YouTube: NPTEL-NOC IITM

Machine Learning for Engineering and Science Applications

Machine Learning for Engineering and Science Applications

  • Machine Learning for Engineering and Science Applications – Intro Video
  • Introduction to the Course History of Artificial Intelligence
  • Overview of Machine Learning
  • Why Linear Algebra ? Scalars, Vectors, Tensors
  • Basic Operations
  • Norms
  • Linear Combinations Span Linear Independence
  • Matrix Operations Special Matrices Matrix Decompositions
  • Introduction to Probability Theory Discrete and Continuous Random Variables
  • Conditional – Joint – Marginal Probabilities Sum Rule and Product Rule Bayes’ Theorem
  • Bayes’ Theorem – Simple Examples
  • Independence Conditional Independence Chain Rule Of Probability
  • Expectation
  • Variance Covariance
  • Some Relations for Expectation and Covariance (Slightly Advanced)
  • Machine Representation of Numbers, Overflow, Underflow, Condition Number
  • Derivatives,Gradient,Hessian,Jacobian,Taylor Series
  • Matrix Calculus (Slightly Advanced)
  • Optimization – 1 Unconstrained Optimization
  • Introduction to Constrained Optimization
  • Introduction to Numerical Optimization Gradient Descent – 1
  • Gradient Descent – 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping Criteria
  • Introduction to Packages
  • The Learning Paradigm
  • A Linear Regression Example
  • Linear Regression Least Squares Gradient Descent
  • Coding Linear Regression
  • Generalized Function for Linear Regression
  • Goodness of Fit
  • Bias-Variance Trade Off
  • Gradient Descent Algorithms
  • Feedforward Neural Network
  • Structure of an Artificial Neuron
  • Multinomial Classification – One Hot Vector
  • Multinomial Classification- Introduction
  • XOR Gate
  • NOR, AND, NAND Gates
  • OR Gate Via Classification
  • Logistic Regression
  • Summary of Week 05
  • Introduction to back prop
  • Biological neuron
  • Schematic of multinomial logistic regression
  • Multinomial Classification – Softmax
  • Code for Logistic Regression
  • Gradient of logistic regression
  • Differentiating the sigmoid
  • Binary Entropy cost function
  • Introduction to Week 5 (Deep Learning)
  • Introduction to Convolution Neural Networks (CNN)
  • Types of convolution
  • CNN Architecture Part 1 (LeNet and Alex Net)
  • CNN Architecture Part 2 (VGG Net)
  • CNN Architecture Part 3 (GoogleNet)
  • CNN Architecture Part 4 (ResNet)
  • CNN Architecture Part 5 (DenseNet)
  • Train Network for Image Classification
  • Semantic Segmentation
  • Hyperparameter optimization
  • Transfer Learning
  • Segmentation of Brain Tumors from MRI using Deep Learning
  • Introduction to RNNs
  • Summary of RNNs
  • Deep RNNs and Bi- RNNs
  • Why LSTM Works
  • LSTM
  • RNN Architectures
  • Vanishing Gradients and TBPTT
  • Training RNNs – Loss and BPTT
  • Example – Sequence Classification
  • Batch Normalizing
  • Data Normalization
  • Learning Rate decay, Weight initialization
  • Activation Functions
  • Introduction- Week 09
  • Knn
  • Binary decision trees
  • Binary regression trees
  • Bagging
  • Random Forest
  • Boosting
  • Gradient boosting
  • Unsupervised learning & Kmeans
  • Agglomerative clustering
  • Probability Distributions Gaussian, Bernoulli
  • Covariance Matrix of Gaussian Distribution
  • Central Limit Theorem
  • Naïve Bayes
  • MLE Intro
  • PCA part 1
  • PCA part 2
  • Support Vector Machines
  • MLE, MAP and Bayesian Regression
  • Introduction to Generative model
  • Generative Adversarial Networks (GAN)
  • Variational Auto-encoders (VAE)
  • Applications: Cardiac MRI – Segmentation & Diagnosis
  • Applications: Cardiac MRI Analysis – Tensorflow code walkthrough
  • Introduction to Week 12
  • Application 1 description – Fin Heat Transfer
  • Application 1 solution
  • Application 2 description – Computational Fluid Dynamics
  • Application 2 solution
  • Application 3 description – Topology Optimization
  • Application 3 solution
  • Application 4 – Solution of PDE/ODE using Neural Networks
  • Summary and road ahead

Qt Raspberry Pi

RaspberryPi, 2016
Raspberry Pi Beginners Guide, 2017
RaspberryPi2EGLFS, 2019, Broadcom closed source drivers
RaspberryPiWithWebEngine, 2019, VC4 open source drivers

YouTube

Qt for Raspberry pi – Qt 5 Cross Compilation and installation ubuntu, 20.07.2018
How to debug Qt5 applications with QtCreator for Raspberry pi, 08.11.2018
Qt 5 development for Raspberry pi
LinkedIn: Ulaş DİKME

StackOverflow

How do I prepare a Raspberry Pi with Raspbian so I can cross compile Qt5 programs from a Linux host?
QT Creator, compiling and deploying a c or c++ to a remote device (BeagleBone or R pi)

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

Facebook
Patreon

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