Telegram Web Link
Forget Coding; start Vibing! Tell AI what you want, and watch it build your dream website while you enjoy a cup of coffee.

Date: Thursday, April 17th at 9 PM IST

Register for FREE: https://lu.ma/4nczknky?tk=eAT3Bi

Limited FREE Seat !!!!!!
Mathematical theory of Deep Learning:

[Download 282-page PDF. Updated version]:
arxiv.org/abs/2407.18384

#AI #ML #MachineLearning #DeepLearning #Mathematics #DataScience #DataScientist

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Thomas
🔥ENTER VIP FOR FREE! ENTRY 24 HOURS FREE!

LISA TRADER - most successful trader for 2024. A week ago they finished a marathon in their vip channel where from $100 they made $2000, in just two weeks of time!

Entry to her channel cost : $1500 FOR 24 ENTRY FREE!

JOIN THE VIP CHANNEL NOW!
JOIN THE VIP CHANNEL NOW!
JOIN THE VIP CHANNEL NOW!
Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
Data cleaning and preparation techniques.

https://www.tg-me.com/DataScienceM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
ML Tools GRadio.pdf
203.3 KB
Gradio: The easiest way to demo your models.

- Core Idea: Quickly turn #ML models into interactive web apps.

- No frontend skills needed. It's all #Python.

- Works with any Python code, including custom functions.

- Share via temporary links or deploy on #HuggingFace Spaces.

- Get user feedback to improve your models.

If you're looking to create interactive demos for your ML project, check out #Gradio!

♻️ Repost if you found this useful

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

https://www.tg-me.com/addlist/8_rRW2scgfRhOTc0

https://www.tg-me.com/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Python Courses
Media is too big
VIEW IN TELEGRAM
Click Me Load More CSV files into a database using Python.

🖥 By: https://www.tg-me.com/Python53

⭐️ BEST DATA SCIENCE CHANNELS ON TELEGRAM ⭐️
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Python Courses
🚀 LunaProxy - The Most Cost-effective Residential Proxy Exclusive Benefits for Members of This Group: 💥 Residential Proxy: As low as $0.77 / GB. Use the discount code [lunapro30] when placing an order and save 30% immediately. ✔️ Over 200 million pure IPs | No charge for invalid ones | Success rate > 99.9% 💥 Unlimited Traffic Proxy: Enjoy a discount of up to 72%, only $79 / day. ✔️ Unlimited traffic | Unlimited concurrency | Bandwidth of over 100Gbps | Customized services | Save 90% of the cost when collecting AI/LLM data Join the Luna Affiliate Program and earn a 10% commission. There is no upper limit for the commission, and you can withdraw it at any time.
👉 Take action now: https://www.lunaproxy.com/?ls=data&lk=?01
Please open Telegram to view this post
VIEW IN TELEGRAM
Mastering CNNs: From Kernels to Model Evaluation

If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’s break it down from basic to advanced.

1. What is Conv2D?

Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.

2. What is a Kernel (or Filter)?

A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.

A 3x3 kernel means the filter looks at 3x3 chunks of the image.

The kernel detects patterns like edges, textures, etc.


Example:
A vertical edge detection kernel might look like:

[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]

3. What Are Filters in Conv2D?

In CNNs, we don’t use just one filter—we use multiple filters in a single Conv2D layer.

Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).

So if you have 32 filters in the Conv2D layer, you’ll get 32 feature maps.

More Filters = More Features = More Learning Power

4. Kernel Size and Its Impact

Smaller kernels (e.g., 3x3) are most common; they capture fine details.

Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.

Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.

5. Life Cycle of a CNN Model (From Data to Evaluation)

Let’s visualize how a CNN model works from start to finish:

Step 1: Data Collection

Images are gathered and labeled (e.g., cat vs dog).

Step 2: Preprocessing

Resize images

Normalize pixel values

Data augmentation (flipping, rotation, etc.)

Step 3: Model Building (Conv2D layers)

Add Conv2D + Activation (ReLU)

Use Pooling layers (MaxPooling2D)

Add Dropout to prevent overfitting

Flatten and connect to Dense layers

Step 4: Training the Model

Feed data in batches

Use loss function (like cross-entropy)

Optimize using backpropagation + optimizer (like Adam)

Adjust weights over several epochs

Step 5: Evaluation

Test the model on unseen data

Use metrics like Accuracy, Precision, Recall, F1-Score

Visualize using confusion matrix

Step 6: Deployment

Convert model to suitable format (e.g., ONNX, TensorFlow Lite)

Deploy on web, mobile, or edge devices

Summary

Conv2D uses filters (kernels) to extract image features.

More filters = better feature detection.

The CNN pipeline takes raw image data, learns features, and gives powerful predictions.

If this helped you, let me know! Or feel free to share your experience learning CNNs!

💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
This media is not supported in your browser
VIEW IN TELEGRAM
How do transformers work? Learn it by hand 👇

𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵

[1] Given
↳ Input features from the previous block (5 positions)

[2] Attention
↳ Feed all 5 features to a query-key attention module (QK) to obtain an attention weight matrix (A). I will skip the details of this module. In a follow-up post I will unpack this module.

[3] Attention Weighting
↳ Multiply the input features with the attention weight matrix to obtain attention weighted features (Z). Note that there are still 5 positions.
↳ The effect is to combine features across positions (horizontally), in this case, X1 := X1 + X2, X2 := X2 + X3....etc.

[4] FFN: First Layer
↳ Feed all 5 attention weighted features into the first layer.
↳ Multiply these features with the weights and biases.
↳ The effect is to combine features across feature dimensions (vertically).
↳ The dimensionality of each feature is increased from 3 to 4.
↳ Note that each position is processed by the same weight matrix. This is what the term "position-wise" is referring to.
↳ Note that the FFN is essentially a multi layer perceptron.

[5] ReLU
↳ Negative values are set to zeros by ReLU.

[6] FFN: Second Layer
↳ Feed all 5 features (d=3) into the second layer.
↳ The dimensionality of each feature is decreased from 4 back to 3.
↳ The output is fed to the next block to repeat this process.
↳ Note that the next block would have a completely separate set of parameters.

#ai #tranformers #genai #learning

💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
🔴 Comprehensive course on "Data Mining"
🖥 Carnegie Mellon University, USA


👨🏻‍💻 Carnegie University in the United States has come to offer a free #datamining course in 25 lectures to those interested in this field.

◀️ In this course, you will deal with statistical concepts and model selection methods on the one hand, and on the other hand, you will have to implement these concepts in practice and present the results.

◀️ The exercises are both combined: theory, #coding, and practical.👇


🥵 Data Mining
⏯️ Course Homepage

💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
2025/07/01 07:02:37
Back to Top
HTML Embed Code: