📕 A Course in Reinforcement Learning by Dimitri P. Bertsekas

Explore the comprehensive world of Reinforcement Learning (RL) with this authoritative textbook by Dimitri P. Bertsekas. This book offers an in-depth overview of RL methodologies, focusing on optimal and suboptimal control, as well as discrete optimization. It's an essential resource for students, researchers, and professionals in the field.

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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!

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This checklist covers the essentials of NumPy in one place, helping you:

- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays

…and much more!

Feel free to share if you found this useful, and let me know in the comments if I missed anything!

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Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥

Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?

This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!

What’s Inside:

✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
✅ Deep Learning & Neural Networks – CNNs, RNNs, LSTMs
✅ End-to-End ML Projects – Data Preprocessing, Feature Engineering, Deployment
✅ Model Optimization – Hyperparameter Tuning, Ensemble Methods
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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

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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!

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🚀 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.

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🧠💻📊

Evolution of Deep Learning by Hand ✍️

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Four best-advanced university courses on NLP & LLM to advance your skills:

1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr

2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v

3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y

4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4

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🔴 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.👇


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💯 Top 100+ Google Data Science Interview Questions

🌟 Essential Prep Guide for Aspiring Candidates

Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.

To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.

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💯 Mastering Matplotlib in 20 Days

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Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.

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