Python Coding challenge - Day 503| What is the output of the following Python Code?

 

Code Explanation:

1. Create NumPy Array

a = np.array([1, 2, 3])

Creates a NumPy array a with values [1, 2, 3].

Shape: (3,), dtype: int64

a now → [1, 2, 3]

2. Assign Reference (Not a Copy)

b = a

b is not a new array — it references the same memory as a.

So, any change in b will reflect in a and vice versa.

3. Modify Element via Reference

b[0] = 99

Changes the first element of b to 99.

Since a and b are the same object, a also becomes [99, 2, 3].

4. Create a Deep Copy

c = a.copy()

Creates an independent copy of a.

Now c = [99, 2, 3], but it does not share memory with a or b.

5. Modify the Copy

c[1] = 88

Changes the second element of c to 88.

c becomes [99, 88, 3].

a and b remain [99, 2, 3].

6. Sum All Arrays and Print Result

print(np.sum(a) + np.sum(b) + np.sum(c))

a = [99, 2, 3] → sum = 104

b = [99, 2, 3] → same as a → sum = 104

c = [99, 88, 3] → sum = 190

Total Sum: 104 + 104 + 190 = 398

Final Output: 398

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3D Checkboard Surface Pattern using python

 

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

z = np.sin(x) * np.cos(y)  

checkerboard = ((np.floor(x) + np.floor(y)) % 2) == 0

colors = np.zeros(x.shape + (3,))

colors[checkerboard] = [1, 1, 1] 

colors[~checkerboard] = [0, 0, 0]  

fig = plt.figure(figsize=(6, 6))

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(x, y, z, facecolors=colors, rstride=1, cstride=1)

ax.set_title("3D Checkerboard Surface", fontsize=14)

ax.set_box_aspect([1, 1, 0.5])  

ax.axis('off')  

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy (as np): Used for creating grids and performing numerical calculations (like sin, cos, floor, etc.).

matplotlib.pyplot (as plt): Used for plotting graphs and rendering the 3D surface.

 

2. Create Grid Coordinates (x, y)

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

np.linspace(-5, 5, 100): Generates 100 evenly spaced values from -5 to 5 for both x and y.

np.meshgrid(x, y): Creates 2D grids from the 1D x and y arrays — necessary for plotting surfaces.

 

3. Define Surface Height (z values)

z = np.sin(x) * np.cos(y)

This creates a wavy surface using a trigonometric function.

Each (x, y) point gets a z value, forming a 3D landscape.

 

4. Generate Checkerboard Pattern

checkerboard = ((np.floor(x) + np.floor(y)) % 2) == 0

np.floor(x): Takes the floor (integer part) of each x and y coordinate.

Adds the floored x + y, and checks if the sum is even (i.e., divisible by 2).

If so → True (white square), else → False (black square).

This results in a checkerboard-like boolean mask.

 

5. Assign Colors to Checkerboard

colors = np.zeros(x.shape + (3,))

colors[checkerboard] = [1, 1, 1]

colors[~checkerboard] = [0, 0, 0]

colors = np.zeros(x.shape + (3,)): Initializes an array for RGB colors (shape: rows × cols × 3).

For True cells in checkerboard, assign white [1, 1, 1].

For False cells, assign black [0, 0, 0].

 

6. Set Up 3D Plot

fig = plt.figure(figsize=(8, 6))

ax = fig.add_subplot(111, projection='3d')

Creates a figure and a 3D subplot using projection='3d'.

 

7. Plot the Checkerboard Surface

ax.plot_surface(x, y, z, facecolors=colors, rstride=1, cstride=1)

Plots the 3D surface using x, y, z data.

facecolors=colors: Applies the checkerboard color pattern.

rstride and cstride: Row/column steps for rendering — set to 1 for full resolution.

 

8. Customize the View

ax.set_title("3D Checkerboard Surface", fontsize=14)

ax.set_box_aspect([1, 1, 0.5])

ax.axis('off')

set_title(): Sets the plot title.

set_box_aspect(): Controls aspect ratio: x:y:z = 1:1:0.5 (compressed z).

axis('off'): Hides axis ticks and labels for a clean look.

 

9. Render the Plot

plt.tight_layout()

plt.show()

tight_layout(): Adjusts spacing to prevent overlap.

show(): Renders the 3D checkerboard surface.

 

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Python Coding challenge - Day 502| What is the output of the following Python Code?

 

Code Explanation:

Line 1: Import reduce function

from functools import reduce

Explanation:

reduce() is a function from the functools module.

It repeatedly applies a function to the items of an iterable, reducing the iterable to a single cumulative value.

Line 2: Define the list numbers

numbers = [1, 2, 3, 4]

Explanation:

A list of integers is created: [1, 2, 3, 4].

Line 3: Define function f

f = lambda x: x * 2

Explanation:

This lambda function doubles the input.

Example: f(5) returns 10.

Line 4: Define function g

g = lambda lst: reduce(lambda a, b: a + b, lst)

Explanation:

g is a function that:

Takes a list lst.

Uses reduce() to sum all elements of the list.

Example: g([1, 2, 3, 4]) will compute 1 + 2 + 3 + 4 = 10.

Line 5: Combine functions and print result

print(f(g(numbers)))

Step-by-step Evaluation:

g(numbers):

Input: [1, 2, 3, 4]

Sum = 1 + 2 + 3 + 4 = 10

f(10):

10 * 2 = 20

Final Output:

20

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Python Coding challenge - Day 501| What is the output of the following Python Code?

 

Code Explanation:

Line 1: Define combine function

def combine(f, g):

    return lambda x: f(g(x))

Explanation:

This function takes two functions f and g as inputs.

It returns a new anonymous function (lambda) that takes an input x, applies g(x) first, and then applies f() to the result of g(x).

In other words, it returns f(g(x)) — this is called function composition.

Line 2: Define f

f = lambda x: x ** 2

Explanation:

f is a lambda function that squares its input.

Example: f(4) = 4 ** 2 = 16

Line 3: Define g

g = lambda x: x + 2

Explanation:

g is a lambda function that adds 2 to its input.

Example: g(3) = 3 + 2 = 5

Line 4: Compose functions using combine

h = combine(f, g)

Explanation:

h is now a new function created by combining f and g.

h(x) will compute f(g(x)), which is:

First: g(x) → add 2

Then: f(g(x)) → square the result

Line 5: Call and print h(3)

print(h(3))

Step-by-step Evaluation:

h(3) = f(g(3))

g(3) = 3 + 2 = 5

f(5) = 5 ** 2 = 25

So, h(3) = 25

Final Output:

25

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Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

 

Spring System Design in Practice — A Detailed Review and Key Takeaways

As the software world rapidly moves toward microservices and distributed systems, mastering scalable system design becomes not just a bonus skill but a necessity. "Spring System Design in Practice" is a hands-on, practical guide that offers an essential roadmap for developers, architects, and tech leads who want to harness the power of Spring Boot, microservices architecture, and design patterns.

In this blog, we’ll break down the structure, key themes, and practical insights of the book, and explain why it’s a must-read for Java/Spring developers aiming to build robust and scalable systems.

Book Overview

Full Title: Spring System Design in Practice: Build Scalable Web Applications Using Microservices and Design Patterns in Spring and Spring Boot

Best for: Mid-level to senior Java/Spring developers, architects, backend engineers

The book takes a problem-solution approach, focusing on real-world use cases and system-level design challenges. It teaches how to break a monolith into microservices, choose the right design patterns, and build high-performance, secure, and scalable applications using Spring Boot, Spring Cloud, and other related tools.

Key Topics Covered

1. Monolith to Microservices Transition

The book begins by illustrating why and when you should move away from monoliths. It presents practical strategies for decomposing a monolithic application and transitioning to microservices incrementally using Spring Boot.

Highlights:

• Domain-driven decomposition
• Strangler fig pattern
• Service boundaries and Bounded Contexts

2. Core Microservices Principles in Spring

Each microservice is treated as a mini-application. The book details the fundamental practices:

• Using Spring Boot for lightweight services
• Leveraging Spring WebFlux for reactive programming
• Managing inter-service communication via REST and gRPC
• Patterns Explored:
• API Gateway
• Circuit Breaker (Resilience4j)
• Service Discovery (Spring Cloud Netflix Eureka)

3. Design Patterns for Scalable Systems

This is arguably the most valuable section. The book dives deep into classic and cloud-native design patterns like:

• Repository Pattern (for clean data access)
• Command Query Responsibility Segregation (CQRS)
• Event Sourcing
• Saga Pattern (for distributed transactions)
• Outbox Pattern
• Bulkhead and Rate Limiting

Each pattern is explained with practical code samples and trade-offs.

4. System Design Case Studies

This is where theory meets reality. The book includes multiple case studies such as:

• E-commerce system
• Payment gateway
• Order management service
• Each case study demonstrates:
• Domain modeling
• API design
• Database design
• Service integration

5. Infrastructure and DevOps

To build truly scalable systems, infrastructure is key. The book covers:

Containerization with Docker

Deploying to Kubernetes

Using Spring Cloud Config Server for centralized configuration

Observability with Sleuth, Zipkin, and Prometheus/Grafana

6. Security and Resilience

Security in microservices can be tricky. The book teaches:

OAuth2 and JWT with Spring Security

Securing service-to-service calls

Implementing TLS, API keys, and mutual TLS

It also emphasizes graceful degradation, circuit breakers, and retries to ensure high availability.

Who Should Read This Book?

This book is perfect for:

• Backend Developers looking to level up their Spring ecosystem skills
• Tech Leads & Architects who design and manage distributed systems
• DevOps Engineers wanting to understand system requirements from the developer's perspective
• Students & Interviewees preparing for system design interviews

Pros

• Practical approach with step-by-step code examples
• Covers both design theory and engineering practices
• Deep dives into design patterns with real-world scenarios
• Infrastructure and DevOps coverage (Docker, Kubernetes)

 Cons

•  Assumes basic familiarity with Spring; not ideal for total beginners
• Some topics (e.g., gRPC or GraphQL) could use more depth

Hard Copy : Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

Kindle : Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

Final Takeaway

"Spring System Design in Practice" is more than just a programming book — it’s a manual for building real-world systems in the modern, cloud-native world. Whether you're migrating a monolith, designing a new microservice, or scaling an existing platform, this book gives you the tools, insights, and patterns to do it right.

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Python Coding Challange - Question with Answer (01220525)

 

Key Concepts:

🔹 lst=[] is a mutable default argument.

• In Python, default argument values are evaluated only once when the function is defined, not each time it’s called.

• That means the same list (lst) is reused across multiple calls unless a new one is explicitly provided.

---

Step-by-Step Execution:

First Call:

append_item(1)

val = 1

• No list is passed, so lst defaults to []

• 1 is appended to the list → list becomes [1]

• It returns [1]

Second Call:

append_item(2)

val = 2

• Still using the same list as before ([1])

• 2 is appended → list becomes [1, 2]

• It returns [1, 2]

---

✅ Output:

[1]
[1, 2]

---

 How to Avoid This Pitfall:

To make sure a new list is used for each call, use None as the default and create the list inside the function:

def append_item(val, lst=None):
    if lst is None:
       lst = []
    lst.append(val)     
    return lst

Now each call will work with a fresh list.

APPLICATION OF PYTHON IN FINANCE

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Machine Learning Basics

 

Machine Learning Basics: A Complete Beginner's Guide

What is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Instead of following hard-coded rules, ML systems use statistical techniques to identify patterns in data and apply those patterns to new, unseen information. For example, an ML model can learn to recognize cats in images after analyzing thousands of labeled photos. Just like humans learn from experience, machines learn from data.

Why is Machine Learning Important?

Machine learning has become a core technology in almost every industry. It powers the personalized recommendations on Netflix and Amazon, enables virtual assistants like Siri and Alexa to understand speech, helps banks detect fraudulent transactions, and supports doctors in diagnosing diseases. Its ability to make data-driven decisions at scale makes it one of the most transformative technologies of the 21st century.

Data: The Foundation of Machine Learning

At the heart of machine learning is data. Models are trained using datasets that contain examples of what the system is expected to learn. These examples include features (inputs like age, temperature, or words in a sentence) and labels (the desired output, such as a category or value). The more accurate, complete, and relevant the data, the better the model’s performance. A model trained on poor-quality data will struggle to deliver useful predictions.

Training and Testing Models

Machine learning involves two primary phases: training and testing. During training, the model studies a dataset to learn patterns. Once trained, it is evaluated on a separate testing dataset to see how well it performs on new data. This helps determine if the model can generalize beyond the examples it was trained on. A good model strikes a balance — it must be complex enough to capture patterns but not so specific that it only works on the training data (a problem known as overfitting).

Types of Machine Learning

There are three major categories of machine learning:

Supervised Learning

In supervised learning, the algorithm is given labeled data — meaning each input has a known output. The model learns to map inputs to outputs. Common applications include spam detection, sentiment analysis, and price prediction.

Unsupervised Learning

Unsupervised learning works with unlabeled data. The model tries to uncover hidden patterns or groupings within the dataset. Examples include customer segmentation, recommendation systems, and topic modeling.

Reinforcement Learning

In reinforcement learning, an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. It’s widely used in robotics, game AI (like AlphaGo), and self-driving cars.

Common Algorithms (Simplified)

Machine learning uses various algorithms to solve different types of problems. Some basic ones include:

Linear Regression: Predicts a numerical value (e.g., house price).

Logistic Regression: Used for binary classification (e.g., spam or not spam).

Decision Trees: Splits data into decision paths based on rules.

K-Nearest Neighbors (KNN): Classifies new data points based on similarity to known points.

Neural Networks: Inspired by the brain, used for complex tasks like image and speech recognition.

These algorithms vary in complexity and are chosen based on the problem type and data characteristics.

Challenges in Machine Learning

Machine learning isn’t magic — it comes with its own set of challenges:

Overfitting: When a model learns the training data too well, including its noise or errors, leading to poor performance on new data.

Underfitting: When a model is too simple to capture the underlying patterns in the data.

Bias and Fairness: If the training data reflects human biases, the model can perpetuate and even amplify them — leading to unfair or unethical outcomes.

Understanding and addressing these issues is critical for building reliable and responsible ML systems.

Tools and Languages Used in ML

While deep technical knowledge isn’t required to grasp ML basics, professionals often use the following tools:

Languages: Python (most popular), R

Libraries: scikit-learn, TensorFlow, PyTorch, Keras

Platforms: Google Colab, Jupyter Notebooks, Kaggle, AWS SageMaker

These tools allow data scientists to build, test, and deploy ML models efficiently.

How to Start Learning Machine Learning

You don’t need to be a programmer to begin learning about ML. Here’s how to start:

Understand the Concepts: Take beginner-friendly courses like “Machine Learning for All” on Coursera or watch YouTube explainers.

Learn Basic Python: Most ML is done in Python, and basic programming skills go a long way.

Explore Datasets: Use public data on platforms like Kaggle to practice.

Try Mini Projects: Build simple projects like spam filters, movie recommenders, or image classifiers.

Practice and experimentation are key to gaining hands-on experience.

The Future of Machine Learning

Machine learning will continue to revolutionize how we work, communicate, and solve problems. It’s already being used in fields like agriculture, education, finance, transportation, and climate science. As the technology becomes more accessible, we’ll see a rise in citizen data scientists — professionals in every field using ML tools to make better decisions and drive innovation.

Join Free : Machine Learning Basics

Final Thoughts

Machine Learning may sound complex, but at its core, it's about learning from data and making predictions. As we enter an increasingly data-driven world, understanding ML—even at a basic level—will help you become a more informed and empowered citizen. Whether you’re a student, a professional, or just curious, the best time to start learning about machine learning is now.

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Chrono Web Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

r = np.linspace(0.1, 5, 200)

theta = np.linspace(0, 2 * np.pi, 200)

r, theta = np.meshgrid(r, theta)

X = r * np.cos(theta)

Y = r * np.sin(theta)

Z = np.sin(4 * theta - 2 * r) * np.exp(-0.1 * r)

fig = plt.figure(figsize=(6, 6))

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='black', linewidth=0.1)

ax.set_title('Chrono Web', fontsize=18, fontweight='bold')

ax.axis('off')

ax.view_init(elev=30, azim=45)

plt.tight_layout()

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy is used for numerical operations, especially for creating arrays and mathematical functions.

matplotlib.pyplot is the plotting library used for visualization.

mpl_toolkits.mplot3d enables 3D plotting capabilities in matplotlib.

2. Create the Polar Grid

r = np.linspace(0.1, 5, 200)

theta = np.linspace(0, 2 * np.pi, 200)

r, theta = np.meshgrid(r, theta)

r (radius) goes from 0.1 to 5 in 200 steps.

theta (angle) goes from 0 to 2π (a full circle) in 200 steps.

np.meshgrid creates a 2D grid from these vectors, so we can calculate X, Y, and Z values over the full polar coordinate system.

3. Convert Polar Coordinates to Cartesian

X = r * np.cos(theta)

Y = r * np.sin(theta)

Converts each point in the polar grid into Cartesian coordinates.

This is needed because matplotlib 3D plots are in X-Y-Z space.

4. Define Z Values (Height) – the "Chrono Web" Pattern

Z = np.sin(4 * theta - 2 * r) * np.exp(-0.1 * r)

This formula creates radial sine wave ripples.

4 * theta gives a rotational (angular) ripple with 4 waves per rotation.

-2 * r makes the wave shift inward or outward, creating a spiraling effect.

np.exp(-0.1 * r) damps the wave amplitude as the radius increases — simulating fading over distance, like time decay.

5. Set Up the Plot

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

fig = plt.figure(...) creates the figure window with a specific size.

add_subplot(..., projection='3d') initializes a 3D plot.

6. Draw the Surface

ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='black', linewidth=0.1)

plot_surface draws a 3D surface.

cmap='viridis' gives a smooth color gradient.

edgecolor='black', linewidth=0.1 adds a subtle grid to give a web-like structure.

7. Customize the Plot

ax.set_title('Chrono Web', fontsize=18, fontweight='bold')

ax.axis('off')

ax.view_init(elev=30, azim=45)

set_title(...) adds a bold title to the plot.

axis('off') hides the axes for a cleaner, more artistic look.

view_init(...) sets the camera angle (elevation = 30°, azimuth = 45°) for 3D viewing.

8. Final Layout and Display

plt.tight_layout()

plt.show()

tight_layout() adjusts the spacing to fit all elements nicely.

plt.show() renders the plot window and displays the final "Chrono Web" 3D pattern.

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Signal Interference Mesh Pattern using Python

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.linspace(-10, 10, 200)

y = np.linspace(-10, 10, 200)

X, Y = np.meshgrid(x, y)

sources = [

    {'center': (-3, -3), 'freq': 2.5},

    {'center': (3, 3), 'freq': 3.0},

    {'center': (-3, 3), 'freq': 1.8},

]

Z = np.zeros_like(X)

for src in sources:

    dx = X - src['center'][0]

    dy = Y - src['center'][1]

    r = np.sqrt(dx**2 + dy**2) + 1e-6  

    Z += np.sin(src['freq'] * r) / r  

fig = plt.figure(figsize=(6, 8))

ax = fig.add_subplot(111, projection='3d')

ax.plot_wireframe(X, Y, Z, rstride=3, cstride=3, color='mediumblue', alpha=0.8, linewidth=0.5)

ax.set_title("Signal Interference Mesh", fontsize=16)

ax.set_xlabel("X")

ax.set_ylabel("Y")

ax.set_zlabel("Amplitude")

ax.set_box_aspect([1,1,0.5])

plt.tight_layout()

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Required Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

Explanation:

numpy (as np) is used for efficient array manipulation and mathematical operations.

matplotlib.pyplot (as plt) is used for plotting 2D/3D graphs.

mpl_toolkits.mplot3d.Axes3D enables 3D plotting with Matplotlib.

2. Creating the Grid

x = np.linspace(-10, 10, 200)

y = np.linspace(-10, 10, 200)

X, Y = np.meshgrid(x, y)

Explanation:

np.linspace(-10, 10, 200) creates 200 evenly spaced points between -10 and 10 for both x and y.

np.meshgrid(x, y) creates 2D grid coordinates X and Y, representing the Cartesian plane over which signals will be calculated.

3. Defining Signal Sources

sources = [

    {'center': (-3, -3), 'freq': 2.5},

    {'center': (3, 3), 'freq': 3.0},

    {'center': (-3, 3), 'freq': 1.8},

]

Explanation:

This list defines 3 point sources, each with:

A center coordinate in 2D space (x, y)

A freq (frequency) value that affects the signal's oscillation

4. Calculating the Resulting Signal

Z = np.zeros_like(X)

for src in sources:

    dx = X - src['center'][0]

    dy = Y - src['center'][1]

    r = np.sqrt(dx**2 + dy**2) + 1e-6

    Z += np.sin(src['freq'] * r) / r

Explanation:

Z = np.zeros_like(X) initializes a 2D grid to accumulate the total signal amplitude at each point.

For each source:

dx, dy: distance in X and Y from the source center.

r: radial distance from the source to each grid point (with a small epsilon added to avoid division by zero).

np.sin(freq * r) / r: simulates a wave signal from a point source that decays with distance.

These signals are added together to simulate interference.

5. Plotting the 3D Wireframe

fig = plt.figure(figsize=(6, 8))

ax = fig.add_subplot(111, projection='3d')

Explanation:

A figure object is created with a specified size (6x8 inches).

add_subplot(111, projection='3d') creates a 3D axis for plotting.

6. Rendering the Mesh Plot

ax.plot_wireframe(X, Y, Z, rstride=3, cstride=3, color='mediumblue', alpha=0.8, linewidth=0.5)

Explanation:

plot_wireframe creates a mesh-style 3D plot showing how the signal amplitude varies.

rstride and cstride control mesh resolution.

color, alpha, and linewidth adjust aesthetics.

7. Setting Titles and Labels

ax.set_title("Signal Interference Mesh", fontsize=16)

ax.set_xlabel("X")

ax.set_ylabel("Y")

ax.set_zlabel("Amplitude")

ax.set_box_aspect([1,1,0.5])

Explanation:

Adds a title and axis labels to explain what the axes represent.

set_box_aspect controls the 3D plot's aspect ratio for better visual balance.

8. Finalizing and Displaying the Plot

plt.tight_layout()

plt.show()

Explanation:

tight_layout() adjusts spacing to prevent clipping.

show() renders the final interactive 3D plot window.

 

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Python Coding Challange - Question with Answer (01210525)

 

Step-by-step Explanation:

1. Function Definition:

   
   def foo():
       return "Original"

   This defines a normal function foo that returns the string "Original". At this point, foo refers to this function.

2. Function Overwriting:

   foo = lambda: "Reassigned"

   Now you're overwriting the foo identifier. Instead of pointing to the function defined earlier, it now points to a lambda function (an anonymous function) that returns "Reassigned".

   ✅ The original foo() function is still in memory, but it's now inaccessible because the name foo now refers to something else.

3. Function Call:

   
   print(foo())

   Now when you call foo(), you're calling the lambda function, which returns "Reassigned". So the output will be:

   
   Reassigned

---

Key Concept:

In Python, functions are objects, and variable names (like foo) can be reassigned just like any other variable. Once you assign a new function (or any value) to foo, the original one is no longer accessible through that name.

CREATING GUIS WITH PYTHON

https://pythonclcoding.gumroad.com/l/chqcp

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Python Coding challenge - Day 496| What is the output of the following Python Code?

 

Code Explanation:

 Function Definition

def foo(x=[]):

What this does: Defines a function foo with one parameter x.

Default argument: The default value for x is an empty list [].

Important note: In Python, default arguments are evaluated only once when the function is defined, not each time it is called. This means that x will keep its state between calls if no new argument is passed.

 Function Body

    x.append(1)

Action: Appends the integer 1 to the list x.

So if x starts as [], it becomes [1] after one call, [1, 1] after two calls, etc.

    return x

Returns: The (now modified) list x.

First Function Call

print(foo())

No argument is passed → x uses the default value [].

x.append(1) → x becomes [1].

Returns [1], which is printed.

Second Function Call

print(foo())

Again, no argument is passed → it uses the same list from the previous call (not a fresh empty list).

x.append(1) → x becomes [1, 1].

Returns [1, 1], which is printed.

Output Summary

[1]

[1, 1]

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Python Coding Challange - Question with Answer (01200525)

 

Step-by-step Explanation

✅ Step 1: Assign values

a = True
b = False

✅ Step 2: Evaluate the condition in the if statement

if a and b or not a:

We apply operator precedence:

• not has higher precedence than and, which has higher precedence than or.

• So Python evaluates the expression like this:

if (a and b) or (not a):

Now evaluate each part:

• a and b → True and False → False

• not a → not True → False

So:

if False or False:

This simplifies to:

if False:

✅ Step 3: Since the condition is False, the else block runs:

else:
    print("Stop")

---

 Final Output:

Stop

---

 Key Concepts:

• Logical AND (and): Only True if both operands are True.

• Logical OR (or): True if at least one operand is True.

• Logical NOT (not): Reverses the truth value.

• Operator Precedence: not > and > or

HANDS-ON STATISTICS FOR DATA ANALYSIS IN PYTHON

https://pythonclcoding.gumroad.com/l/eqmpdm

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Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models

 

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models – A Journey Through AI's Evolution

In recent years, machine learning (ML) has gone from a niche academic interest to a transformative force shaping industries, economies, and even our daily lives. Whether it's the language models powering chatbots like ChatGPT or generative AI systems creating stunning artwork, the impact of ML is undeniable. For anyone interested in understanding how we reached this point—from early statistical methods to cutting-edge generative models—Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models provides a comprehensive and insightful guide to the history and future of the field.

A Step Back: The Classical Foundations of Machine Learning

The book opens with a deep dive into the roots of machine learning, revisiting classical algorithms that laid the groundwork for today’s more complex systems. It introduces foundational concepts such as linear regression, logistic regression, and decision trees, offering a mathematical and conceptual understanding of how these models were used to solve real-world problems. These classical methods, though seemingly simple compared to today's deep networks, remain powerful tools for many applications, especially in environments where interpretability and transparency are key.

The book also highlights ensemble methods like random forests and boosting techniques such as AdaBoost and XGBoost. These methods have continued to evolve, maintaining their relevance even in the age of deep learning. The authors make an important point: these classic techniques, often overshadowed by newer approaches, are not relics of the past but vital tools that still have much to offer in machine learning tasks today.

The Deep Learning Revolution

Moving from the past to the present, the book then transitions into the era of deep learning, where neural networks began to dominate the ML landscape. The development of deep learning was marked by several breakthroughs that pushed the boundaries of what was possible. The authors explore the mechanics of neural networks, starting with the perceptron and progressing to deep multilayer networks, explaining how backpropagation and gradient descent have become essential for training these models.

The book then delves into the rise of convolutional neural networks (CNNs), which revolutionized computer vision, and recurrent neural networks (RNNs), which are used for sequential data like text or time series. These architectures enabled machines to excel at tasks that were previously considered insurmountable, such as image classification, object detection, and language translation. Challenges in training deep models, such as the problem of vanishing gradients and overfitting, are thoroughly discussed, along with solutions like dropout, batch normalization, and more recently, transformer networks.

The Transformer Revolution: A New Era in Natural Language Processing

Perhaps the most exciting and contemporary section of the book focuses on transformers—the architecture that has driven the recent surge in natural language processing (NLP) and beyond. Introduced in the seminal paper “Attention is All You Need,” transformer models like BERT and GPT have become the backbone of state-of-the-art models across a variety of tasks, from text generation to translation to summarization.

What makes transformers unique is their attention mechanism, which allows the model to weigh different parts of an input sequence differently, depending on their relevance. This innovation marked a significant shift from previous models, which relied on sequential processing. The book explains how transformers can process data in parallel, making them more efficient and scalable. This section is incredibly valuable for anyone interested in understanding how modern language models work, as it walks readers through the structure of these models and their applications, both in research and in industry.

The book doesn't just stop at the technical details of transformers; it also discusses the scaling laws that show how increasing the size of models and datasets leads to dramatic improvements in performance. It covers pretraining and fine-tuning, shedding light on how these models are adapted for a wide range of tasks with minimal task-specific data.

Diffusion Models: The Cutting-Edge of Generative AI

Finally, the book brings readers to the cutting edge of AI with diffusion models, the latest development in generative modeling. Diffusion models, such as Stable Diffusion and DALL·E 2, are now at the forefront of AI-generated art, allowing machines to create detailed images from textual descriptions. The book explains how these models work by iteratively adding noise to data during training and then learning to reverse this process to generate high-quality outputs.

This section provides a clear overview of denoising diffusion probabilistic models (DDPMs) and score-based generative models, explaining the theoretical underpinnings and practical applications of these approaches. What’s fascinating is how diffusion models, unlike other generative methods such as GANs (Generative Adversarial Networks), are stable during training and have fewer issues with mode collapse or quality degradation.

The authors also compare diffusion models with other generative techniques like GANs and Variational Autoencoders (VAEs), offering insights into the strengths and weaknesses of each. With the rise of text-to-image and text-to-video generation, diffusion models are rapidly becoming one of the most important tools in the generative AI toolkit.

A Unified Perspective on the Evolution of Machine Learning

One of the strengths of Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models is how it ties together the different epochs of machine learning. By connecting the classical statistical models to the modern deep learning architectures, and then extending to the latest generative models, the book provides a cohesive narrative that shows how each advancement built on the last. It’s clear that ML has been an iterative process, with each breakthrough contributing to the next, often in unexpected ways.

This unified perspective makes the book more than just a technical guide; it serves as a historical document that helps readers appreciate the deep interconnections between the various ML approaches and understand where the field is heading. The final chapters provide a glimpse into the future, speculating on the next big advancements and the potential societal impacts of AI.

Who Should Read This Book?

Students & Beginners in Machine Learning:

If you’re a student starting your journey in machine learning, this book provides an excellent foundation. It covers both the classical algorithms and the modern deep learning architectures, making it a perfect resource for building a comprehensive understanding of the field. The clear explanations and gradual progression from simpler concepts to more advanced topics make it easy to follow, even for beginners.

Aspiring AI Practitioners:

For anyone looking to enter the field of artificial intelligence, this book offers the essential knowledge needed to navigate the landscape. It touches upon both traditional machine learning techniques and cutting-edge innovations like transformers and diffusion models, which are critical to today’s AI applications. If you're working toward building AI models or developing applications, this book will help you grasp the key techniques used in the industry.

Researchers in Machine Learning and AI:

If you're a researcher, especially in fields like natural language processing (NLP), computer vision, or generative AI, this book will serve as both a solid reference and an inspiration. The detailed discussions on transformer models and diffusion models, along with their theoretical backgrounds, offer insights into the current state of the art and highlight areas for future research.

AI and Machine Learning Educators:

This book is also a fantastic resource for educators who are teaching machine learning. The structure, which progresses logically from foundational concepts to more advanced topics, makes it ideal for course material. The clear, intuitive explanations paired with practical examples can make it easier for instructors to convey complex ML ideas to students.

Data Scientists & Engineers:

If you're already working in data science or engineering and want to update your knowledge, this book offers a deep dive into modern deep learning techniques such as transformers and generative models. Whether you're building NLP applications, computer vision systems, or using generative AI for creative tasks, understanding the theoretical and practical aspects of these models is crucial for advancing your work.

Machine Learning Enthusiasts & Practitioners Looking to Expand Their Knowledge:

If you have some experience with machine learning but are interested in understanding more about cutting-edge models like transformers and diffusion models, this book will guide you through these advanced concepts. It will help you connect older techniques with the latest innovations in a cohesive manner, expanding your understanding of the entire field.

Tech Industry Professionals Curious About AI’s Evolution:

If you're a tech professional working in any capacity related to AI, this book provides the historical context that helps explain how we got to where we are today. Whether you’re working in product management, strategy, or technical roles, understanding the progression from classical machine learning to today’s generative models will enrich your perspective on the potential of AI technologies in various industries.

AI Enthusiasts and Hobbyists:

For those who are passionate about AI and want to learn how it’s evolved over time, this book offers an accessible but deep exploration. It’s great for those who might not be pursuing a career in AI but are interested in understanding how modern models work, the theoretical principles behind them, and how these technologies are reshaping the world.

What Will You Learn?

Foundations of Classical Machine Learning Models:

• You will master the core concepts of traditional machine learning algorithms, such as linear regression, logistic regression, and decision trees.
• Learn about ensemble methods like random forests and boosting techniques (e.g., AdaBoost, XGBoost), which are still crucial in many real-world machine learning tasks.
• Understand model evaluation techniques like cross-validation, confusion matrices, and performance metrics (accuracy, precision, recall, F1-score).
• Gain an understanding of the strengths and weaknesses of classical models and when they are most effective.

Deep Learning Concepts and Architectures:

• Understand how neural networks work and why they are such a powerful tool for solving complex tasks.
• Dive into key deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs) for image recognition, and recurrent neural networks (RNNs) for sequential data like time series and text.
• Learn about optimization techniques like stochastic gradient descent (SGD), Adam optimizer, and strategies for avoiding problems such as vanishing gradients and overfitting.
• Discover how regularization techniques like dropout, batch normalization, and early stopping help to train more robust models.

Transformers and Natural Language Processing (NLP):

• Learn about the revolutionary transformer architecture and how it enables models to process sequential data more efficiently than traditional RNNs and LSTMs.
• Understand the self-attention mechanism and how it allows models to focus on different parts of the input dynamically, improving performance in tasks like translation, text generation, and summarization.
• Explore powerful models like BERT (Bidirectional Encoder Representations from Transformers) for understanding context in language, and GPT (Generative Pretrained Transformer) for generating human-like text.
• Learn about fine-tuning pre-trained models and the importance of transfer learning in modern NLP tasks.
• Gain insight into the significance of scaling large models and the role of prompt engineering in achieving better performance.

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Conclusion: An Essential Resource for ML Enthusiasts

Whether you're a student just beginning your journey in machine learning, a seasoned practitioner looking to expand your knowledge, or simply an AI enthusiast eager to understand the technologies that are changing the world, this book is an invaluable resource. Its clear explanations, practical examples, and comprehensive coverage make it a must-read for anyone interested in the evolution of machine learning—from its humble beginnings to its cutting-edge innovations.

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Data Mining: Practical Machine Learning Tools and Techniques

 

Data Mining: Practical Machine Learning Tools and Techniques

In today’s data-driven world, data mining is no longer a luxury—it’s a necessity. From detecting fraud in financial systems to recommending products on e-commerce platforms, data mining powers intelligent decision-making across industries. At the heart of this process lies a set of practical machine learning tools and techniques that make sense of massive volumes of data. This blog will explore the fundamentals of data mining, delve into essential machine learning techniques, and introduce some of the most widely used tools in practice.

What Is Data Mining?

Data mining is the process of discovering patterns, correlations, trends, and useful information from large datasets using statistical, mathematical, and computational techniques. It is a core step in the larger process of knowledge discovery in databases (KDD).

Key Goals of Data Mining:

Classification: Assign data into predefined categories (e.g., spam detection).

Clustering: Group similar data points without predefined labels (e.g., customer segmentation).

Association Rule Learning: Discover relationships between variables (e.g., market basket analysis).

Anomaly Detection: Identify rare items or events (e.g., fraud detection).

Prediction/Regression: Predict a continuous value (e.g., stock prices).

Machine Learning and Data Mining: The Connection

Machine learning (ML) provides the algorithms and models that drive most data mining tasks. While data mining focuses on uncovering patterns from data, machine learning focuses on building predictive models using that data.

Types of Machine Learning in Data Mining:

Supervised Learning: Uses labeled data to train models (e.g., decision trees, support vector machines).

Unsupervised Learning: Identifies patterns in unlabeled data (e.g., k-means, DBSCAN).

Semi-Supervised Learning: Combines a small amount of labeled data with a large amount of unlabeled data.

Reinforcement Learning: Agents learn optimal behaviors through trial and error (less common in traditional data mining).

Practical Tools for Data Mining

Modern data scientists rely on powerful tools to apply machine learning techniques effectively. Here are some of the most popular tools:

1. WEKA (Waikato Environment for Knowledge Analysis)

A comprehensive suite of machine learning algorithms for data mining tasks.

Written in Java with a GUI and command-line interface.

Supports classification, clustering, regression, association rules, and data preprocessing.

Excellent for educational purposes and prototyping.

2. Scikit-learn

A robust Python library for classical machine learning algorithms.

Built on top of NumPy, SciPy, and matplotlib.

Ideal for classification, regression, clustering, dimensionality reduction, and model evaluation.

3. R and caret

R provides statistical computing and graphics.

The caret package streamlines model training and evaluation.

Especially strong in statistical analysis and visualization.

4. RapidMiner

A GUI-based data science platform with drag-and-drop capabilities.

Supports data preprocessing, modeling, validation, and deployment.

Suitable for both beginners and professionals.

5. KNIME (Konstanz Information Miner)

An open-source data analytics platform.

Offers visual workflows for data mining and machine learning.

Integrates well with Python, R, and other tools.

Essential Techniques in Data Mining

Let’s explore some of the foundational techniques commonly used:

1. Decision Trees

Flowchart-like structure for decision-making.

Easy to interpret and visualize.

Algorithms: ID3, C4.5, CART.

2. k-Nearest Neighbors (k-NN)

A simple yet effective classification technique.

Classifies based on the majority class of the nearest neighbors.

3. Naïve Bayes

Probabilistic classifier based on Bayes’ theorem.

Assumes feature independence.

Particularly effective for text classification.

4. Support Vector Machines (SVM)

Finds the optimal hyperplane to separate classes.

Effective in high-dimensional spaces.

5. Clustering Algorithms

K-means: Partitions data into k clusters.

Hierarchical Clustering: Builds a tree of clusters.

DBSCAN: Density-based clustering for finding arbitrary-shaped clusters.

6. Association Rule Learning

Finds interesting relationships among variables.

Commonly used in market basket analysis.

Algorithms: Apriori, FP-Growth.

Data Preprocessing: The Unsung Hero

Before mining, data must be cleaned and prepared. This involves:

• Handling missing values
• Normalizing or scaling features
• Encoding categorical variables
• Feature selection and extraction
• Splitting datasets into training and testing sets

Without proper preprocessing, even the most advanced algorithms can yield poor results.

Evaluating Model Performance

Choosing the right evaluation metric is crucial:

• Accuracy (for balanced classes)
• Precision, Recall, F1-score (for imbalanced data)
• Confusion Matrix
• ROC-AUC Curve
• Cross-Validation for robust evaluation

Real-World Applications of Data Mining

• Healthcare: Predict disease outbreaks, personalize treatments.
• Finance: Credit scoring, fraud detection.
• Retail: Customer segmentation, recommendation systems.
• Marketing: Targeted advertising, churn prediction.
• Cybersecurity: Intrusion detection systems.

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Final Thoughts

Data mining, empowered by practical machine learning tools and techniques, is transforming industries and redefining how decisions are made. Whether you’re a data enthusiast or a business leader, understanding the practical side of data mining opens up opportunities to harness data for meaningful insights and strategic advantage.

If you’re just getting started, tools like WEKA and Scikit-learn provide an accessible gateway. As you grow, integrating more advanced techniques and workflows will elevate your data mining capabilities to the next level.

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Designing Large Language Model Applications: A Holistic Approach to LLMs

 

Designing Large Language Model Applications: A Holistic Approach to LLMs

Large Language Models (LLMs) like GPT, BERT, and T5 have quickly revolutionized the world of artificial intelligence (AI). Their ability to understand and generate human-like text has enabled breakthroughs in natural language processing (NLP) tasks such as text generation, translation, summarization, and more. As organizations and developers explore ways to leverage these models, designing effective LLM applications has become an essential skill. The process, however, is not just about selecting the right model; it involves integrating various components to build robust, scalable, and efficient systems. In this blog, we’ll take a holistic approach to designing large language model applications, considering the various stages, challenges, and best practices involved in their development and deployment.

1. Defining the Problem: What Problem Are You Solving?

Before jumping into the technicalities of using LLMs, it's crucial to clearly define the problem you're solving. The problem definition stage helps in determining the scope, requirements, and success metrics for the application. Here’s what needs to be considered:

Task Type: Identify the NLP task you want the LLM to perform, such as text generation, question answering, summarization, sentiment analysis, or translation.

User Needs: Understand what end-users expect from the application, whether it's generating creative content, automating customer support, or providing real-time insights from data.

Constraints: Determine the limitations you may face, such as response time, model accuracy, and handling domain-specific jargon.

The clearer you are about the problem, the easier it will be to select the right LLM and design the application accordingly.

2. Choosing the Right LLM

With the problem defined, the next step is selecting the right LLM for the application. There are multiple models available, each with strengths suited for different types of tasks:

Pretrained Models: Models like GPT-3, GPT-4, BERT, and T5 are general-purpose and come with pretrained knowledge that can be fine-tuned for specific use cases. If your task is general, these models might be ideal.

Domain-Specific Models: For specialized tasks (e.g., medical diagnostics, legal documents, or financial forecasting), domain-specific models like BioBERT or FinBERT may offer better performance due to their fine-tuning on industry-specific data.

Custom Models: If none of the off-the-shelf models fit the problem at hand, you can train a custom model from scratch or fine-tune an existing one based on your data. This approach requires substantial resources but can provide highly tailored performance.

Choosing the correct LLM is essential to ensure that the model is capable of handling the complexity and nuances of the task.

3. Data Collection and Preprocessing

Data is at the heart of any machine learning application, and LLMs are no exception. To effectively design an LLM application, you'll need access to a robust dataset that represents the problem domain. The quality and quantity of data will directly influence the performance of the model.

Data Collection: For general tasks, large, publicly available datasets may suffice. For domain-specific applications, however, you might need to gather and label proprietary data.

Preprocessing: LLMs require text data to be preprocessed into a format suitable for model training. This may involve tokenization (splitting text into smaller units), removing noise (e.g., stop words, special characters), and converting data into vectors that the model can understand.

Data diversity is key: Ensure that your dataset captures the wide variety of language inputs your application might encounter. The more representative your data is of real-world use cases, the better the performance.

4. Fine-Tuning the Model

While large language models come pretrained, they often need to be fine-tuned on domain-specific data to improve their performance for specialized applications. Fine-tuning helps adapt the model to the nuances of a particular task.

Transfer Learning: Transfer learning allows the model to leverage knowledge from one domain and apply it to another. Fine-tuning involves adjusting the weights of a pretrained model using your specific dataset.

Hyperparameter Tuning: Adjusting hyperparameters (e.g., learning rate, batch size) during fine-tuning can greatly impact model performance. Automated tools like Hyperopt or Optuna can assist in finding optimal settings.

This step is crucial to ensuring that the LLM understands the subtleties of your specific problem, including domain-specific terms, tone, and context.

5. Designing the User Interface (UI)

For an LLM application to be effective, it must be user-friendly. The user interface (UI) plays a key role in ensuring that users can easily interact with the system and get value from it.

Interactive Design: Depending on the use case, the UI can vary from a simple chat interface (like chatbots or virtual assistants) to complex dashboards or dashboards with analytics, depending on user needs.

Feedback Loop: Incorporate ways for users to provide feedback, helping improve the system over time. For instance, users could flag incorrect responses, which can then be used to fine-tune the model in future iterations.

An intuitive UI will help ensure that users can access and leverage the model’s capabilities without needing deep technical expertise.

6. Scalability and Deployment

Once the model is fine-tuned and the UI is designed, the application needs to be deployed in a scalable, reliable, and secure way. The challenges here include:

Model Hosting: LLMs are computationally intensive, so you’ll need powerful infrastructure. Cloud services like AWS, Google Cloud, or Azure offer scalable environments that allow you to deploy and manage large models.

Latency and Performance: Ensure the application can handle real-time requests without significant latency. This might involve techniques like model distillation (creating smaller, faster versions of the model) or batching requests to improve throughput.

Monitoring and Logging: Implement monitoring tools to track the model’s performance in production. Logs should include metrics like response time, accuracy, and error rates, which are important for ensuring smooth operation.

Scalability is especially important if the application needs to handle high volumes of traffic or if it's integrated with other systems, such as in customer service or e-commerce platforms.

7. Continuous Improvement and Feedback Loop

Once the LLM application is live, the process of improving it is continuous. As users interact with the system, they will inevitably encounter edge cases or performance issues that need to be addressed.

Model Retraining: Regularly retrain the model with new data to ensure that it keeps up with changes in language use or industry developments.

User Feedback: Incorporate user feedback to identify common issues or gaps in the model’s capabilities. This feedback can be used to fine-tune the model and improve performance over time.

By implementing a feedback loop, you can ensure that your application remains relevant and continues to provide value in the long term.

8. Ethical Considerations and Responsible AI

With the power of LLMs comes the responsibility of ensuring that they are used ethically. Ethical considerations include:

Bias Mitigation: LLMs are trained on vast datasets, and these datasets can contain biased or unrepresentative data. It’s important to evaluate the model for potential bias and take steps to mitigate it.

Transparency: LLMs are often considered “black boxes,” which can be challenging when it comes to explaining their decisions. Providing users with clear explanations of how the model arrived at a decision can help foster trust.

Privacy: Especially in domains like healthcare or finance, ensuring that user data is kept private and secure is essential.

Developing and deploying LLM applications with ethical practices at the forefront is key to building trust with users and avoiding negative societal impacts.

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Conclusion

Designing effective LLM applications is a multifaceted process that requires not only an understanding of large language models but also a deep awareness of the technical, user experience, and ethical considerations involved. By following a holistic approach—from problem definition to model selection, fine-tuning, deployment, and continuous improvement—you can create impactful applications that harness the power of LLMs to deliver tangible value to users. With careful attention to these areas, you’ll be well-equipped to develop scalable, efficient, and ethical AI-driven applications that can address real-world problems and elevate user experiences.

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