Monthly Archives: April 2020

YouTube: Deeplearning.ai

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

Youtube: Skyentific

Skyentific, EPFL

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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)

sonic-vision.TV

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

Mathe by Daniel Jung

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

Python C and C++ Extensions

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