💧 18 of the best blogs to start and grow on your data science path!


👨🏻‍💻 If I were to start my data journey all over again, I would follow these 18 blogs without a break!

💬 The path to growth in data has nothing to do with your starting point, what matters is how consistently you learn.

✅ These blogs are like a mentor in your email; every week a new insight, a learning, a new idea. 👇


🥵 The Data Hustle Blog

⏪ Real experiences from the world of data in a friendly tone.

➖➖➖

🥵 Diving Into Data Blog

⏪ Summarizing data-heavy concepts in simple language.

➖➖➖

🥵 Tech Growth Series Blog

⏪ A guide to professional growth in the field of technology and data.

➖➖➖

🥵 Data Neighbor Blog

⏪ Discussion and experience of the real learning path in the data field.

➖➖➖

🥵 DataEngineer.io Blog

⏪ The world of data engineering with real solutions.

➖➖➖

🥵 The Data Analyst Blueprint Blog

⏪ A complete roadmap to becoming a data analyst step by step.

➖➖➖

🥵 Jam with AI Blog

⏪ Up-to-date content in the field of AI; suitable for starting and continuing learning.

➖➖➖

🥵 Zero2Dataengineer Blog

⏪ From zero to becoming an engineer with a specific path.

➖➖➖

🥵 Maistermind Blog

⏪ Deep insights and systems thinking in data.

➖➖➖

🥵 The Fit Data Scientist Blog

⏪ Data science + healthy lifestyle = true balance.

➖➖➖

🥵 To Be a Data Scientist Blog

⏪ Focus on career path, soft skills, and motivation to continue in the world of data.

➖➖➖

🥵 Smarter Techies Blog

⏪ Continuous learning and golden tips for data scientists.

➖➖➖

🥵 Data Marks Blog

⏪ An excellent compilation of useful resources and tools in the world of data.

➖➖➖

🥵 Tech Audience Accelerator Blog

⏪ Building a personal brand and growing in the technology space.

➖➖➖

🥵 ByteByteGo Blog

⏪ System design and technical concepts for every data engineer.

➖➖➖

🥵 The Neural Maze Blog

⏪ Exploring neural models and artificial intelligence with tangible examples.

➖➖➖

🥵 To Data & Beyond Blog

⏪ Data, personal growth, and new paths for the future of work.

➖➖➖

🥵 Non-Brand Data Blog

⏪ Combining data with marketing expertise and personalized strategy.

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 🌟

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 🌟

Data cleaning and preparation techniques.

https://news.1rj.ru/str/DataScienceM 🌟

📢 5-Day Generative AI Intensive Course with #Google is now available as a self-paced Learn Guide!

Access whitepapers, podcasts, code labs, & recorded livestreams. Additionally, there is a bonus assignment for you!
https://www.kaggle.com/learn-guide/5-day-genai

#GenerativeAI #GoogleAI #AICourse #SelfPacedLearning #MachineLearning #DeepLearning #Kaggle #AICommunity #TechEducation #AIforEveryone

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

A Complete Course to Learn Robotics and Perception

Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson

github.com/gtbook/robotics

roboticsbook.org/intro.html

#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

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 🌟

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://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0

✅ https://news.1rj.ru/str/Codeprogrammer

Loading CSV files into a database using Python.

🖥 By: https://news.1rj.ru/str/Python53

⭐️ BEST DATA SCIENCE CHANNELS ON TELEGRAM ⭐️

فرصة عمل عن بعد 🧑‍💻
لا يتطلب اي مؤهل او خبره الشركه تقدم تدريب كامل ✨
ساعات العمل مرنه  ⏰
يتم التسجيل ثم التواصل معك لحضور لقاء تعريفي بالعمل والشركه

https://forms.gle/hqUZXu7u4uLjEDPv8

🚀 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

Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:

Function optimization and solving equations

Linear algebra operations

ODE integration and statistical analysis

Signal processing and spatial data manipulation

Data clustering and distance computation ...and much more!

#Python #SciPy #MachineLearning #DataScience #CheatSheet #ArtificialIntelligence #Optimization #LinearAlgebra #SignalProcessing #BigData

💯 BEST DATA SCIENCE CHANNELS ON 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 🌟

🚀 Master the Transformer Architecture with PyTorch! 🧠

Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".

🔗 Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 Insights into the self-attention mechanism and positional encoding.

By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.

#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks



💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

🧠💻📊