Machine Learning and Deep Learning Maths - Matrix and Vector Operations
Explore essential matrix and vector operations for machine learning and deep learning using Python, covering creation, manipulation, and mathematical computations.
IT Data Structures Class - University of South Florida Fall 2016
Comprehensive exploration of data structures, covering arrays, STL containers, C++ classes, and Big O notation. Includes practical coding examples and best practices.
C++
Master C++ essentials: arrays, loops, STL containers, file I/O, classes, and Big O notation. Hands-on practice with CLion IDE and modern C++ features.
C++ Programming
Learn essential C++ programming concepts and apply them to game development using SFML. Master variables, vectors, loops, sprites, user interactions, and game maps for diverse applications.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2
Comprehensive introduction to TensorFlow and deep learning fundamentals, covering tensor operations, neural network regression, and classification. Hands-on coding examples and practical insights for building and improving models.
The State of Julia in 2021 - JuliaCon Keynote
Comprehensive overview of Julia's progress in 2021, covering threading, compiler improvements, linear algebra advancements, package updates, and future development plans.
Interplay of Linear Algebra, Machine Learning, and HPC - JuliaCon 2021 Keynote
Explore the interplay of linear algebra, machine learning, and HPC with Dr. Li. Learn about fast solvers, low-rank compression, and how ML can optimize linear algebra computations for big data problems.
Principal Component Analysis - Lecture 8
Explore Principal Component Analysis: from matrix rank to SVD. Learn to measure data cloud size, understand correlated data, and navigate high-dimensional spaces using statistical techniques.
Transformations - Composability and Linearity in Computational Thinking - Lecture 4
Explore composability and linearity in transformations, focusing on image processing applications and advanced Julia programming techniques for vector-valued functions and linear/nonlinear transformations.
Transformations and Automatic Differentiation in Computational Thinking - Lecture 3
Explore transformations and automatic differentiation in Julia, covering linear and non-linear transformations, function types, and applications in machine learning, with practical examples and visualizations.
Transforming Images and Image Processing Techniques - Lecture 2
Explore image transformation techniques using Julia, including downsampling, linear combinations, convolutions, and filters. Learn about complexity, GPUs, and data structures in computational thinking.
Matrix Calculus for Linear Algebra - MIT 18.06 Spring 2020
Comprehensive exploration of matrix calculus, covering gradients, Jacobians, and advanced concepts. Ideal for those seeking a deep understanding of mathematical foundations in linear algebra.
Understanding Structure in Data - Week 3
Explore data structure analysis using Julia, covering matrix rank, data visualization, PCA, and building a simple recommendation engine. Gain practical insights into advanced data science concepts.
Structure and Matrices in Julia Programming - Lecture 3
Explore matrices, sparse structures, and SVD for image compression and computational thinking in Julia, with practical applications in machine learning and linear algebra.
Tricks and Tips in Numerical Computing - Keynote
Explore advanced numerical computing techniques, from complex differentiation to low-precision arithmetic, with insights on error analysis, matrix operations, and IEEE standards for improved computational accuracy and efficiency.
Excel Array Formulas & Functions - 50 Examples of How to Become an Array Expert
Master Excel array formulas with 50 practical examples, covering fundamental concepts, advanced functions, and real-world applications for data analysis and reporting.
3D on Apple II
Explore 3D graphics on Apple II+ using AppleSoft BASIC. Learn matrix math, rotation techniques, and cube rendering in this nostalgic coding challenge.
Systems of Differential Equations - Diagonalization and Jordan Canonical Form
Explore diagonalization and Jordan Canonical Form for systems of differential equations, covering eigenvectors, complex conjugates, repeated eigenvalues, and general matrix applications.
Non-Normal Linear Systems and Transient Energy Growth - Bypass Transition to Turbulence
Explore non-normal linear systems and their role in turbulence, including examples, code demonstrations, and applications in fluid dynamics and bypass transition.
Drawing Phase Portraits for Nonlinear Systems
Learn to analyze nonlinear systems by drawing phase portraits, identifying fixed points, linearizing, and inferring global dynamics. Gain practical skills in stability analysis using eigenvalues and eigenvectors.
Systems of ODEs: Imaginary Eigenvalues and Center Fixed Points
Explore imaginary eigenvalues and center fixed points in 2D ODE systems. Learn to compute eigenvectors, write solutions, and understand geometric intuition behind rotational dynamics.