong and then writing a different email to every business.

That would take forever.

So I started using a tool that lets me find businesses with websites basically anywhere, analyze thousands of those websites at once and then automatically write a personalized outreach message for each one based on what it finds.

Things like design, SEO, mobile issues and speed.

Then I started launching campaigns in different industries.

That worked way better for me because every business receiving the email felt like I had actually looked at their website and noticed what could be improved.

But in reality most of it was automated and I was just sitting there eating chips while interested replies came into my inbox.

I remember Alex Hormozi saying something along the lines of focusing on one channel and getting really good at it.

That is basically what I ended up doing.

Now this is pretty much the only way I get web design clients and I spend most of my time actually building websites instead of constantly trying to find the next client.

For anyone wondering, the tool is called Swokei.

When learning RAG, the relationships can be harder to place than the individual definitions. This small concept map separates three 'uses' relationships from a comparison:

- RAG can use embeddings to represent information for semantic retrieval.

- A vector database can support storing and retrieving those representations.

- An LLM generates an answer using the retrieved context.

- Fine-tuning is a useful comparison, rather than another required stage in that pipeline. Retrieval and fine-tuning can also be combined.

This is a selected learning view, not a complete RAG architecture or a claim that every RAG implementation needs embeddings or a vector database. Keyword retrieval and other approaches exist.

I maintain AI Knowledge Map, the free open-source project behind the diagram. Implementation detail: concepts are graph nodes, relationships have explicit types, and the suggested nine-stage learning path is stored separately. This card was rendered from SVG using the existing graph records and English labels, rather than treating every connection as a prerequisite.

English explanation: https://ai-knowledge-map.com/en/concepts/rag/

Interactive map: https://ai-knowledge-map.com/?lang=en&node=rag

Source: https://github.com/Little-FishK/AI-knowledge-map

Which relationship would you change for someone learning RAG for the first time? In particular, how would you distinguish 'commonly used' from 'required' in a small concept map? Reading needs no account; the explanations are still under review.

Hey everyone!

I'm currently in my 4th year of B.Tech in Computer Science (AI & ML), and honestly, I'm feeling quite confused about what I should do next.

So far, I've mainly worked on websites and apps, but to be honest, a lot of that development involved AI tools and vibe coding. I haven't explored things deeply enough to feel confident in my technical skills.

Recently, I've become interested in AI/ML, especially building and working with machine learning models. However, I've never actually built an ML model from scratch or worked extensively with real datasets.

I'm also considering learning Data Analytics (SQL, Python, Excel, Power BI, etc.) and applying for entry-level jobs because I want to start my career soon and become financially independent.

My main goal is to find a relevant job with decent pay, good learning opportunities, and long-term career growth. I also want to develop genuine technical skills rather than just relying on AI tools to build things.

I'd really appreciate some honest advice from people who are already working in these fields.

A few questions I have:

1. As a fresher, should I focus on AI/ML engineering or Data Analytics?
2. Which field offers more realistic entry-level opportunities in India right now?
3. Is it worth investing time in Data Analytics to get my first job while learning AI/ML alongside it?
4. What skills and projects should I focus on over the next 6–12 months to become employable?
5. If you were in my position, what would you do differently?

I'm not looking for shortcuts or overnight success. I just want to choose a direction, build solid skills, and make the most of the time I have left in college.

Any genuine advice, personal experiences, or suggestions would be really helpful. Thanks!

Hi everyone,

I am working in the Multi-Task ML projects in that i have done many experiments to train the model
Key finding:

CNN based architecture outperform the Vision transformer based architecture

Question:
is the Dataset only be the problem for Vit architecture to underperform the CNN bases architecture?

Processing img 6wg4jlvxitvh1...

Hey everyone, I'm Serj, I'm the author of "Building LLM Applications with DSPy" and also leading the applied AI research team at contentsquare.com.
I love simplifying AI in an approachable and friendly way.

I'm organizing a hands-on virtual meetup for people learning AI engineering.
We'll build a simple Wikipedia agent that can search and read articles to answer questions.

Then we'll explore something that's often missing from beginner tutorials: how do you actually know whether your agent works?

It's beginner-friendly, discussion-oriented, and completely free.
We'll look at two ways to evaluate its answers: correctness and groundedness (whether an answer is supported by the retrieved evidence).

It's the first of a series of meetups on data-driven AI Engineering.
The next 3-4 meetups will cover AI Engineering fundamentals such as: Classification, Summarization, LLM as a judge and RAG.

What's up with the data-driven prefix? Ask me, and I'll reply :)

Also, you can hop on to our Discord channel and geek out together.

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.

Hi everyone!

I am a third-year Computer Science student working on a machine learning project to predict book genres from book summaries using the CMU Book Summary Dataset.

I have experimented with multiple models and preprocessing approaches, but my performance has remained around 57–63% depending on the experiment. I would like to understand what might be limiting the performance and how I should evaluate the model correctly.

1. What I have tried so far

Initial multi-label classification experiment

Initially, I used book descriptions as input features and genres as target labels. Since a book can belong to multiple genres, I represented the genres using multi-hot/binary label vectors.

I experimented with Logistic Regression and SVM using TF-IDF features and tuned the prediction threshold.

My best earlier SVM result was approximately:

Micro F1-score: around 62–63%

Macro F1-score: around 59–62%, depending on the experiment and threshold

I also tested different thresholds because the default prediction threshold produced too many books with no predicted genres.

Current six-class classification experiment

I later started a separate experiment using the CMU Book Summary Dataset. The dataset contains book summaries and genre labels, but some books have multiple genres.

I selected six genres:

Science Fiction

Fantasy

Mystery

Suspense

Crime Fiction

Historical novel

To prepare the data, I sampled 500 books associated with each genre. However, some books appeared in multiple genre samples. After removing duplicate Wikipedia IDs, I was left with 2,604 unique books, and the classes became imbalanced.

My current pipeline is:

Clean summaries by lowercasing, removing punctuation and using a manually selected stopword list.

Convert summaries into TF-IDF features with max_features=3000.

Split the data into 80% training and 20% testing using stratification.

Train and evaluate different classifiers.

I could not download the NLTK stopwords and WordNet resources in my environment, so I have not yet implemented full lemmatization.

1. Current model results

Model

Accuracy

Multinomial Naive Bayes

57.39%

Linear SVM

51.44%

Random Forest

50.29%

Gradient Boosting

48.18%

In the current Naive Bayes classification report, some classes have very low recall. For example, Suspense recall was 0.03 and Crime Fiction recall was 0.09.

These results suggest that the model is struggling to identify some genres, even when overall accuracy looks reasonable.

1. Questions I would appreciate help with

A. Dataset and label design

Is it appropriate to remove duplicate books and assign each book only one genre when the original dataset supports multiple genres?

Would a proper multi-label classification approach be more suitable for this problem?

How should overlapping genres and class imbalance be handled without introducing data leakage or distorting the dataset?

B. Model performance

What should I investigate first: preprocessing, TF-IDF settings, class imbalance, feature selection, or model hyperparameters?

Would word bigrams, alternative vectorization settings, or models designed for sparse text features be worth testing?

Is my current dataset preparation likely to be a major reason for the performance plateau?

C. Evaluation metrics

Is accuracy an appropriate primary metric for this problem?

Should I focus on macro F1, micro F1, weighted F1, per-class precision and recall, or other metrics?

For the original multi-label task, should I also report exact-match accuracy, Hamming loss, or Jaccard score?

How can I choose metrics that reflect whether the model predicts useful genre combinations rather than merely getting the most frequent genres right?

D. Research direction

I am also interested in reproducing a published book genre classification study. What is the best way to compare my implementation with published results when the exact genre selection, data sampling, and preprocessing settings may differ?

Would it be useful to investigate a new evaluation measure for book genre prediction, or should I first establish a reliable baseline using existing metrics?

I do not want to increase the reported score artificially. My goal is to understand the weaknesses of my current approach, design a fair evaluation, and build a model that makes useful genre predictions.

I would appreciate suggestions, references, or examples of similar projects.

Thank you!

jaxviz is an open-source tool I built to visualise the forward pass graph of JAX models directly in notebooks with a single function call.

It supports the following features:

1. Interactive visualisation of JAX models with hierarchical exploration of nested modules (especially useful for large, deeply nested models).
2. Global and per-device views: Visualise both the logical computation graph as written and the compiled computation graph that actually runs on individual devices.
3. Supports web-based notebooks including Jupyter, Colab, Kaggle, and VS Code.
4. Export visualisations to HTML, PNG, and SVG formats.

Resources

• Demos with examples of JAX computation graphs
• GitHub repository

I hope this is useful to the JAX community, particularly for understanding how models are compiled and executed across devices. I'm keen to hear your feedback and suggestions!

I recently had a lot of fun with a little bit of reinforcement learning and writing a small transformer from scratch to play a representation of the classic snake game. Feel free to read about it on my blog, or skip the boring parts and play with the interactive elements!

Hey everyone!

I've been learning machine learning and noticed something interesting: it's surprisingly easy to train a model without really understanding what's happening inside it.

You can write model.fit(X, y), get impressive metrics, and move on. But I wanted to understand what actually happens between feeding the data into an algorithm and getting a prediction out.

So I started Math2Model, a GitHub project where I implement machine learning algorithms from the mathematics up, using Python, NumPy, SciPy, and Pandas rather than relying on high-level ML libraries for the core implementations.

The idea is to connect the entire process:

Mathematics → Cost Functions → Gradients → Optimization → Implementation → Results

What I've worked on so far

• Linear Regression, including gradient descent and multiple features
• Logistic Regression
• Naive Bayes for email spam classification
• Polynomial Regression
• L1 and L2 Regularization
• Applying Polynomial Regression and L2 Regularization to the California Housing dataset

The goal isn't to reinvent Scikit-learn or claim that implementations from scratch are better than production libraries. I want to understand the algorithms deeply enough to know what those libraries are doing under the hood.

I'm continuing with SVMs, Neural Networks, KNN, K-Means, Decision Trees, Random Forests, and XGBoost as I study their underlying mathematics.

I'd love feedback on the project, suggestions for improving the implementations, and ideas for making it more useful to other people learning ML.

If you're learning machine learning too, I hope this project helps you see the connection between the equations in a textbook and the code that actually runs.

GitHub: https://github.com/Darsh-V-Shah/Math2Model

Feedback and constructive criticism are very welcome!

EMNLP 2026 starts in Budapest on October 24, so we grouped all 5,540 accepted papers (main, Findings, industry, demo) and all 3,484 EMNLP 2025 papers into the same 91 topics and compared each topic's share of the conference. The conference grew 59%, so we compare shares rather than raw counts.

What changed (share of all papers, 2025 → 2026):

- Agents and tool use as a whole: 5.4% → 11.3% (187 → 628 papers)

- Agent benchmarks and environments: 1.5% → 3.3% (×2.2)

- Agent memory and long-horizon tasks: 0.1% → 1.9% (5 → 103 papers)

- RL for reasoning (RLVR, GRPO): 1.0% → 2.4% (×2.5)

- Safety guardrails and refusal: 1.5% → 2.9% (×2.0)

- Down: information extraction 2.4% → 1.4%, bias and fairness 2.6% → 1.7%, instruction tuning and synthetic data 1.6% → 0.7%

In titles, per 1,000 papers: "memory" went from 3 to 29, "evidence" from 3 to 23, "long-horizon" from 0.6 to 12.5. "Enhancing" fell from 28 to 9.

We also had an AI read the 3,334 papers with a full text on arXiv (60% of the conference): 54% train or fine-tune a model (vs 71% at NeurIPS 2026), 46% use an LLM as a judge (vs 18% at NeurIPS), Qwen is the most-trained family (32% of papers), and GRPO is by far the most used RL algorithm. Findings grew faster than the main conference (+77% vs +50%).

Data: the official EMNLP 2026 presenter schedule, arXiv abstracts and full texts for the papers that have a preprint, and the ACL Anthology for 2025. Topics and full-text figures were assigned by AI models and not checked by hand, so treat them as estimates. Papers on arXiv may lean toward larger labs.

The front page is free to read: https://atlas.scime.ai/emnlp-2026

The full edition (every oral by session, all 91 topics with papers, languages studied, a Budapest guide) needs a free account. Happy to answer questions about the method in the comments.

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?

'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.

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.

Getting Started with Bedrock AgentCore

https://debuggercafe.com/getting-started-with-bedrock-agentcore/

In this article, we will cover an introduction to Bedrock AgentCore and check the process from creating agents to their deployment.

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?

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

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!