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25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on...

273,905 Aufrufe • vor 2 Jahren •via X (Twitter)

9 Kommentare

Profilbild von John Crickett
John Crickettvor 2 Jahren

99% of programmers will never write one of this professionally. Ironically the ones I have done aren't on this list.

Profilbild von Santiago
Santiagovor 2 Jahren

They won't. But the usefulness of learning these algorithms is not to write them later. That's the argument I make in the video.

Profilbild von Raul Junco
Raul Juncovor 2 Jahren

The main benefit of understanding algorithms is the skills you learn to solve problems efficiently and effectively.

Profilbild von Byron Goodman
Byron Goodmanvor 2 Jahren

I've never implemented DFS except in college, and graduate school. I have implemented A* and variations (BFS) many, many times. The problem with DFS is incomplete graphs and the bias introduced. A* requires more memory in comparison, but memory is cheap. Dijkstra's algorithm is basically A* so you can take that one off as well.

Profilbild von Calc Consulting
Calc Consultingvor 2 Jahren

21 Every algorithm related to neural networks 😂

Profilbild von Santiago
Santiagovor 2 Jahren

Yup :)

Profilbild von Edward Manda
Edward Mandavor 2 Jahren

What’s the name of the book 📖?

Profilbild von Anirudh Sharma
Anirudh Sharmavor 2 Jahren

Learning algorithms is not only useful for interviews but also beneficial for becoming a better problem solver.

Profilbild von inner voice priv/acc
inner voice priv/accvor 2 Jahren

Seriously! 📺 vids!

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Khurram Javed

52,078 Aufrufe • vor 6 Monaten

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

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