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36 篇笔记Classification with a Neural Network
Neural networks are advanced computational models that mimic the human brain's structure, enabling them to capture and model complex, non-linear relationships between inputs and outputs. The
读这篇 →`DataFrame` data structure
The DataFrame data structure is the heart of the Panda's library. It's a primary object used in data analysis and cleaning tasks. Conceptually, a DataFrame is a two-dimensional series object
读这篇 →`DataFrame` Indexing and Loading
Introduction to CSV Files
读这篇 →`Series` Data Structure
A Series in pandas is a one-dimensional array-like object that can hold various data types, similar to a list in Python, but with additional features. It combines elements of lists and dicti
读这篇 →Activity Selection
The Activity Selection problem is a classic example of a greedy algorithm application. It is concerned with selecting the maximum number of activities that don't overlap in time from a given
读这篇 →Bellman-Ford Algorithm
The Bellman-Ford algorithm is a graph-searching algorithm that calculates the shortest paths from a single source vertex to all other vertices in a weighted graph. It is particularly useful
读这篇 →Binary Search
Binary search is a highly efficient searching algorithm used to find the position of a target value within a sorted array. This algorithm operates on the divide and conquer principle, which
读这篇 →Breadth-first search (BFS)
Breadth-first search BFS is a widely used algorithm for traversing or searching tree and graph data structures. It starts at a selected node often referred to as the 'root' in the context of
读这篇 →Bubble Sort
Bubble Sort is one of the simplest sorting algorithms in computer science. It repeatedly steps through the list to be sorted, compares adjacent elements, and swaps them if they are in the wr
读这篇 →Data Cleaning with Pandas
In this lecture, we covered a basic data cleaning process using pandas. We focused on cleaning a dataset containing a list of US presidents from Wikipedia. The key operations included import
读这篇 →Date Functionality
In today's lecture, we explored time series and date functionality in pandas. Manipulating dates and times in pandas is highly flexible, enabling us to conduct advanced analysis such as time
读这篇 →Depth-First Search (DFS)
Depth-First Search DFS is a fundamental algorithm used in graph theory to traverse or search through the nodes of a graph in a systematic manner. DFS explores as deep as possible along each
读这篇 →Dijkstra's Algorithm
Dijkstra's algorithm is a graph search algorithm that finds the shortest paths from a single source node to all other nodes in a weighted graph with non-negative edge weights. It was conceiv
读这篇 →Errors and Exceptions
The Try-Except Construct
读这篇 →Fibonacci Series
The Fibonacci series is a sequence of numbers where each number is the sum of the two preceding ones, usually starting with 0 and 1. Mathematically, it is defined as:
读这篇 →Floyd-Warshall Algorithm
The Floyd-Warshall Algorithm is a dynamic programming method used to find the shortest paths between all pairs of nodes in a weighted graph. This algorithm can handle both directed and undir
读这篇 →Fractional Knapsack Problem
The Fractional Knapsack Problem is a variant of the knapsack problem where it's permissible to take fractional parts of items rather than having to make a binary choice for each item all or
读这篇 →Grouping Data
Grouping data is an essential task for data analysis, allowing us to understand and manipulate data at a group level. Pandas provides the groupby function to facilitate this, implementing th
读这篇 →Heap Sort
Heap Sort is a comparison-based sorting algorithm that uses a binary heap data structure to efficiently sort elements. It's particularly effective for data sets stored in random access struc
读这篇 →Huffman Coding
Huffman Coding is a widely used method for data compression that involves creating variable-length codes for input characters, with the lengths based on the frequencies of the characters. It
读这篇 →Idioms
In Python programming, certain idioms are considered more appropriate and efficient. Pandas, a sub-language within Python, has its own idioms, often referred to as "pandorable." These idioms
读这篇 →Indexing `DataFrame`
In pandas, both Series and DataFrame objects can have indices applied to them. An index serves as a row-level label, corresponding to axis zero. Indices can be autogenerated or explicitly se
读这篇 →Insertion Sort
Insertion Sort is a fundamental comparison-based sorting algorithm that mirrors the way you might sort playing cards in your hand. By building a sorted array one element at a time, it offers
读这篇 →Job Sequencing Problem
The Job Sequencing Problem is a classic problem in computer science, often solved using greedy algorithms. The problem involves scheduling jobs to maximize profit when each job has a deadlin
读这篇 →Knapsack Problem
The Knapsack Problem is a classic optimization problem that can be efficiently solved using dynamic programming. The goal is to choose a subset of items with maximum total value, subject to
读这篇 →Kruskal's algorithm
Kruskal's algorithm is a popular method used to find the minimum spanning tree MST for a connected, weighted, undirected graph. The goal of the algorithm is to find a subset of the graph's e
读这篇 →Linear Search
Linear search, also known as sequential search, is a fundamental algorithm used to find a specific element within a list. It operates by starting at the beginning of the list and examining e
读这篇 →Log Loss in Machine Learning
Machine learning often involves optimization problems that aim to minimize or maximize a particular function, known as a loss function. Two of the most common loss functions are square loss
读这篇 →Longest Common Subsequence
The Longest Common Subsequence LCS problem is a classic computer science problem used to find the longest subsequence common to all sequences in a set of sequences often just two sequences .
读这篇 →Merge Sort
Merge Sort is an efficient, general-purpose, comparison-based sorting algorithm. It follows the divide and conquer paradigm, which involves breaking a problem into smaller subproblems, solvi
读这篇 →Merging DataFrames
Relational Theory Concepts
读这篇 →Minimum Spanning Tree (MST)
In graph theory, a Minimum Spanning Tree MST for a weighted graph is a spanning tree with the minimum weight among all the spanning trees. It connects all the vertices together without any c
读这篇 →Missing Values
Missing values are common in data cleaning activities and can occur for various reasons. Understanding the nature and handling of missing data is crucial for accurate data analysis.
读这篇 →Ordinary Least Squares
Linear models aim to predict a target value as a linear combination of input features. Commonly represented with:
读这篇 →Other Test Concepts
Black Box vs. White Box Testing
读这篇 →Pandas
Official Website
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