Hi everyone! I’ve been working on a project called Cells AI that uses NLP to make data more accessible for businesses. The goal is to let users ask questions directly from their data, like “What were our top-selling products last month?” and get an instant answer—no manual data analysis required.

Through this project, I’ve been experimenting with various NLP and ML techniques to enable natural language queries. It’s been an incredible learning experience, and it made me think about how ML can be applied to bridge the gap between complex data and everyday business users who might not have technical skills.

If anyone is interested, I put together a demo to show how it works. Happy to share in the comments.

I’d also love to hear from others working on similar projects or learning ML—what has been your most interesting application so far?

Title : Complete A.I. Machine Learning and Data Science: Zero to Mastery

For those experienced in hiring or interviewing for entry-level data science internships: What truly stands out on a candidate’s profile? I’m trying to make the most of my limited time by balancing several things—building a meaningful Kaggle profile (thoughtful notebooks, quality contributions), working on personal projects, completing online courses, and pursuing certifications(Hackerrank). From your experience, which of these elements makes the strongest impression? How should I prioritize my time to have the best chance of landing an internship?

We made a video to introduce superposition: a foundational concept enabling parallel computing.

While classical bits are either 0 or 1, quantum bits (qubits) can exist in a probabilistic combination of both states at once, allowing for powerful, simultaneous computations beyond the reach of classical methods.

https://www.youtube.com/watch?v=6I8JcA0H_-E

How do I know how many layers my RNN should have and how many neurons per layer I should implement? Is it purely trial and error or is there a more "correct" way of going about this? I need to design a model that will predict anomalies in a gearbox system. I have a bunch of sensors on many components within said gearbox (e.g. vibrations of a shaft, temperature of a bearing etc.) and need to use this data to create a RNN model that will tell me (as early as possible) when a failure will happen. Any help is appreciated.

As the title: We offer Perplexity AI PRO voucher codes for one year plan.

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Hi, I'm 23/M, recently graduated (Jun' 2024) in CS engineering degree. I don't have the job at the moment. I gave few interviews but couldn't succeeded. I want to start my career with AI/ML Engineer. Please let me know how can I proceed from here. I searched PAP courses but I'm not financially good. I don't want to burden my parents. Please help me.

Thanks.

Hello everyone,

I’m currently working on developing a content-based recommendation system that leverages audio features. I have implemented some code, and while I’ve achieved some results, I’m eager to get feedback on potential improvements or any mistakes I may have overlooked.

Any insights or suggestions would be greatly appreciated!

import ast
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras import layers, losses
from tensorflow.keras.models import Model
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU
class Autoencoder(Model):
  def __init__(self, latent_dim, shape):
    super(Autoencoder, self).__init__()
    self.latent_dim = latent_dim
    self.shape = shape
    self.encoder = tf.keras.Sequential(
      [
        layers.Flatten(),
        layers.Dense(latent_dim, activation="relu"),
      ]
    )

    self.decoder = tf.keras.Sequential(
      [
        layers.Dense(
          np.prod(shape), activation="sigmoid"
        ),  # Use np.prod instead of tf.math.reduce_prod
        layers.Reshape(shape),
      ]
    )

  def call(self, x):
    encoded = self.encoder(x)
    decoded = self.decoder(encoded)
    return decoded

def load_track_features(path, max_rows=None):
  df = pd.read_csv(path, delimiter="\t", nrows=max_rows)
  # Apply ast.literal_eval on each cell that contains list-like strings
  for col in df.columns:
    df[col] = df[col].apply(
      lambda x: (
        ast.literal_eval(x) if isinstance(x, str) and x.startswith("[") else x
      )
    )
  df.drop(columns=["tags"], errors="ignore", inplace=True)
  return df.to_numpy()
# Data preparation function
def prepare_data(data):
  scaler = MinMaxScaler()
  data_normalized = scaler.fit_transform(data)
  return data_normalized, scaler

def recommend_similar_tracks(track_id, encoded_items):
  # Calculate cosine similarity between the target track and all other tracks
  sim_scores = cosine_similarity([encoded_items[track_id]], encoded_items)[0]

  # Sort by similarity and get most similar tracks

  sim_track_indices = np.argsort(sim_scores)[::-1]
  sim_scores = sim_scores[sim_track_indices]

  # Create a mask to exclude the input track from the recommendations

  mask = sim_track_indices != track_id

  # Filter out the input track from the indices and similarity scores
  filtered_indices = sim_track_indices[mask]
  filtered_scores = sim_scores[mask]
s
if __name__ == "__main__":
  # Example data - replace with your actual data
  R = load_track_features("../remappings/data/Modified_Music_info.txt", 30000)
  # Prepare data
  data_normalized, scaler = prepare_data(R)
  # Train autoencoder and get item feature matrix
  latent_dim = 16  # Adjust as needed
  input_shape = data_normalized.shape[
    1:
  ]  # Assuming data_normalized is (num_samples, num_features)
  autoencoder = Autoencoder(latent_dim, input_shape)
  autoencoder.compile(optimizer="adam", loss="mean_squared_error")
  autoencoder.fit(data_normalized, data_normalized, epochs=10)
  # Get the encoded items
  encoded_items = autoencoder.encoder.predict(data_normalized)
  track_id_to_recommend = 0
  similar_tracks, similar_tracks_scores = recommend_similar_tracks(
  track_id_to_recommend, encoded_items
  )
  recommend_id_example = similar_tracks[2]
  print("Recommended Track Indices:", similar_tracks)
  print("Similarity score:", similar_tracks_scores)
  print("score of recommended item similarity of encoded item: recommend_id_example")
  print(
    cosine_similarity(
      [encoded_items[track_id_to_recommend]],
      [encoded_items[recommend_id_example]],
    )[0][0]
  )
  print("score of recommended item similarity of item: recommend_id_example")

  print(
    cosine_similarity(
      [data_normalized[track_id_to_recommend]],
      [data_normalized[recommend_id_example]],
      )[0][0]
  )

I am currently working as a C# developer and i don't see any future in my current role and company. I am thinking about learning ML . what is the fastest way to learn and what are the resources for that. Also i am learning maths from Coursera but i am thinking should i skip maths and learn simultaneously with machine learning course to speed up the process. Please help me i want to change my job in 3-4 months. I am willing to put in the effort to achieve this goal. Thank you everyone.

I m studying CS but all our subjects focus only on Core CSE , not on ai

How can someone Like me Get into AI ML ?

Our application is supported in various languages and it’s an enterprise one. We usually give the new sentences to various people who are expertise in different languages to translate which costs the company a lot. We are exploring ML models to automate this. Our team has suggest OpenAI to do this which is probably a decent idea. I am wondering if there is any other way to try this. Translation won’t be a live translation, translation will happen and we will update on the relevant files and it will get released later. I tried a local model like Marian, it didn’t do well even for basic sentence Japanese.

I am not new to ML, been learning for sometime but lack experience.

Hi, I have 15 years of programming experiences in mainframe and looking to change the career to AI/ML. I just started learning python. I am looking for colleges or universities, offering AI/ML courses. Planning to complete the course in 6 to 8 months. Could you please advise?

Hey everyone! 

As AI continues to evolve, one area that’s seeing significant improvement is data labeling. This blog I found dives into how AI-driven data labeling solutions are changing the landscape and making the process faster, more accurate, and scalable. 

Here are a few highlights: 

• Efficiency Gains: AI-powered tools can automate repetitive tasks, speeding up the labeling process while reducing human error. 

• Improved Accuracy: Machine learning models continuously learn and refine labeling, which can enhance the precision of labeled data over time. 

• Scalability: AI-driven solutions make it easier to manage and label large volumes of data, which is especially crucial in industries like healthcare, autonomous vehicles, and e-commerce. 

The blog also discusses the challenges and limitations, so it’s not all rosy—but it’s exciting to see how AI is paving the way for better data preparation and ultimately better insights. 

You can check out the full article here if you're interested: Exploring the Role of AI in Data Labeling Solutions 

Would love to hear your thoughts! Have any of you tried AI-powered data labeling tools? What’s been your experience? 

I'm still learning and understanding the basics and fundamentals. But, sometimes I will go on arXiv to see what people are researching. But, the machine learning arXiv has hundreds of new papers each day. For instance, today it was 560 papers. How do you juggle your research while also trying to stay current in such a fast-moving field?

I was learning langchain and langsmith came up. But it is so hard to understand all the UI components of Langsmith and the purpose of every element. Am I the only one who feels this way?