On Windows this is a named pipe and on Unix this is a local domain socket.
QLocalServer Class
QLocalSocket Class

QTcpServer Class
QTcpServer Class

Fortune Server Example

Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)

Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)

• Welcome (Deep Learning Specialization C1W1L01)
• What is a Neural Network? (C1W1L02)
• Supervised Learning with a Neural Network (C1W1L03)
• Why is deep learning taking off? (C1W1L04)
• About This Course (C1W1L05)
• Course Resources (C1W1L06)
• Binary Classification (C1W2L01)
• Logistic Regression (C1W2L02)
• Logistic Regression Cost Function (C1W2L03)
• Gradient Descent (C1W2L04)
• Derivatives (C1W2L05)
• More Derivative Examples (C1W2L06)
• Computation Graph (C1W2L07)
• Derivatives With Computation Graphs (C1W2L08)
• Logistic Regression Gradient Descent (C1W2L09)
• Gradient Descent on m Examples (C1W2L10)
• Vectorization (C1W2L11)
• More Vectorization Examples (C1W2L12)
• Vectorizing Logistic Regression (C1W2L13)
• Vectorizing Logistic Regression’s Gradient Computation (C1W2L14)
• Broadcasting in Python (C1W2L15)
• A Note on Python/Numpy Vectors (C1W2L16)
• Quick Tour of Jupyter/iPython Notebooks (C1W2L17)
• Explanation of Logistic Regression’s Cost Function (C1W2L18)
• Neural Network Overview (C1W3L01)
• Neural Network Representations (C1W3L02)
• Computing Neural Network Output (C1W3L03)
• Vectorizing Across Multiple Examples (C1W3L04)
• Explanation For Vectorized Implementation (C1W3L05)
• Activation Functions (C1W3L06)
• Why Non-linear Activation Functions (C1W3L07)
• Derivatives Of Activation Functions (C1W3L08)
• Gradient Descent For Neural Networks (C1W3L09)
• Backpropagation Intuition (C1W3L10)
• Random Initialization (C1W3L11)
• Deep L-Layer Neural Network (C1W4L01)
• Forward Propagation in a Deep Network (C1W4L02)
• Getting Matrix Dimensions Right (C1W4L03)
• Why Deep Representations? (C1W4L04)
• Building Blocks of a Deep Neural Network (C1W4L05)
• Forward and Backward Propagation (C1W4L06)
• Parameters vs Hyperparameters (C1W4L07)
• What does this have to do with the brain? (C1W4L08)

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (Course 2 of the Deep Learning Specialization)

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (Course 2 of the Deep Learning Specialization)

• Train/Dev/Test Sets (C2W1L01)
• Bias/Variance (C2W1L02)
• Basic Recipe for Machine Learning (C2W1L03)
• Regularization (C2W1L04)
• Why Regularization Reduces Overfitting (C2W1L05)
• Dropout Regularization (C2W1L06)
• Understanding Dropout (C2W1L07)
• Other Regularization Methods (C2W1L08)
• Normalizing Inputs (C2W1L09)
• Vanishing/Exploding Gradients (C2W1L10)
• Weight Initialization in a Deep Network (C2W1L11)
• Numerical Approximations of Gradients (C2W1L12)
• Gradient Checking (C2W1L13)
• Gradient Checking Implementation Notes (C2W1L14)
• Mini Batch Gradient Descent (C2W2L01)
• Understanding Mini-Batch Gradient Dexcent (C2W2L02)
• Exponentially Weighted Averages (C2W2L03)
• Understanding Exponentially Weighted Averages (C2W2L04)
• Bias Correction of Exponentially Weighted Averages (C2W2L05)
• Gradient Descent With Momentum (C2W2L06)
• RMSProp (C2W2L07)
• Adam Optimization Algorithm (C2W2L08)
• Learning Rate Decay (C2W2L09)
• Tuning Process (C2W3L01)
• Using an Appropriate Scale (C2W3L02)
• Hyperparameter Tuning in Practice (C2W3L03)
• Normalizing Activations in a Network (C2W3L04)
• Fitting Batch Norm Into Neural Networks (C2W3L05)
• Why Does Batch Norm Work? (C2W3L06)
• Batch Norm At Test Time (C2W3L07)
• Softmax Regression (C2W3L08)
• Training Softmax Classifier (C2W3L09)
• The Problem of Local Optima (C2W3L10)
• TensorFlow (C2W3L11)

Structuring Machine Learning Projects (Course 3 of the Deep Learning Specialization)

Structuring Machine Learning Projects (Course 3 of the Deep Learning Specialization)

• Improving Model Performance (C3W1L01)
• Orthogonalization (C3W1L02 )
• Single Number Evaluation Metric (C3W1L03)
• Satisficing and Optimizing Metrics (C3W1L04)
• Train/Dev/Test Set Distributions (C3W1L05)
• Sizeof Dev and Test Sets (C3W1L06)
• When to Change Dev/Test Sets (C3W1L07)
• C3W1L08 WhyHumanLevelPerformance
• Avoidable Bias (C3W1L09)
• Understanding Human-Level Performance? (C3W1L10)
• Surpassing Human-Level Performance (C3W1L11)
• Improving Model Performance (C3W1L12)
• Carrying Out Error Analysis (C3W2L01)
• Cleaning Up Incorrectly Labelled Data (C3W2L02)
• Build First System Quickly, Then Iterate (C3W2L03)
• Training and Testing on Different Distributions (C3W2L04)
• Bias and Variance With Mismatched Data (C3W2L05)
• Addressing Data Mismatch (C3W2L06)
• Transfer Learning (C3W2L07)
• Multitask Learning (C3W2L08)
• What is end-to-end deep learning? (C3W2L09)
• Whether to Use End-To-End Deep Learning (C3W2L10)

Convolutional Neural Networks (Course 4 of the Deep Learning Specialization)

Convolutional Neural Networks (Course 4 of the Deep Learning Specialization)

• C4W1L01 Computer Vision
• C4W1L02 Edge Detection Examples
• C4W1L03 More Edge Detection
• C4W1L04 Padding
• C4W1L05 Strided Convolutions
• C4W1L06 Convolutions Over Volumes
• C4W1L07 One Layer of a Convolutional Net
• C4W1L08 Simple Convolutional Network Example
• C4W1L09 Pooling Layers
• C4W1L10 CNN Example
• C4W1L11 Why Convolutions
• C4W2L01 Why look at case studies?
• C4W2L02 Classic Network
• C4W2L03 Resnets
• C4W2L04 Why ResNets Work
• C4W2L05 Network In Network
• C4W2L06 Inception Network Motivation
• C4W2L07 Inception Network
• C4W2L08 Using Open Source Implementation
• C4W2L09 Transfer Learning
• C4W2L10 Data Augmentation
• C4W2L11 State of Computer Vision
• C4W3L01 Object Localization
• C4W3L02 Landmark Detection
• C4W3L03 Object Detection
• C4W3L04 Convolutional Implementation Sliding Windows
• C4W3L06 Intersection Over Union
• C4W3L07 Nonmax Suppression
• C4W3L08 Anchor Boxes
• C4W3L09 YOLO Algorithm
• C4W3L10 Region Proposals
• C4W4L01 What is face recognition
• C4W4L02 One Shot Learning
• C4W4L03 Siamese Network
• C4W4L04 Triplet loss
• C4W4L05 Face Verification
• C4W4L06 What is neural style transfer?
• C4W4L07 What are deep CNs learning?
• C4W4L08 Cost Function
• C4W4L09 Content Cost Function
• C4W4L10 Style Cost Function
• C4W4L11 1D and 3D Generalizations

Sequence Models (Course 5 of the Deep Learning Specialization)

Sequence Models (Course 5 of the Deep Learning Specialization)

• C5W3L01 Basic Models
• C5W3L02 Picking the most likely sentence
• C5W3L06 Bleu Score (Optional)
• C5W3L07 Attention Model Intuition
• C5W3L08 Attention Model
• C5W3L09 SpeechRecog

Skyentific, EPFL

Facebook
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Skyentific (Playlist)

Robotic Arm

• Small affordable 6DoF 3D printed robotic arm
• Is it the best DIY 3D printed robotic arm? Precision, speed and payload test.
• 6DoF mostly 3D Printed Robot Arm (Part 1)
• 6DoF mostly 3D Printed Robot Arm (Part 2)
• 6DoF mostly 3D Printed Robot Arm (Part 3)
• 6DoF mostly 3D Printed Robot Arm (Part 4)
• 6DoF mostly 3D Printed Robot Arm (Part 5) New electronics!
• 6DoF mostly 3D Printed Robot Arm (Part 6) Arduino code!
• Robot Arm programmed and controlled with SmartPhone (Bluetooth)
• Three examples of using Robot Arm (Serious stuff)
• PID demo

Brushless motor and ODrive

• TUTORIAL: ODrive Brushless Motor with Raspberry Pi and Arduino
• DIY: High Five Robot (Brusless Motor + ODrive + Arduino)
• High Five Robot vs Egg and Tomato (Brushless motor + ODrive + Arduino)
• Fast Force Feedback for Brushless Motor (ODrive + Arduino)
• DIY: Gearbox for Brushless Motor (ODrive + Arduino)

7DoF Robot Arm

• Another 7-axis (7DoF) Brushless Robot Arm (part 3)
• Side Project: Simple Axis 1 (Alternative Easy Axis1)
• Another 7-axis (7DoF) Brushless Robot Arm (part 4)

11. Digitaltechnik und digitale Mischpulte

11. Digitaltechnik und digitale Mischpulte

• 11.1 Was ist Digitaltechnik?
• 11.2 Zahlensysteme
• 11.3 Rechnen mit digitalen Audiodaten
• 11.4 Abtastung / Zeitquantisierung
• 11.5 Spannungsquantisierung
• 11.6 Digitale Pegel
• 11.7 Codierung
• 11.8 Fehlerkorrektur und Interleaving
• 11.9 Digitale Audiotechnik im Überblick
• 11.10 Übertragung von digitalem Audio
• 11.11 Datenkompression
• 11.12 Hohe Abtastraten und Bittiefen
• 11.13 Yamaha 01V96 – Teil 1
• 11.14 Yamaha 01V96 – Teil 2

Fourier-Analyse, Fourierreihen, Fouriertransformation

Fourier-Analyse, Fourierreihen, Fouriertransformation

• Fourierreihe, Übersicht, Fourier-Analyse, Reihenentwicklung, Unimathematik
• Fourier-Analyse, Vorbereitung, Grundlagen, Vokabeln, Übersicht, Unimathematik
• Fourierreihen, Vorbereitung, Signal, Zeit, Frequenz, Fourier-Analyse
• Fourierreihen, Vorbereitung, Sinus-/Kosinusgrundlagen, trigonometrische Funktionen
• Fourierreihen, Vorbereitung, Transformation von Funktionen, Fourier-Analyse
• Fourierreihe als Linearkombination, Fourier-Analyse, Unimathematik
• Fourierreihe als Linearkombination, komplex mit Euler, Fourier-Analyse
• Fouriertransformation, ganz grobe Übersicht, Fourier-Analyse, Unimathematik
• Fourierreihe, Fouriertransformation, Schnellübersicht, Fourier-Analyse
• Indexschreibweisen bei Fourier und Frequenz, Fourier-Analyse, Fourierreihe
• Periode, Frequenz, Trigonometrische Funktion, Fourier-Analyse, Fourierreihe
• Fourierreihe als Basiswechsel, Beispiel, Fourier-Analyse, Reihenentwicklung
• Fourier-Analyse, vom Zeitsignal zum Spektrum, Beispiel, Unimathematik
• Fourier-Analyse, vom Zeitsignal zum Spektrum, Fourier-Transformation
• Fourier-Analyse, Vorbereitung, Folgen, Reihen, Vektorraum, Unimathematik
• Fourier-Analyse, Vorbereitung, Integralgrundlagen, Unimathematik
• Fourier-Analyse, Vorbereitung, Komplexe Zahlen, Unimathematik
• Fourierreihe, Warum unendlich, Beispiel mit Gerade, Fourier-Analyse
• Fourierreihe, Übersicht, Beispiel mit Gerade, Unimathematik, Fourier-Analyse
• Fourier Reihe, wieder mal optische Bastelei, Video nach Frequenz
• Fourier, Faszination Fourierreihe, Funktionen basteln, approximieren
• Fourierreihe, ungerade, gerade Funktionen erkennen, wichtig für an und bn
• Fourierreihe, Verständis trigonometrische Funktionen, Beispiel mit Gerade
• Fourierreihe, Formel mit a0 oder a0 geteilt durch 2, beides geht:)
• Fourier Koeffizienten berechnen, Formeln, Fourierreihe, Fourier-Analyse
• Fourierreihe, Schreibweise mit Sinus, Kosinus, Summenzeichen, Fourier-Analyse
• Abwandlung Schreibweise bei Fourier Koeffizienten, mit n oder k…, Unimathematik
• Fourierreihe, Schreibweise mit Euler, Fourier-Analyse, Komplexe Form
• Fourierreihe, Schreibweise Euler, Vektorraumhintergrund, Fourier-Analyse
• Fourier Koeffizienten, Komplex, Fourier-Analyse, komplexe Fourierreihe
• Fourierreihe, Werte verstehen, Beispiel mit Gerade, Fourier-Analyse
• Fourierreihe, a0 berechnet, Beispiel mit Gerade, Fourier-Analyse
• Fourierreihe, an und bn berechnen, Beispiel mit Gerade, Fourier-Analyse
• Fourierreihe, Achtung für 1 & -1!!!, Beispiel mit Gerade steht noch 2 & -2
• Fourierreihe, Werte, Achtung für 1 und -1!!!, Beispiel mit Gerade, Fourier-Analyse
• Fourierreihe, Muster erkennen, Achtung für 1 und -1!!!, Beispiel mit Gerade
• Fourier-Analyse, Auswirkung Tiefpassfilter, Unimathematik, Zeit, Signal, Spektrum
• Fourier-Analyse, Periodisches Signal als Funktion & Grundfrequenz
• Fourier-Analyse, Periodisches Signal als Funktion, Zusatznotiz, Unimathematik
• Fourier-Analyse, Spielerei mit komplexer Reihe, Vorarbeit Fourier-Transformation
• Fourier-Analyse, Transformation, Analyse, Synthese, Unimathematik
• Fourier-Analyse, Transformierte, Inverse, Schreibweise Alternative wegen 2 Pi mit Wurzel
• Fourier-Analyse, Transformierte, Inverse, Schreibweise, Unimathematik
• Fourier, Übersicht, Zusammenhänge, Vokabeln
• Reihen, Konvergenz, Wurzelkriterium, Quotientenkriterium

Qt+Cmake (Playlist)

QtWS19: Using CMake and vcpkg in Qt application development
Using Modern CMake with Qt

New

CMake and Qt (Part 1) – CMake Introduction
CMake and Qt (Part 2) – Qt6 switches to CMake
CMake and Qt (Part 3) – Hello World in CMake
CMake and Qt (Part 4) – Using Libraries with CMake

Old

Getting Started with C++, Qt5 and CMake: Part 1
Getting Started with C++, Qt5 and CMake: Part 2
Getting Started with C++, Qt5 and CMake: Part 3
Getting Started with C++, Qt5 and CMake: Part 4
Getting Started with C++, Qt5 and CMake: Part 5

A modern, browser-based frontend to gdb (gnu debugger)

C++ Weekly – Ep 110 – gdbgui

Wir suchen dich als Senior Embedded C/C++ Entwickler/in. Überleg nicht lang, komm zu uns – we know how.
Senior Embedded C/C++ Entwickler/in – Noser Engineering AG – Winterthur, Schweiz

Qt parsing JSON using QJsonDocument, QJsonObject, QJsonArray

JSON Save Game Example
JSON Support in Qt
QJsonObject Class

Extending Python with C or C++
Building C and C++ Extensions

Unleash The Power of C++ In Python, 11.07.2019
Joe Jevnik – How to Write and Debug C Extension Modules – PyCon 2017, 18.05.2017
Cython Tutorial – Bridging between Python and C/C++ for performance gains, 10.07.2017
Python3 Advanced Tutorial 9 – C Extensions, 23.07.2015

Anaconda compiler tools
How to install Python support in Visual Studio on Windows
WindowsCompilers