This combination is perhaps as low as we can get to explain how the Transformer works

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100 Python Interview Questions and Answers

This book is a practical guide to mastering Python interview preparation. It contains 100 carefully curated questions with clear, concise answers designed in a quick-reference style.

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Python Interview Codes Cheatsheet

Python Interview Codes Cheatsheet

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🤖🧠 PokeeResearch: Advancing Deep Research with AI and Web-Integrated Intelligence

🗓️ 09 Nov 2025
📚 AI News & Trends

In the modern information era, the ability to research fast, accurately and at scale has become a competitive advantage for businesses, researchers, analysts and developers. As online data expands exponentially, traditional search engines and manual research workflows are no longer sufficient to gather reliable insights efficiently. This need has fueled the rise of AI research ...

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🤖🧠 Pico-Banana-400K: The Breakthrough Dataset Advancing Text-Guided Image Editing

🗓️ 09 Nov 2025
📚 AI News & Trends

Text-guided image editing has rapidly evolved with powerful multimodal models capable of transforming images using simple natural-language instructions. These models can change object colors, modify lighting, add accessories, adjust backgrounds or even convert real photographs into artistic styles. However, the progress of research has been limited by one crucial bottleneck: the lack of large-scale, high-quality, ...

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🤖🧠 Concerto: How Joint 2D-3D Self-Supervised Learning Is Redefining Spatial Intelligence

🗓️ 09 Nov 2025
📚 AI News & Trends

The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to ...

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🤖🧠 The Transformer Architecture: How Attention Revolutionized Deep Learning

🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

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🤖🧠 BERT: Revolutionizing Natural Language Processing with Bidirectional Transformers

🗓️ 11 Nov 2025
📚 AI News & Trends

In the ever-evolving landscape of artificial intelligence and natural language processing (NLP), BERT (Bidirectional Encoder Representations from Transformers) stands as a monumental breakthrough. Developed by researchers at Google AI in 2018, BERT introduced a new way of understanding the context of language by using deep bidirectional training of the Transformer architecture. Unlike previous models that ...

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🤖🧠 Plandex AI: The Future of Autonomous Coding Agents for Large-Scale Development

🗓️ 11 Nov 2025
📚 AI News & Trends

As software development becomes increasingly complex, developers are turning to AI tools that can manage, understand and automate large portions of the coding workflow. Among the most promising innovations in this space is Plandex AI, an open-source terminal-based coding agent designed for real-world, large-scale projects. Unlike simple AI coding assistants that handle small snippets, Plandex ...

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🤖🧠 Nanobrowser: The Open-Source AI Web Automation Tool Changing How We Browse

🗓️ 12 Nov 2025
📚 AI News & Trends

The rise of artificial intelligence has redefined how we interact with the web, transforming routine browsing into a space for automation and productivity. Among the most exciting innovations in this field is Nanobrowser, an open-source AI-powered web automation tool designed to run directly inside your browser. Developed as a free alternative to OpenAI Operator, Nanobrowser ...

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🗓️ 12 Nov 2025
📚 AI News & Trends

In the era of rapid digital transformation, automation is the driving force behind business efficiency and innovation. While most AI agents are limited to browsers or APIs, a groundbreaking open-source project called Bytebot has redefined what AI can achieve. Bytebot introduces a self-hosted AI desktop agent — a virtual computer that performs complex, multi-step tasks ...

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Tip for clean code in Python:

Use Dataclasses for classes that primarily store data. The @dataclass decorator automatically generates special methods like __init__(), __repr__(), and __eq__(), reducing boilerplate code and making your intent clearer.

from dataclasses import dataclass

# --- BEFORE: Using a standard class ---
# A lot of boilerplate code is needed for basic functionality.

class ProductOld:
    def __init__(self, name: str, price: float, sku: str):
        self.name = name
        self.price = price
        self.sku = sku

    def __repr__(self):
        return f"ProductOld(name='{self.name}', price={self.price}, sku='{self.sku}')"

    def __eq__(self, other):
        if not isinstance(other, ProductOld):
            return NotImplemented
        return (self.name, self.price, self.sku) == (other.name, other.price, other.sku)

# Example Usage
product_a = ProductOld("Laptop", 1200.00, "LP-123")
product_b = ProductOld("Laptop", 1200.00, "LP-123")

print(product_a)  # Output: ProductOld(name='Laptop', price=1200.0, sku='LP-123')
print(product_a == product_b)  # Output: True


# --- AFTER: Using a dataclass ---
# The code is concise, readable, and less error-prone.

@dataclass(frozen=True) # frozen=True makes instances immutable
class Product:
    name: str
    price: float
    sku: str

# Example Usage
product_c = Product("Laptop", 1200.00, "LP-123")
product_d = Product("Laptop", 1200.00, "LP-123")

print(product_c)  # Output: Product(name='Laptop', price=1200.0, sku='LP-123')
print(product_c == product_d)  # Output: True

#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality

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By: @CodeProgrammer ✨

📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read

Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.

#PyTorch #MachineLearning #NeuralNetwork #Regression #Python