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Artificial Intelligence, Deep Learning and Computer Vision Masterclass
Introduction
Introduction (2:31)
Environment Setup
Installing PyCharm and Python on Windows
Installing PyCharm and Python on Mac
Installing TensorFlow and Keras (2:02)
Artificial Intelligence Basics
Why to learn artificial intelligence and machine learning? (5:24)
Types of artificial intelligence learning (8:29)
### MACHINE LEARNING ###
Machine learning algorithms
Linear Regression
What is linear regression? (8:52)
Linear regression theory - optimization (8:00)
Linear regression theory - gradient descent (7:23)
Linear regression implementation I (14:11)
Linear regression implementation II (4:24)
Mathematical formulation of linear regression
Logistic Regession
What is logistic regression? (11:54)
Logistic regression and maximum likelihood estimation (4:58)
Logistic regression example I - sigmoid function (11:29)
Logistic regression example II- credit scoring (10:53)
Logistic regression example III - credit scoring (5:51)
Mathematical formulation of logistic regression
Cross Validation
What is cross validation? (5:57)
Cross validation example (5:17)
K-Nearest Neighbor Classifier
What is the k-nearest neighbor classifier? (5:52)
Concept of lazy learning (3:33)
Distance metrics - Euclidean-distance (6:47)
Bias and variance trade-off (3:51)
K-nearest neighbor implementation I (7:10)
K-nearest neighbor implementation II (8:45)
K-nearest neighbor implementation III (4:12)
Mathematical formulation of k-nearest neighbor classifier
Naive Bayes Classifier
What is the naive Bayes classifier? (10:03)
Naive Bayes classifier illustration (4:24)
Naive Bayes classifier implementation (4:01)
What is text clustering? (8:55)
Text clustering - inverse document frequency (TF-IDF) (4:42)
Naive Bayes example - clustering news (14:23)
Mathematical formulation of naive Bayes classifier
Support Vector Machine (SVM)
What are Support Vector Machines (SVMs)? (5:19)
Linearly separable problems (14:10)
Non-linearly separable problems (6:32)
Kernel functions (9:49)
Support vector machine example I - simple (10:50)
Support vector machine example II - iris dataset (6:31)
Support vector machines example III - parameter tuning (7:17)
Support vector machine example IV - digit recognition (10:04)
Support vector machine example V - digit recognition (5:43)
Advantages and disadvantages (2:32)
Mathematical formulation of Support Vector Machines (SVMs)
Decision Trees
Decision trees introduction - basics (7:43)
Decision trees introduction - entropy (8:56)
Decision trees introduction - information gain (7:51)
The Gini-index approach (10:00)
Decision trees introduction - pros and cons (2:31)
Decision trees implementation I (5:50)
Decision trees implementation II - parameter tuning (4:28)
Decision tree implementation III - identifying cancer (4:49)
Mathematical formulation of decision trees
Random Forest Classifier
Pruning introduction (6:56)
Bagging introduction (7:49)
Random forest classifier introduction (5:36)
Random forests example I - iris dataset (4:14)
Random forests example II - credit scoring (3:29)
Random forests example III - OCR parameter tuning (9:49)
Mathematical formulation of random forest classifiers
Boosting
Boosting introduction - basics (4:18)
Boosting introduction - illustration (5:40)
Boosting introduction - equations (7:13)
Boosting introduction - final formula (8:27)
Boosting implementation I - iris dataset (6:29)
Boosting implementation II -wine classification (11:46)
Boosting vs. bagging (3:08)
Mathematical formulation of boosting
Principal Component Analysis (PCA)
Principal component analysis (PCA) introduction (8:18)
Principal component analysis example (10:38)
Principal component analysis example II (9:25)
Mathematical formulation of principle component analysis (PCA)
Clustering
K-means clustering introduction (9:57)
K-means clustering example (7:49)
K-means clustering - text clustering (9:01)
DBSCAN introduction (6:25)
DBSCAN example (8:43)
Hierarchical clustering introduction (6:33)
Hierarchical clustering example (8:10)
Hierarchical clustering - market segmentation (9:34)
Mathematical formulation of clustering
Computer Vision - Face Detection
Computer vision introduction (3:49)
Viola-Jones algorithm (10:53)
Haar-features (8:31)
Integral images (6:22)
Boosting in computer vision (6:18)
Cascading (4:13)
Face detection implementation I - installing OpenCV (2:50)
Face detection implementation II - CascadeClassifier (9:58)
Face detection implementation III - CascadeClassifier parameters (4:06)
Face detection implementation IV - tuning the parameters (4:51)
Face detection implementation V - detecting faces real-time (5:22)
Machine Learning Project I - Face Recognition
The Olivetti dataset (7:54)
Understanding the dataset (6:10)
Finding optimal number of principal components (eigenvectors) (6:17)
Understanding "eigenfaces" (8:01)
Constructing the machine learning models (4:33)
Using cross-validation (2:53)
### NEURAL NETWORKS AND DEEP LEARNING ###
Neural networks and deep learning algorithms
Feed-Forward Neural Network Theory
Artificial neural networks - inspiration (5:20)
Artificial neural networks - layers (4:42)
Artificial neural networks - the model (5:09)
Why to use activation functions? (6:46)
Neural networks - the big picture (9:07)
Using bias nodes in the neural network (1:44)
How to measure the error of the network? (4:46)
Optimization with gradient descent (8:28)
Gradient descent with backpropagation (6:33)
Backpropagation explained (12:15)
Mathematical formulation of feed-forward neural networks
Single Layer Networks Implementation
Simple neural network implementation - XOR problem (12:56)
Simple neural network implementation - Iris dataset (13:26)
Credit scoring with simple neural networks (4:24)
Deep Learning
Types of neural networks (3:51)
Deep Neural Networks Theory
Deep neural networks (5:15)
Activation functions revisited (9:44)
Loss functions (5:57)
Gradient descent and stochastic gradient descent (7:17)
Hyperparameters (5:14)
Mathematical formulation of deep neural networks
Deep Neural Networks Implementation
Deep neural network implementation I (6:30)
Deep neural network implementation II (6:26)
Deep neural network implementation III (4:45)
Multiclass classification implementation I (7:48)
Multiclass classification implementation II (5:27)
Machine Learning Project II - Smile Detector
Understanding the classification problem (2:14)
Reading the images and constructing the dataset I (6:13)
Reading the images and constructing the dataset II (4:45)
Building the deep neural network model (3:23)
Evaluating and testing the model (3:32)
Convolutional Neural Networks (CNNs) Theory
Convolutional neural networks basics (6:04)
Feature selection (4:11)
Convolutional neural networks - kernel (4:16)
Convolutional neural networks - kernel II (5:42)
Convolutional neural networks - pooling (5:47)
Convolutional neural networks - flattening (4:59)
Convolutional neural networks - illustration (2:38)
Mathematical formulation of convolution neural networks
Convolutional Neural Networks (CNNs) Implementation
Handwritten digit classification I (11:12)
Handwritten digit classification II (12:09)
Handwritten digit classification III (5:32)
Machine Learning Project III - Identifying Objects with CNNs
What is the CIFAR-10 dataset? (6:30)
Preprocessing the data (2:46)
Fitting the model (5:34)
Tuning the parameters - regularization (9:21)
Recurrent Neural Networks (RNNs) Theory
Why do recurrent neural networks are important? (4:30)
Recurrent neural networks basics (8:58)
Vanishing and exploding gradients problem (9:22)
Long-short term memory (LSTM) model (10:55)
Gated recurrent units (GRUs) (3:23)
Mathematical formulation of recurrent neural networks
Recurrent Neural Networks (RNNs) Implementation
Time series analysis example I (4:01)
Time series analysis example II (5:11)
Time series analysis example III (6:06)
Time series analysis example IV (2:33)
Time series analysis example V (3:59)
Time series analysis example VI (3:58)
Reinforcement Learning
What is reinforcement learning?
Applications of reinforcement learning (2:44)
Markov Decision Process (MDP) Theory
Markov decision processes basics I (5:38)
Markov decision processes basics II (6:21)
Markov decision processes - equations (12:00)
Markov decision processes - illustration (7:49)
Bellman-equation (5:41)
How to solve MDP problems? (2:20)
What is value iteration? (6:28)
What is policy iteration? (3:52)
Mathematical formulation of reinforcement learning
Exploration vs. Exploitation Problem
Exploration vs exploitation problem (3:29)
N-armed bandit problem introduction (8:46)
N-armed bandit problem implementation (11:12)
Applications: A/B testing in marketing (4:11)
Q Learning Theory
What is Q learning? (5:44)
Q learning introduction - the algorithm (7:08)
Q learning illustration (11:06)
Mathematical formulation of Q learning
Q Learning Implementation (Tic Tac Toe)
Tic tac toe with Q learning implementation I (3:45)
Tic tac toe with Q learning implementation II (7:38)
Tic tac toe with Q learning implementation III (7:26)
Tic tac toe with Q learning implementation IV (7:27)
Tic tac toe with Q learning implementation V (4:56)
Tic tac toe with Q learning implementation VI (12:06)
Tic tac toe with Q learning implementation VII (6:23)
Tic tac toe with Q learning implementation VIII (6:39)
Deep Q Learning Theory
What is deep Q learning? (4:47)
Deep Q learning and ε-greedy strategy (3:11)
Remember and replay (3:34)
Mathematical formulation of deep Q learning
Deep Q Learning Implementation (Tic Tac Toe)
Tic Tac Toe with deep Q learning implementation I (3:53)
Tic Tac Toe with deep Q learning implementation II (6:27)
Tic Tac Toe with deep Q learning implementation III (10:39)
Tic Tac Toe with deep Q learning implementation IV (5:05)
Tic Tac Toe with deep Q learning implementation V (4:43)
### COMPUTER VISION ###
Computer vision algorithms
History of Computer Vision
Evolution of computer vision related algorithms (3:36)
Handling Images and Pixels
Images and pixel intensities (5:13)
Handling pixel intensities I (6:18)
Handling pixel intensities II (5:16)
Why convolution is so important in image processing? (12:07)
Image processing - blur operation (5:08)
Image processing - edge detection kernel (5:38)
Image processing - sharpen operation (3:43)
Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)
Lane detection - the problem (1:44)
Lane detection - handling videos (5:36)
Lane detection - first transformations (4:21)
What is Canny edge detection? (6:32)
Getting the useful region of the image - masking (13:20)
Detecting lines - what is Hough transformation? (10:30)
Drawing lines on video frames (9:05)
Testing lane detection algorithm (2:19)
Histogram of Oriented Gradients (HOG) Algorithm Theory
Histogram of oriented gradients basics (3:48)
Histogram of oriented gradients - gradient kernel (6:55)
Histogram of oriented gradients - magnitude and angle (7:42)
Histogram of oriented gradients - normalization (4:46)
Histogram of oriented gradients - big picture (3:14)
Histogram of Oriented Gradients (HOG) Implementation
Showing the HOG features programatically (10:31)
Face detection with HOG implementation I (6:12)
Face detection with HOG implementation II (13:03)
Face detection with HOG implementation III (5:32)
Face detection with HOG implementation IV (7:30)
Convolutional Neural Networks (CNNs) Based Approaches
The standard convolutional neural network (CNN) way (5:57)
Region proposals and convolutional neural networks (CNNs) (9:41)
Detecting bounding boxes with regression (6:32)
What is the Fast R-CNN model? (2:30)
What is the Faster R-CNN model? (1:49)
You Only Look Once (YOLO) Algorithm Theory
What is the YOLO approach? (5:26)
YOLO algorithm - grid cells (6:45)
YOLO algorithm - intersection over union (9:02)
How to train the YOLO algorithm? (7:21)
YOLO algorithm - loss function (4:53)
YOLO algorithm - non-max suppression (2:52)
Why to use the so-called anchor boxes? (6:10)
You Only Look Once (YOLO) Algorithm Implementation
YOLO algorithm implementation I (6:15)
YOLO algorithm implementation II (9:08)
YOLO algorithm implementation III (8:45)
YOLO algorithm implementation IV (12:47)
YOLO algorithm implementation V (12:07)
YOLO algorithm implementation VI (1:47)
YOLO algorithm implementation VII (3:47)
Single-Shot MultiBox Detector (SSD) Theory
What is the SSD algorithm? (3:50)
Basic concept behind SSD algorithm (architecture) (7:17)
Bounding boxes and anchor boxes (10:16)
Feature maps and convolution layers (4:40)
Hard negative mining during training (2:22)
Regularization (data augmentation) and non-max suppression during training (2:14)
SSD Algorithm Implementation
SSD implementation I (6:06)
SSD implementation II (2:15)
SSD implementation III (5:10)
SSD implementation IV (7:56)
SSD implementation V (2:57)
Course Materials
Slides
Source code
Datasets
Course Materials (DOWNLOADS)
Source code and slides
Regularization (data augmentation) and non-max suppression during training
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