代做COMP3314 Tutorial 1 Basics for Python Programming & Assignment 1代做Python语言

COMP3314 Tutorial 1

Basics for Python Programming & Assignment 1

Agenda

● Introduction

● Basics of Python

○ Setting Up Python Environment

○ Installing Miniconda

○ Managing Python Virtual Environments

○ Installing Libraries

● Development environment

○ Introduction to Jupyter Notebook

○ Leveraging Google Colab

○ Using Visual Studio Code for Python Development

● Overview of Assignment 1

● Summary and Q&A

Basics of Python

● What is Python?

○ High-Level, Interpreted Language: Known for simplicity and readability.

○ Created by Guido van Rossum in the late 1980s.

○ Purpose: Designed for ease of use, quick application development.

● Key Features of Python

○ Intuitive Syntax: Ideal for beginners.

○ Versatile Use: From web development to automation.

○ Open Source: With a large, supportive community.

○ Rich Libraries: For data analysis, ML, scientific computing.

○ Interpreted Nature: Facilitates quick prototyping.

● Python in Machine Learning & Data Science

○ Dominant Language: Due to simplicity and powerful libraries.

○ Strong Community Support: Resources and forums for learning.

○ Efficient for Prototyping: Quick experimentation with ML models.

One of the most popular programming languages

● Python is the 2# most popular programming language on GitHub

Installing Miniconda

● What is a Python virtual environment?

○ A virtual environment is a "container" for of multiple installed Python libraries and executables

○ Best practice: use separate environment for each project

● What is Miniconda?

○ A popular tool for managing Python virtual environment

○ Miniconda is the "mini" version of conda, recommended for general use

● Installing Miniconda

○ Find the proper version for your OS and follow the steps

■ https://docs.conda.io/projects/miniconda/en/latest/

○ Optional: prevent conda from activating base automatically

■ https://stackoverflow.com/a/54560785/1255535

● conda config --set auto_activate_base false

● Live demo for installation on macOS/Linux

○ Please refer to: https://asciinema.org/a/YhEyleUmEHeKfPRKIX4nxlKuK

● Windows installation

○ Please refer to: https://www.youtube.com/watch?v=oHHbsMfyNR4

Managing Python Virtual Environments with Conda

● Creating a new virtual environment

○ # Create an environment called "demo"

○ conda create -n demo python=3.8

● Activating and deactivating environments

○ # Check existing environment

○ conda env list

○ # Activate "demo" environment

○ conda activate demo

○ # Check python version

○ python --version

○ # Deactivate environment

○ conda deactivate

Installing Python Libraries

● Introduction to pip and conda

● Common libraries for machine learning

○ NumPy, scikit-learn, PyTorch, TensorFlow, Jupyter

● Installing libraries using pip commands

○ # Activate your virtual environment first!

○ conda activate demo

○ # Install Python libraries

○ pip install numpy

○ pip install scikit-learn

○ pip install jupyter

○ ...

NumPy

● What is NumPy?

○ NumPy: A fundamental package for numerical computation in Python.

○ Core Feature: Multidimensional array object (ndarray).

○ Purpose: Optimized for numerical operations, linear algebra, random number capabilities.

● Key Features of NumPy

○ Efficient Array Computing: Fast, memory-efficient array processing.

○ Mathematical Functions: Comprehensive mathematical functions.

○ Interoperability: Works well with other libraries.

import numpy as np

# Creating a NumPy array

arr = np.array([1, 2, 3, 4, 5])

# Performing element-wise operations

squared = arr ** 2

# Computing basic statistics

mean_value = np.mean(arr)

print(f"Original Array: {arr}")

print(f"Squared Array: {squared}")

print(f"Mean Value: {mean_value}")

Original Array: [1 2 3 4 5]

Squared Array: [ 1 4 9 16 25]

Mean Value: 3.0

scikit-learn

● What is Scikit-Learn?

○ Scikit-Learn: A Python library for machine learning.

○ Purpose: Offer simple and efficient tools for data mining and data analysis.

● Key Features of Scikit-Learn

○ Wide Range of Algorithms: Classification, regression, clustering, etc.

○ Data Preprocessing Tools: Feature scaling, normalization, .etc.

○ Model Evaluation: Cross-validation, metrics for performance evaluation.

from sklearn.datasets import load_iris

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import

train_test_split

from sklearn.metrics import accuracy_score

# Load dataset

iris = load_iris()

X, y = iris.data, iris.target

# Split dataset

X_train, X_test, y_train, y_test =

train_test_split(X, y, test_size=0.3)

# Train a model

classifier = DecisionTreeClassifier()

classifier.fit(X_train, y_train)

# Predict and evaluate

predictions = classifier.predict(X_test)

accuracy = accuracy_score(y_test, predictions)

PyTorch

● What is PyTorch?

○ PyTorch: An open-source machine learning library developed by Facebook's AI Research lab.

○ Purpose: Preferred for deep learning and artificial intelligence projects.

○ Features: Dynamic computational graph and tensor computation with strong GPU acceleration.

● Key Features of PyTorch

○ Dynamic Computation Graphs: Flexibility and ease in defining and modifying neural networks.

○ Tensor Library: Similar to NumPy, but with GPU support.

○ Autograd Module: Automatic differentiation for gradient calculations.

import torch

import torch.nn as nn

import torch.optim as optim

# Simple neural network

class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

self.fc = nn.Linear(1, 1)

def forward(self, x):

return self.fc(x)

# Create a model, criterion and optimizer

model = Net()

criterion = nn.MSELoss()

ptimizer = optim.SGD(model.parameters(), lr=0.01)

# Dummy data

inputs = torch.tensor([[1.0], [2.0], [3.0]])

targets = torch.tensor([[2.0], [4.0], [6.0]])

# Forward pass, backward pass, optimize

optimizer.zero_grad()

utputs = model(inputs)

loss = criterion(outputs, targets)

loss.backward()

optimizer.step()

print(f"Loss: {loss.item()}")