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Here are the top ten resources for learning Python programming: 1. Stack Overflow: This platform is a valuable resource for programmers to find solutions to coding problems and learn from others' experiences. You can ask questions, search for answers, and explore different approaches to solving programming challenges. def hello_world(): print("Hello, world!") hello_world() 2. Python Documentation: The official Python documentation is a comprehensive guide that covers all aspects of the language, including syntax, built-in functions, and standard libraries. It is a great reference for both beginners and experienced programmers. def add_numbers(a, b): return a + b result = add_numbers(5, 10) print(result) 3. Codecademy: Codecademy offers interactive Python courses that allow you to practice coding in a hands-on way. The platform provides step-by-step guidance and instant feedback to help you learn Python effectively. def calculate_area(radius): return

Sure! Here is a script you can use for your video: --- Introduction: In Python, the max() function is used to find the largest item in an iterable or among multiple arguments. It returns the maximum value. Example 1: Explaining: 1. Create a list of numbers. 2. Use the max() function to find the largest number in the list. 3. Print the result. Code: numbers = [5, 10, 3, 8, 15] largest_number = max(numbers) print(largest_number) Example 2: Explaining: 1. Define multiple variables with different values. 2. Pass these variables as arguments to the max() function. 3. Print the maximum value among the variables. Code: a = 25 b = 40 c = 15 maximum_value = max(a, b, c) print(maximum_value) By following these examples, you can effectively use the max() function in Python to find the maximum value in a list or among multiple arguments.

Lambda functions in Python are anonymous functions that can have any number of arguments, but can only have one expression. They are useful when you need a simple function for a short period of time. Example 1: Adding two numbers using a lambda function Step 1: Define a lambda function that takes two arguments and returns their sum. Step 2: Call the lambda function with the desired arguments. add = lambda x, y: x + y result = add(5, 3) print(result) In this example, we define a lambda function called add that takes two arguments x and y and returns their sum. We then call the lambda function with arguments 5 and 3 , which results in 8 being printed. Example 2: Filtering a list using a lambda function Step 1: Define a list of numbers. Step 2: Use the filter function with a lambda function to filter out even numbers from the list. Step 3: Convert the filtered result into a list. numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] even_numbers = list(filter(lambda x: x % 2 == 0,

Recursion in Python is a powerful technique where a function calls itself in order to solve a problem. It is particularly useful when dealing with problems that can be broken down into smaller, similar subproblems. Example 1: Let's consider a classic example of recursion - calculating the factorial of a number. The factorial of a non-negative integer n is the product of all positive integers less than or equal to n. For example, the factorial of 5 is 5*4*3*2*1 = 120. 1. Define a recursive function called factorial that takes a single parameter n. 2. Check if n is equal to 0 or 1, return 1 in these cases as the factorial of 0 and 1 is 1. 3. If n is greater than 1, call the factorial function recursively with n-1 and multiply the result by n. 4. Repeat this process until n becomes 1, then return the final result. def factorial(n): if n == 0 or n == 1: return 1 else: return n * factorial(n - 1) result = factorial(5) print(result) # Output: 120 Example 2:

Generative adversarial networks (GANs) are a type of deep learning model that consists of two neural networks - a generator and a discriminator. The generator generates new data instances, while the discriminator evaluates them for authenticity. Step 1: In the first step, we need to define the generator network. This network takes random noise as input and generates fake data samples that resemble the real data. We will use a simple feedforward neural network for the generator. import torch import torch.nn as nn class Generator(nn.Module): def __init__(self, input_dim, output_dim): super(Generator, self).__init__() self.model = nn.Sequential( nn.Linear(input_dim, 128), nn.ReLU(), nn.Linear(128, output_dim), nn.Tanh() ) def forward(self, x): return self.model(x) Step 2: Next, we need to define the discriminator network. This network takes input data samples and predicts whether they are re

In the first example, we are going to use the Stochastic Gradient Descent (SGD) optimization algorithm in Python for deep learning. Step 1: Import the necessary libraries import numpy as np import tensorflow as tf Step 2: Define the model and compile it using SGD optimizer model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy']) Step 3: Train the model on your dataset model.fit(x_train, y_train, epochs=10, batch_size=32) In the second example, we are going to use the Adam optimization algorithm in Python for deep learning. Step 1: Import the necessary libraries import numpy as np import tensorflow as tf Step 2: Define the model and compile it using Adam optimizer model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.lay