FreeBSD KVM (Kernel Virtual Memory Access)

svnweb.freebsd.org/base/release/10.2.0/bin/pkill/
Tracker, is a filesystem indexer, metadata storage system and search tool.
fossies.org/linux/tracker/src/tracker/tracker-process.c

How can a process inquire, when it was started?
github.com/dnabre/misc/tree/master/proc_info, Get time process started
github.com/hishamhm/htop/blob/master/freebsd/FreeBSDProcessList.c

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>

#include <signal.h>

/* _POSIX2_LINE_MAX */
#include <limits.h>

/* kvm_openfiles(), kvm_getprocs() */
#include <kvm.h>

/* errx() */
#include <err.h>

/* _PATH_DEVNULL */
#include <paths.h>

/* O_RDONLY */
#include <fcntl.h>

/* KERN_PROC_PROC */
#include <sys/sysctl.h>

/* struct kinfo_proc */
#include <sys/user.h>

#define STATUS_MATCH    0
#define STATUS_NOMATCH  1
#define STATUS_BADUSAGE 2
#define STATUS_ERROR    3

int
main(int argc, char **argv)
{
    int i;

    /* PID */
    pid_t mypid;
    pid_t pid;

    /* KVM openfiles() */
    kvm_t      *kd;
    const char *execf;
    const char *coref;
    char        buf[_POSIX2_LINE_MAX];

    /* KVM getprocs() */
    int                 nproc;
    struct kinfo_proc  *plist;
    struct kinfo_proc  *kp;

    /* KVM getargv() */
    char **strv;

    char *selected;

    execf = NULL;
    coref = _PATH_DEVNULL;

    mypid = getpid();

    /*
     * Retrieve the list of running processes from the kernel.
     */
    kd = kvm_openfiles(execf, coref, NULL, O_RDONLY, buf);
    if (kd == NULL)
        errx(STATUS_ERROR, "Cannot open kernel files (%s)", buf);

    /*
     * Use KERN_PROC_PROC instead of KERN_PROC_ALL, since we
     * just want processes and not individual kernel threads.
     */
    plist = kvm_getprocs(kd, KERN_PROC_PROC, 0, &nproc);
    if (plist == NULL) {
        errx(STATUS_ERROR, "Cannot get process list (%s)",
            kvm_geterr(kd));
    }
    printf("nproc=%d\n", nproc);

    /*
     * Allocate memory which will be used to keep track of the
     * selection.
     */
    if ((selected = malloc(nproc)) == NULL) {
        err(STATUS_ERROR, "Cannot allocate memory for %d processes",
            nproc);
    }
    memset(selected, 0, nproc);

    /*
     * Take the appropriate action for each matched process, if any.
     */
    for (i = 0, kp = plist; i < nproc; i++, kp++) {
        printf("comm=%s tdname=%s wmesg=%s login=%s lockname=%s emul=%s loginclass=%s",
            kp->ki_comm,
            kp->ki_tdname,
            kp->ki_wmesg,
            kp->ki_login,
            kp->ki_lockname,
            kp->ki_emul,
            kp->ki_loginclass);
        if ((strv = kvm_getargv(kd, kp, 0)) != NULL) {
            printf(" argv=%s", strv[0]);
        }
        printf("\n");
    }

    return 0;
}

Machine Learning Models

There are four major ML models:

Supervised Machine Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Random Forest
  • Gradient Boosted Trees
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Naive Bayes
  • Nearest Neighbor

Semi-supervised Machine Learning Algorithms

  • Unsupervised Machine Learning Algorithms
  • k-means clustering
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • PCA (Principal Component Analysis)
  • Association rule

Reinforcement Machine Learning Algorithms

  • Q-Learning
  • Temporal Difference (TD)
  • Monte-Carlo Tree Search (MCTS)
  • Asynchronous Actor-Critic Agents (A3C)

CM_QRM

  • Qualitätssicherung
  • Risikobeurteilung
  • Qualitätsmanagement
  • ISO 9001

ISO 26’000

ISO 26000
Was ist ISO 26000 – Kurz und kompakt erklärt
Corporate Social Responsibility (CSR): Praktische Perspektiven von Thomas Beschorner
Was ist eigentlich Corporate Social Responsibility (CSR)?
Little Green Bags: Was ist echte unternehmerische Nachhaltigkeit?

Demingkreis

EFQM – Das Kriterienmodell (Playlist)
Total Quality Management (TQM & EFQM) einfach erklärt – Qualitätsmanagement in Unternehmen
Frag den Prof: Change Management
Change Management – Brokkoli: Change Management einfach erklärt

Florian Frankl

QM-System für Einsteiger
Qualitätsmanagement

audit hilfe

ISO 9001
15 Pflichtelemente einer Managementbewertung nach ISO 9001

Schütte Quality Solutions

ISO 9001 Qualitätsmanagement

VOREST AG

Qualitätsmanagement – QM ISO 9001
Qualitätssicherung & Methoden
Prozessmanagement
Umweltmanagement ISO 14001 & Umweltschutz

MariusEbert

TQM und EFQM, 14.12.2019
Qualitätsprüfung, Fragen, 12.12.2019
Qualitätsanforderungen der Kunden, 11.12.2019
Qualitätsmanagement, Argumente dafür, Mind Map, 09.09.2019
Lieferantenbezogene Qualitätssicherung, wie?, 18.05.2018
Qualitätsaudit, Bestandteile, 01.04.2015
Instandhaltung u. Qualitätsmanagement, Zusammenhang, 25.03.2015
Qualitätslenkung in der Fertigung, Funktion, 25.03.2015
Ishikawa-Diagramm und Qualitätsmanagement, 09.03.2015
TQM, Maßnahmen, 09.03.2015
TQM, Grundgedanke u. Philosophie, 09.03.2015
QFD, Erfassung u. Umsetzung der Kundenwünsche, 07.03.2015
Qualitätsanforderungen des Kunden
Qualitätsprüfung, welche grundlegenden Fragen?
Magisches Qualitätsdreieck, Begriff
Total Quality Management (TQM), 22.09.2010
TQM und EFQM, 14.12.2019
Total Quality Management (TQM), Merkmale, Prüfungsfrage, 14.06.2012
Total Quality Management (TQM), Grundgedanke
ISO 9001, Regelkreis
Konzept zur Qualitätssicherung, 01.08.2012
Lean Management
Total Quality Management (TQM), 30.09.2011
Total Quality Management (TQM) und Kunde, 30.09.2011
Qualitätsmanagement Marius Ebert (Playlist)

Woche 9

Bow-Tie-Analyse
Ursache-Wirkungs-Diagramm, Ishikawa-Diagramm, Fishbone-Diagramm
FMEA (Failure Mode and Effects Analysis)
PAAG-Verfahren, wie HAZOP-Verfahren (von englisch Hazard and Operability)
Hazard and Operability Study (HAZOP)

  • FMEA
  • HAZOP
  • Ishikawa
  • Fishbone
  • Fault Tree Analysis

Articles

Do companies get the most out of Hazard & Operability (HazOp) analysis?

YouTube

Ishikawa Diagramm Erklärung & Beispiel (Unternehmensführung Fachwirt IHK) -Fischgrätendiagramm
Wie funktioniert das Ishikawa Diagramm? – Ishikawa Beispiel & Erklärung

Woche 10

Fehlerbaumanalyse, Fault Tree Analysis (FTA)

YouTube

Erklärvideo: Risikomanagement 2.0
Folge 14 – Risiken im Projekt managen (Teil 1)
Risikomanagement, Teil 1: Risikopolitik

Woche 15

Developing Effective Bow Tie Diagrams Webinar, ABBConsultingUK
Learning with Risktec

Risk Reduction & ALARP (for High Hazard Industries), 16.01.2019
Risk & Safety Management: Making Training Count with ALARP, Bowtie and more, 21.08.2018

Does risk assessment reveal risk?
Finding things to worry about
Why investigate – part 1
Why investigate – Part 2
Risk Acceptance 1 – Introduction
Risk Acceptance 2 – Absolute risk
Risk Acceptance 3 – Relative risk
Risk Acceptance 4 – Trade-offs
Risk Acceptance 5 – ALARP
Risk Acceptance 6 – Implied Acceptability
Risk Acceptance 7 – Fiat
What is safety

Risikobeurteilung (Playlist)

Wie denke ich strategisch? Strategie als Sehen nach Mintzberg
Management & Unternehmensführung | Operative & Strategische Unternehmensführung | BWL
Einführung in effektives und effizientes Risikomanagement nach DIN ISO 31000

Mathematik

Measuring Reliability
08 Exponentialverteilungen Einführung
09 Exponentialverteilungen Definition und Dichtefunktion
Zufallsvariable, Massenfunktion, Dichtefunktion und Verteilungsfunktion
Dichtefunktion: Wie rechnet man damit? Wie zeichnet man eine Dichte? Was sind die Eigenschaften?
Rechnen mit einer Dichtefunktion: Erwartungswert, Median, Verteilungsfunktion bestimmen
4.5.7 Mean Time to Failure: Video

Victor Lavrenko: YouTube

Naive Bayes Classifier

Naive Bayes Classifier

  • IAML5.1: Overview
  • IAML5.2: Bayesian classification
  • IAML5.3: Class model and the prior
  • IAML5.4: Role of denominator in Naive Bayes
  • IAML5.5: Probabilistic classifiers: generative vs discriminative
  • IAML5.6: Independence assumption in Naive Bayes
  • IAML5.7: Mutual independence vs conditional independence
  • IAML5.8: Naive Bayes for real-valued data
  • IAML5.9: Gaussian Naive Bayes classifier
  • IAML5.10: Naive Bayes decision boundary
  • IAML5.11: Example where Naive Bayes fails
  • IAML5.12: Naive Bayes for spam detection
  • IAML5.13: The zero-frequency problem
  • IAML5.14: Missing values in Naive Bayes

Decision Tree Learning

Decision Tree Learning

  • IAML7.1 Decision Trees: an introduction
  • IAML7.2 Decision tree example
  • IAML7.3 Quinlan’s ID3 algorithm
  • IAML7.4 Decision tree: split purity
  • IAML7.5 Decision tree entropy
  • IAML7.6 Information gain
  • IAML7.7 Overfitting in decision trees
  • IAML7.8 Decision tree pruning
  • IAML7.9 Information gain ratio
  • IAML7.10 Decision trees are DNF formulas
  • IAML7.11 Decision trees and real-valued data
  • IAML7.12 Decision tree regression
  • IAML7.13 Pros and cons of decision trees
  • IAML7.14 Random forest algorithm
  • IAML7.15 Summary

Generalization and Evaluation

Generalization and Evaluation

  • IAML8.1 Generalization in machine learning
  • IAML8.2 Overfitting and underfitting
  • IAML8.3 Examples of overfitting and underfitting
  • IAML8.4 How to control overfitting
  • IAML8.5 Generalization error
  • IAML8.6 Estimating the generalization error
  • IAML8.7 Confidence interval for generalization
  • IAML8.8 Why we need validation sets
  • IAML8.9 Cross-validation
  • IAML8.10 Leave-one-out cross-validation
  • IAML8.11 Stratified sampling
  • IAML8.12 Evaluating classification and regression
  • IAML8.13 False positives and false negatives
  • IAML8.14 Classification error and accuracy
  • IAML8.15 When classification error is wrong
  • IAML8.16 Recall, precision, miss and false alarm
  • IAML8.17 Classification cost and utility
  • IAML8.18 Receiver Operating Characteristic (ROC) curve
  • IAML8.19 Evaluating regression: MSE, MAE, CC
  • IAML8.20 Mean squared error and outliers
  • IAML8.21 Mean absolute error (MAE)
  • IAML8.22 Correlation coefficient

k-Nearest Neighbor Algorithm

k-Nearest Neighbor Algorithm

  • kNN.1 Overview
  • kNN.2 Intuition for the nearest-neighbor method
  • kNN.3 Voronoi cells and decision boundary
  • kNN.4 Sensitivity to outliers
  • kNN.5 Nearest-neighbor classification algorithm
  • kNN.6 MNIST digit recognition
  • kNN.7 Nearest-neighbor regression algorithm
  • kNN.8 Nearest-neighbor regression example
  • kNN.9 Number of nearest neighbors to use
  • kNN.10 Similarity / distance measures
  • kNN.11 Breaking ties between nearest neighbors
  • kNN.12 Parzen windows, kernels and SVM
  • kNN.13 Pros and cons of nearest-neighbor methods
  • kNN.14 Computational complexity of finding nearest-neighbors
  • kNN.15 K-d tree algorithm
  • kNN.16 Locality sensitive hashing (LSH)
  • kNN.17 Inverted index

K-means Clustering

K-means Clustering

  • Clustering 1: monothetic vs. polythetic
  • Clustering 2: soft vs. hard clustering
  • Clustering 3: overview of methods
  • Clustering 4: K-means clustering: how it works
  • Clustering 5: K-means objective and convergence
  • Clustering 6: how many clusters?
  • Clustering 7: intrinsic vs. extrinsic evaluation
  • Clustering 8: alignment and pair-based evaluation
  • Clustering 9: image representation

IR15 Web Search and PageRank

IR15 Web Search and PageRank

  • Web search 1: more data = higher precision
  • Web search 2: big data beats clever algorithms
  • Web search 3: introduction to PageRank
  • Web search 4: PageRank algorithm: how it works
  • Web search 5: PageRank at convergence
  • Web search 6: PageRank using MapReduce
  • Web search 7: sink nodes in PageRank
  • Web search 8: hubs and authorities
  • Web search 9: link spam
  • Web search 10: anchor text

IR7 Inverted Indexing

IR7 Inverted Indexing

  • Indexing 1: what makes google fast
  • Indexing 2: inverted index
  • Indexing 3: sparseness and linear merge
  • Indexing 4: phrases and proximity
  • Indexing 5: XML, structure and metadata
  • Indexing 6: delta encoding (compression)
  • Indexing 7: v-byte encoding (compression)
  • Indexing 8: doc-at-a-time query execution
  • Indexing 9: doc-at-a-time worst case
  • Indexing 10: term-at-a-time query execution
  • Indexing 11: query execution tradeoffs
  • Indexing 12: expected cost of execution
  • Indexing 13: heuristics for faster search
  • Indexing 14: structured query execution
  • Indexing 15: index construction
  • Indexing 16: MapReduce
  • Indexing 17: distributed search

IR13 Evaluating Search Engines

IR13 Evaluating Search Engines

  • Evaluation 1: overview
  • Evaluation 2: research hypotheses
  • Evaluation 3: effectiveness vs. efficiency
  • Evaluation 4: Cranfield paradigm
  • Evaluation 5: relevance judgments
  • Evaluation 6: precision and recall
  • Evaluation 7: why we can’t use accuracy
  • Evaluation 8: F-measure
  • Evaluation 9: when recall/precision is misleading
  • Evaluation 10: recall and precision over ranks
  • Evaluation 11: interpolated recall-precision plot
  • Evaluation 12: mean average precision
  • Evaluation 13: MAP vs NDCG
  • Evaluation 14: query logs and click deviation
  • Evaluation 15: binary preference and Kendall tau
  • Evaluation 16: hypothesis testing
  • Evaluation 17: statistical significance test
  • Evaluation 18: the sign test
  • Evaluation 19: training / testing splits

IR10 Crawling the Web

IR10 Crawling the Web

  • Web crawling 1: sources of data
  • Web crawling 2: blogs, tweets, news feeds
  • Web crawling 3: the algorithm
  • Web crawling 4: inside an HTTP request
  • Web crawling 5: robots.txt
  • Web crawling 6: keeping index fresh

Parwiz Forogh: Qt, QML, Charts, OpenGL

Qt5 C++ GUI Programming

Qt5 C++ GUI Programming

  • Qt5 C++ Tutorial Installation With Visual Studio 2015
  • Qt5 C++ Tutorial Hello World Console Application #2
  • Qt5 C++ Tutorial First GUI Application Window #3
  • Qt5 C++ Signal And Slots With Practical Examples #4
  • Qt5 C++ Creating Layouts #5
  • Qt5 C++ Adding CSS Styles #6
  • Qt5 C++ PushButton #7
  • Qt5 C++ Create CheckBox #8
  • Qt5 C++ Creating RadioButton #9
  • Qt5 C++ ComboBox With Signal And Slots (programming) #10
  • Qt5 C++ Creating ListWidget Application #11
  • Qt5 C++ MessageBox Practical Example #12
  • Qt5 C++ Creating Menu And Toolbar QMenu #13
  • Qt5 C++ Creating Print Dialog (QPrintDialog) #14
  • Qt5 C++ Creating Font Dialog (QFontDialog) #15
  • Qt5 C++ Creating Color Dialog (QColorDialog) #16
  • Qt5 C++ Creating File Dialog (QFileDialog) #17
  • Qt5 C++ Progressbar And Slider (QProgressbar And QSlider) #18
  • Qt5 C++ Creating Animations (QPropertyAnimation) #19
  • Qt5 C++ Controlling Animation With Easing CurveQPropertyAnimation & QEasingCurve #20
  • Qt5 C++ Creating Parallel Animation Group QParallelAnimationGroup #21
  • Qt5 C++ Creating Sequential Animation Group (QSequentialAnimationGroup) #22
  • Qt5 C++ How To Create State Machine In Qt (QStateMachine, QEventTransition) #23
  • Qt5 C++ Drawing Text And Line (QPainter, QPen, QTextDocument) In Qt #24
  • Qt5 C++ Drawing Rectangle (QPainter, QPen, QBrush) In Qt #25
  • Qt5 C++ Drawing Ellipse (QPainter, QPen, QBrush) In Qt #26
  • Qt5 C++ Gradients (QLinearGradients, QRadialGradient, QConicalGradient) #27
  • Qt5 C++ Connecting Qt Application To Mysql Database #28
  • Qt5 C++ How To Connect Qt Application To Sqlite3 Database #29
  • Qt5 C++ Register & Login System With Mysql Main Design Part One #30
  • Qt5 C++ Register & Login System With Mysql Main Design Part Two #31
  • Qt5 C++ Register & Login System Inserting Users Data In To Mysql Part Three #32
  • Qt5 C++ Register & Login System User Login Part Four (Mysql Database) #33
  • Qt5 C++ QSqlQueryModel With Mysql Database & QTableView #34
  • Qt5 C++ QSqlTableModel With Mysql Database & QTableView #35
  • Qt5 C++ Creating BarChart With QtChart | C++ GUI Tutorial
  • Qt5 C++ Creating LineChart With QtChart | C++ GUI Tutorial
  • Qt5 C++ Creating PieChart With QtChart | C++ GUI Tutorial
  • Qt5 C++ Creating DonutChart With QtChart

Developing QtQuick QML Applications in Qt5

Developing QtQuick QML Applications in Qt5

  • QtQuick QML Introduction #1
  • QtQuick QML First Window #2
  • QtQuick QML Our First Rectangle #3
  • QtQuick QML MouseArea #4
  • QtQuick QML Properties #5
  • QtQuick QML Scripting #6
  • QtQuick QML Image Element #7

Qt5 C++ Charts

Qt5 C++ Charts

  • Qt5 C++ Creating BarChart With QtChart
  • Qt5 C++ Creating LineChart With QtChart
  • Qt5 C++ Creating PieChart With QtChart
  • Qt5 C++ Creating DonutChart With QtChart

Qt5 C++ OPENGL PROGRAMMING (OpenGL 2)

Qt5 C++ OPENGL PROGRAMMING

  • 1 Qt5 C++ Opengl Tutorial Creating Window
  • 2 Qt5 C++ Opengl Tutorial Drawing Quads
  • 3 Qt5 C++ Opengl Tutorial Drawing Traingle And Coloring
  • 4 Qt5 C++ Opengl Tutorial Rendering 3D Shape In Screen

Khan Academy YouTube

Multivariable calculus

Multivariable calculus

  • Multivariable functions | Multivariable calculus |
  • Representing points in 3d | Multivariable calculus |
  • Introduction to 3d graphs | Multivariable calculus |
  • Interpreting graphs with slices | Multivariable calculus |
  • Contour plots | Multivariable calculus |
  • Parametric curves | Multivariable calculus |
  • Parametric surfaces | Multivariable calculus |
  • Vector fields, introduction | Multivariable calculus |
  • Fluid flow and vector fields | Multivariable calculus |
  • 3d vector fields, introduction | Multivariable calculus |
  • 3d vector field example | Multivariable calculus |
  • Transformations, part 1 | Multivariable calculus |
  • Transformations, part 2 | Multivariable calculus |
  • Transformations, part 3 | Multivariable calculus |
  • Partial derivatives, introduction
  • Partial derivatives and graphs
  • Formal definition of partial derivatives
  • Symmetry of second partial derivatives
  • Gradient
  • Gradient and graphs
  • Directional derivative
  • Directional derivative, formal definition
  • Directional derivatives and slope
  • Why the gradient is the direction of steepest ascent
  • Gradient and contour maps
  • Position vector valued functions | Multivariable Calculus |
  • Derivative of a position vector valued function | Multivariable Calculus |
  • Differential of a vector valued function | Multivariable Calculus |
  • Vector valued function derivative example | Multivariable Calculus |
  • Multivariable chain rule
  • Multivariable chain rule intuition
  • Vector form of the multivariable chain rule
  • Multivariable chain rule and directional derivatives
  • More formal treatment of multivariable chain rule
  • Curvature intuition
  • Curvature formula, part 1
  • Curvature formula, part 2
  • Curvature formula, part 3
  • Curvature formula, part 4
  • Curvature formula, part 5
  • Curvature of a helix, part 1
  • Curvature of a helix, part 2
  • Curvature of a cycloid
  • Computing the partial derivative of a vector-valued function
  • Partial derivative of a parametric surface, part 1
  • Partial derivative of a parametric surface, part 2
  • Partial derivatives of vector fields
  • Partial derivatives of vector fields, component by component
  • Divergence intuition, part 1
  • Divergence intuition, part 2
  • Divergence formula, part 1
  • Divergence formula, part 2
  • Divergence example
  • Divergence notation
  • 2d curl intuition
  • 2d curl formula
  • 2d curl example
  • 2d curl nuance
  • Describing rotation in 3d with a vector
  • 3d curl intuition, part 1
  • 3d curl intuition, part 2
  • 3d curl formula, part 1
  • 3d curl formula, part 2
  • 3d curl computation example
  • Laplacian intuition
  • Laplacian computation example
  • Explicit Laplacian formula
  • Harmonic Functions
  • Jacobian prerequisite knowledge
  • Local linearity for a multivariable function
  • The Jacobian matrix
  • Computing a Jacobian matrix
  • The Jacobian Determinant
  • What is a tangent plane
  • Controlling a plane in space
  • Computing a tangent plane
  • Local linearization
  • What do quadratic approximations look like
  • Quadratic approximation formula, part 1
  • Quadratic approximation formula, part 2
  • Quadratic approximation example
  • The Hessian matrix
  • Expressing a quadratic form with a matrix
  • Vector form of multivariable quadratic approximation
  • Multivariable maxima and minima
  • Saddle points
  • Warm up to the second partial derivative test
  • Second partial derivative test
  • Second partial derivative test intuition
  • Second partial derivative test example, part 1
  • Second partial derivative test example, part 2
  • Constrained optimization introduction
  • Lagrange multipliers, using tangency to solve constrained optimization
  • Finishing the intro lagrange multiplier example
  • Lagrange multiplier example, part 1
  • Lagrange multiplier example, part 2
  • The Lagrangian
  • Meaning of Lagrange multiplier
  • Proof for the meaning of Lagrange multipliers | Multivariable Calculus |
  • Introduction to the line integral | Multivariable Calculus |
  • Line integral example 1 | Line integrals and Green’s theorem | Multivariable Calculus |
  • Line integral example 2 (part 1) | Multivariable Calculus |
  • Line integral example 2 (part 2) | Multivariable Calculus |
  • Line integrals and vector fields | Multivariable Calculus |
  • Using a line integral to find the work done by a vector field example |
  • Parametrization of a reverse path |
  • Scalar field line integral independent of path direction | Multivariable Calculus |
  • Vector field line integrals dependent on path direction | Multivariable Calculus |
  • Path independence for line integrals | Multivariable Calculus |
  • Closed curve line integrals of conservative vector fields | Multivariable Calculus |
  • Example of closed line integral of conservative field | Multivariable Calculus |
  • Second example of line integral of conservative vector field | Multivariable Calculus |
  • Double integral 1 | Double and triple integrals | Multivariable Calculus |
  • Double integrals 2 | Double and triple integrals | Multivariable Calculus |
  • Double integrals 3 | Double and triple integrals | Multivariable Calculus |
  • Double integrals 4 | Double and triple integrals | Multivariable Calculus |
  • Double integrals 5 | Double and triple integrals | Multivariable Calculus |
  • Double integrals 6 | Double and triple integrals | Multivariable Calculus |
  • Triple integrals 1 | Double and triple integrals | Multivariable Calculus |
  • Triple integrals 2 | Double and triple integrals | Multivariable Calculus |
  • Triple integrals 3 | Double and triple integrals | Multivariable Calculus |
  • Introduction to parametrizing a surface with two parameters | Multivariable Calculus |
  • Determining a position vector-valued function for a parametrization of two parameters |
  • Partial derivatives of vector-valued functions | Multivariable Calculus |
  • Introduction to the surface integral | Multivariable Calculus |
  • Example of calculating a surface integral part 1 | Multivariable Calculus |
  • Example of calculating a surface integral part 2 | Multivariable Calculus |
  • Example of calculating a surface integral part 3 | Multivariable Calculus |
  • Surface integral example part 1: Parameterizing the unit sphere |
  • Surface integral example part 2: Calculating the surface differential |
  • Surface integral example part 3: The home stretch | Multivariable Calculus |
  • Surface integral ex2 part 1: Parameterizing the surface | Multivariable Calculus |
  • Surface integral ex2 part 2: Evaluating integral | Multivariable Calculus |
  • Surface integral ex3 part 1: Parameterizing the outside surface |
  • Surface integral ex3 part 2: Evaluating the outside surface | Multivariable Calculus |
  • Surface integral ex3 part 3: Top surface | Multivariable Calculus |
  • Surface integral ex3 part 4: Home stretch | Multivariable Calculus |
  • Conceptual understanding of flux in three dimensions | Multivariable Calculus |
  • Constructing a unit normal vector to a surface | Multivariable Calculus |
  • Vector representation of a surface integral | Multivariable Calculus |
  • Green’s theorem proof part 1 | Multivariable Calculus |
  • Green’s theorem proof (part 2) | Multivariable Calculus |
  • Green’s theorem example 1 | Multivariable Calculus |
  • Green’s theorem example 2 | Multivariable Calculus |
  • Constructing a unit normal vector to a curve | Multivariable Calculus |
  • 2D divergence theorem | Line integrals and Green’s theorem | Multivariable Calculus |
  • Conceptual clarification for 2D divergence theorem | Multivariable Calculus |
  • Stokes’ theorem intuition | Multivariable Calculus |
  • Green’s and Stokes’ theorem relationship | Multivariable Calculus |
  • Orienting boundary with surface | Multivariable Calculus |
  • Orientation and stokes | Multivariable Calculus |
  • Conditions for stokes theorem | Multivariable Calculus |
  • Stokes example part 1 | Multivariable Calculus |
  • Stokes example part 2: Parameterizing the surface | Multivariable Calculus |
  • Stokes example part 3: Surface to double integral | Multivariable Calculus |
  • Stokes example part 4: Curl and final answer | Multivariable Calculus |
  • Evaluating line integral directly – part 1 | Multivariable Calculus |
  • Evaluating line integral directly – part 2 | Multivariable Calculus |
  • 3D divergence theorem intuition | Divergence theorem | Multivariable Calculus |
  • Divergence theorem example 1 | Divergence theorem | Multivariable Calculus |
  • Stokes’ theorem proof part 1 | Multivariable Calculus |
  • Stokes’ theorem proof part 2 | Multivariable Calculus |
  • Stokes’ theorem proof part 3 | Multivariable Calculus |
  • Stokes’ theorem proof part 4 | Multivariable Calculus |
  • Stokes’ theorem proof part 5 | Multivariable Calculus |
  • Stokes’ theorem proof part 6 | Multivariable Calculus |
  • Stokes’ theorem proof part 7 | Multivariable Calculus |
  • Type I regions in three dimensions | Divergence theorem | Multivariable Calculus |
  • Type II regions in three dimensions | Divergence theorem | Multivariable Calculus |
  • Type III regions in three dimensions | Divergence theorem | Multivariable Calculus |
  • Divergence theorem proof (part 1) | Divergence theorem | Multivariable Calculus |
  • Divergence theorem proof (part 2) | Divergence theorem | Multivariable Calculus |
  • Divergence theorem proof (part 3) | Divergence theorem | Multivariable Calculus |
  • Divergence theorem proof (part 4) | Divergence theorem | Multivariable Calculus |
  • Divergence theorem proof (part 5) | Divergence theorem | Multivariable Calculus |

3Blue1Brown YouTube

Neural networks

Neural networks

  • But what is a Neural Network? | Deep learning, chapter 1
  • Gradient descent, how neural networks learn | Deep learning, chapter 2
  • What is backpropagation really doing? | Deep learning, chapter 3
  • Backpropagation calculus | Deep learning, chapter 4

Essence of calculus

Essence of calculus

  • The Essence of Calculus, Chapter 1
  • The paradox of the derivative | Essence of calculus, chapter 2
  • Derivative formulas through geometry | Essence of calculus, chapter 3
  • Visualizing the chain rule and product rule | Essence of calculus, chapter 4
  • What’s so special about Euler’s number e? | Essence of calculus, chapter 5
  • Implicit differentiation, what’s going on here? | Essence of calculus, chapter 6
  • Limits, L’Hopital’s rule, and epsilon delta definitions | Essence of calculus, chapter 7
  • Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8
  • What does area have to do with slope? | Essence of calculus, chapter 9
  • Higher order derivatives | Essence of calculus, chapter 10
  • Taylor series | Essence of calculus, chapter 11
  • What they won’t teach you in calculus

Differential equations

Differential equations

  • Differential equations, studying the unsolvable | DE1
  • But what is a partial differential equation? | DE2
  • Solving the heat equation | DE3
  • But what is a Fourier series? From heat flow to circle drawings | DE4
  • Understanding e to the i pi in 3.14 minutes | DE5

Essence of linear algebra

Essence of linear algebra

  • Vectors, what even are they? | Essence of linear algebra, chapter 1
  • Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2
  • Linear transformations and matrices | Essence of linear algebra, chapter 3
  • Matrix multiplication as composition | Essence of linear algebra, chapter 4
  • Three-dimensional linear transformations | Essence of linear algebra, chapter 5
  • The determinant | Essence of linear algebra, chapter 6
  • Inverse matrices, column space and null space | Essence of linear algebra, chapter 7
  • Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8
  • Dot products and duality | Essence of linear algebra, chapter 9
  • Cross products | Essence of linear algebra, Chapter 10
  • Cross products in the light of linear transformations | Essence of linear algebra chapter 11
  • Cramer’s rule, explained geometrically | Essence of linear algebra, chapter 12
  • Change of basis | Essence of linear algebra, chapter 13
  • Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
  • Abstract vector spaces | Essence of linear algebra, chapter 15

Global Health with Greg Martin

Statistics made easy ! ! ! Learn about the t-test, the chi square test, the p value and more

Global Health

Global Health

Videos:

  • How to get Global Health Field Experience – getting ready for your career in public health
  • How to get Global Health Field Experience – getting ready for your career in public health
  • The Social Determinants of Health. A Public Health framework.
  • Gender-Based Violence and Violence Against Women – a public health issue
  • How to write a scientific paper
  • How to write a literature review
  • Epidemiological transition
  • Pandemics – a worrying global public health threat
  • Outbreaks – investigation and control
  • Universal Health Coverage explained
  • Epidemiology the backbone of public health
  • Management and Public Health
  • 6 ways that Gender affects Health
  • Health Systems
  • Health Economics
  • Finding the right job in Global Health
  • Finding a job in Global Health
  • Research Methods – Introduction
  • President Trump and Global Health – what are the issues?
  • Climate change and public health – why Trump should NOT have pulled out of the Paris Agreement
  • Justice, Equality and Global Health
  • Global Health Ethics – A Framework for Thinking
  • Global Health Ethics (understudying right and wrong)
  • Global Health and Human Rights
  • Exploitation and Global Health
  • R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
  • Tedros – the new Director General of the World Health Organization
  • Know how to interpret an epidemic curve?
  • Careers in Global Health – identify your area of interest
  • Global Health Careers – Your Role
  • Skills and Competencies for Public Health
  • Jobs in Global Health – who’s hiring
  • The State of Global Health
  • How to get funding for your public health project.
  • Get involved and support this global health channel

Working in Global Health

Working in Global Health

  • How to get Global Health Field Experience – getting ready for your career in public health
  • Careers in Global Health – identify your area of interest
  • Global Health Careers – Your Role
  • Skills and Competencies for Public Health
  • Jobs in Global Health – who’s hiring
  • Finding the right job in Global Health
  • Getting a job at the World Health Organization
  • Consulting jobs Global Health – how to get work
  • Finding a job in Global Health
  • What is public health?
  • Web pages for jobs in Global Health
  • Careers in Global Health – a panel discussion
  • How to get funding for your public health project.
  • Writing a grant application for public health projects
  • The Global Fund’s new funding model
  • Entrepreneurship and Innovation in Public Health
  • Finding a job at a UN agency – This Week in Global Health
  • Access to Medicines (part IV). How to get a job!
  • Apply and interview for jobs in Global Health
  • 19 Videos18.018 AufrufeZuletzt am 30.09.2019 aktualisiert