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| [CourseClub.Me].url |
122б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
49б |
| 001 Artificial neural networks - inspiration_en.srt |
7.10Кб |
| 001 Artificial neural networks - inspiration.mp4 |
24.16Мб |
| 001 Boosting introduction - basics_en.srt |
5.82Кб |
| 001 Boosting introduction - basics.mp4 |
15.75Мб |
| 001 Convolutional neural networks basics_en.srt |
7.89Кб |
| 001 Convolutional neural networks basics.mp4 |
25.00Мб |
| 001 Course materials (source code and slides).html |
66б |
| 001 Decision trees introduction - basics_en.srt |
10.36Кб |
| 001 Decision trees introduction - basics.mp4 |
27.41Мб |
| 001 Deep neural network implementation I_en.srt |
8.99Кб |
| 001 Deep neural network implementation I.mp4 |
17.33Мб |
| 001 Deep neural networks_en.srt |
6.88Кб |
| 001 Deep neural networks.mp4 |
9.28Мб |
| 001 Evolution of computer vision related algorithms_en.srt |
5.02Кб |
| 001 Evolution of computer vision related algorithms.mp4 |
8.68Мб |
| 001 Exploration vs exploitation problem_en.srt |
4.71Кб |
| 001 Exploration vs exploitation problem.mp4 |
7.65Мб |
| 001 Face detection implementation I - installing OpenCV_en.srt |
3.48Кб |
| 001 Face detection implementation I - installing OpenCV.mp4 |
7.64Мб |
| 001 First steps in Python_en.srt |
7.24Кб |
| 001 First steps in Python.mp4 |
7.38Мб |
| 001 Handwritten digit classification I_en.srt |
15.41Кб |
| 001 Handwritten digit classification I.mp4 |
54.32Мб |
| 001 Histogram of oriented gradients basics_en.srt |
4.98Кб |
| 001 Histogram of oriented gradients basics.mp4 |
19.24Мб |
| 001 How to measure the running time of algorithms_en.srt |
14.37Кб |
| 001 How to measure the running time of algorithms.mp4 |
18.29Мб |
| 001 Images and pixel intensities_en.srt |
6.94Кб |
| 001 Images and pixel intensities.mp4 |
10.74Мб |
| 001 Installing PyCharm and Python on Windows.html |
1.54Кб |
| 001 Introduction_en.srt |
5.09Кб |
| 001 Introduction.mp4 |
25.85Мб |
| 001 K-means clustering introduction_en.srt |
14.56Кб |
| 001 K-means clustering introduction.mp4 |
16.62Мб |
| 001 Lane detection - the problem_en.srt |
2.49Кб |
| 001 Lane detection - the problem.mp4 |
4.41Мб |
| 001 Machine learning section.html |
471б |
| 001 Markov decision processes basics I_en.srt |
7.14Кб |
| 001 Markov decision processes basics I.mp4 |
23.20Мб |
| 001 Neural networks and deep learning section.html |
346б |
| 001 Principal component analysis (PCA) introduction_en.srt |
11.04Кб |
| 001 Principal component analysis (PCA) introduction.mp4 |
38.24Мб |
| 001 Pruning introduction_en.srt |
8.75Кб |
| 001 Pruning introduction.mp4 |
15.47Мб |
| 001 Python crash course introduction_en.srt |
3.11Кб |
| 001 Python crash course introduction.mp4 |
3.97Мб |
| 001 PythonMachineLearning.zip |
565.12Мб |
| 001 Showing the HOG features programatically_en.srt |
13.86Кб |
| 001 Showing the HOG features programatically.mp4 |
53.40Мб |
| 001 Simple neural network implementation - XOR problem_en.srt |
17.76Кб |
| 001 Simple neural network implementation - XOR problem.mp4 |
36.70Мб |
| 001 SSD implementation I_en.srt |
7.67Кб |
| 001 SSD implementation I.mp4 |
30.89Мб |
| 001 The Olivetti dataset_en.srt |
10.26Кб |
| 001 The Olivetti dataset.mp4 |
22.83Мб |
| 001 The standard convolutional neural network (CNN) way_en.srt |
8.18Кб |
| 001 The standard convolutional neural network (CNN) way.mp4 |
18.36Мб |
| 001 Tic Tac Toe with deep Q learning implementation I_en.srt |
5.26Кб |
| 001 Tic Tac Toe with deep Q learning implementation I.mp4 |
21.12Мб |
| 001 Tic tac toe with Q learning implementation I_en.srt |
5.16Кб |
| 001 Tic tac toe with Q learning implementation I.mp4 |
16.78Мб |
| 001 Time series analysis example I_en.srt |
5.71Кб |
| 001 Time series analysis example I.mp4 |
14.07Мб |
| 001 Types of neural networks_en.srt |
4.88Кб |
| 001 Types of neural networks.mp4 |
8.01Мб |
| 001 Understanding the classification problem_en.srt |
3.03Кб |
| 001 Understanding the classification problem.mp4 |
4.78Мб |
| 001 Viola-Jones algorithm_en.srt |
14.84Кб |
| 001 Viola-Jones algorithm.mp4 |
40.92Мб |
| 001 What are functions_en.srt |
5.73Кб |
| 001 What are functions.mp4 |
8.09Мб |
| 001 What are Support Vector Machines (SVMs)_en.srt |
7.45Кб |
| 001 What are Support Vector Machines (SVMs).mp4 |
20.14Мб |
| 001 What is cross validation_en.srt |
7.90Кб |
| 001 What is cross validation.mp4 |
24.41Мб |
| 001 What is deep Q learning_en.srt |
6.20Кб |
| 001 What is deep Q learning.mp4 |
9.29Мб |
| 001 What is linear regression_en.srt |
12.34Кб |
| 001 What is linear regression.mp4 |
39.99Мб |
| 001 What is logistic regression_en.srt |
16.15Кб |
| 001 What is logistic regression.mp4 |
40.20Мб |
| 001 What is object oriented programming (OOP)_en.srt |
3.17Кб |
| 001 What is object oriented programming (OOP).mp4 |
5.23Мб |
| 001 What is Q learning_en.srt |
6.94Кб |
| 001 What is Q learning.mp4 |
11.79Мб |
| 001 What is reinforcement learning.html |
899б |
| 001 What is the CIFAR-10 dataset_en.srt |
8.01Кб |
| 001 What is the CIFAR-10 dataset.mp4 |
36.07Мб |
| 001 What is the key advantage of NumPy_en.srt |
5.81Кб |
| 001 What is the key advantage of NumPy.mp4 |
8.16Мб |
| 001 What is the k-nearest neighbor classifier_en.srt |
7.90Кб |
| 001 What is the k-nearest neighbor classifier.mp4 |
13.69Мб |
| 001 What is the naive Bayes classifier_en.srt |
14.13Кб |
| 001 What is the naive Bayes classifier.mp4 |
42.38Мб |
| 001 What is the SSD algorithm_en.srt |
5.24Кб |
| 001 What is the SSD algorithm.mp4 |
18.05Мб |
| 001 What is the YOLO approach_en.srt |
7.57Кб |
| 001 What is the YOLO approach.mp4 |
12.37Мб |
| 001 Why do recurrent neural networks are important_en.srt |
5.69Кб |
| 001 Why do recurrent neural networks are important.mp4 |
21.30Мб |
| 001 Why to learn artificial intelligence and machine learning_en.srt |
7.69Кб |
| 001 Why to learn artificial intelligence and machine learning.mp4 |
14.01Мб |
| 001 YOLO algorithm implementation I_en.srt |
8.84Кб |
| 001 YOLO algorithm implementation I.mp4 |
22.81Мб |
| 002 Activation functions revisited_en.srt |
12.42Кб |
| 002 Activation functions revisited.mp4 |
26.21Мб |
| 002 Applications of reinforcement learning_en.srt |
3.56Кб |
| 002 Applications of reinforcement learning.mp4 |
6.60Мб |
| 002 Artificial neural networks - layers_en.srt |
6.67Кб |
| 002 Artificial neural networks - layers.mp4 |
11.03Мб |
| 002 Bagging introduction_en.srt |
10.09Кб |
| 002 Bagging introduction.mp4 |
16.08Мб |
| 002 Basic concept behind SSD algorithm (architecture)_en.srt |
9.79Кб |
| 002 Basic concept behind SSD algorithm (architecture).mp4 |
43.48Мб |
| 002 Boosting introduction - illustration_en.srt |
7.25Кб |
| 002 Boosting introduction - illustration.mp4 |
11.17Мб |
| 002 Class and objects basics_en.srt |
3.73Кб |
| 002 Class and objects basics.mp4 |
5.39Мб |
| 002 Concept of lazy learning_en.srt |
5.11Кб |
| 002 Concept of lazy learning.mp4 |
15.24Мб |
| 002 Creating and updating arrays_en.srt |
9.57Кб |
| 002 Creating and updating arrays.mp4 |
16.76Мб |
| 002 Cross validation example_en.srt |
6.96Кб |
| 002 Cross validation example.mp4 |
25.13Мб |
| 002 Data structures introduction_en.srt |
4.50Кб |
| 002 Data structures introduction.mp4 |
6.72Мб |
| 002 Decision trees introduction - entropy_en.srt |
10.88Кб |
| 002 Decision trees introduction - entropy.mp4 |
40.84Мб |
| 002 Deep neural network implementation II_en.srt |
8.73Кб |
| 002 Deep neural network implementation II.mp4 |
18.90Мб |
| 002 Deep Q learning and ε-greedy strategy_en.srt |
4.23Кб |
| 002 Defining functions_en.srt |
6.93Кб |
| 002 Defining functions.mp4 |
9.60Мб |
| 002 Face detection implementation II - CascadeClassifier_en.srt |
12.57Кб |
| 002 Face detection implementation II - CascadeClassifier.mp4 |
70.68Мб |
| 002 Face detection with HOG implementation I_en.srt |
7.71Кб |
| 002 Face detection with HOG implementation I.mp4 |
15.45Мб |
| 002 Feature selection_en.srt |
5.49Кб |
| 002 Feature selection.mp4 |
12.15Мб |
| 002 Haar-features_en.srt |
10.65Кб |
| 002 Haar-features.mp4 |
22.13Мб |
| 002 Handling pixel intensities I_en.srt |
8.20Кб |
| 002 Handling pixel intensities I.mp4 |
34.64Мб |
| 002 Handwritten digit classification II_en.srt |
16.79Кб |
| 002 Handwritten digit classification II.mp4 |
55.59Мб |
| 002 Histogram of oriented gradients - gradient kernel_en.srt |
10.14Кб |
| 002 Histogram of oriented gradients - gradient kernel.mp4 |
30.56Мб |
| 002 Installing PyCharm and Python on Mac.html |
1.55Кб |
| 002 K-means clustering example_en.srt |
10.54Кб |
| 002 K-means clustering example.mp4 |
19.52Мб |
| 002 Lane detection - handling videos_en.srt |
7.50Кб |
| 002 Lane detection - handling videos.mp4 |
13.69Мб |
| 002 Linearly separable problems_en.srt |
18.40Кб |
| 002 Linearly separable problems.mp4 |
30.33Мб |
| 002 Linear regression theory - optimization_en.srt |
11.32Кб |
| 002 Linear regression theory - optimization.mp4 |
45.04Мб |
| 002 Logistic regression and maximum likelihood estimation_en.srt |
6.68Кб |
| 002 Logistic regression and maximum likelihood estimation.mp4 |
22.68Мб |
| 002 Markov decision processes basics II_en.srt |
8.37Кб |
| 002 Markov decision processes basics II.mp4 |
14.15Мб |
| 002 Naive Bayes classifier illustration_en.srt |
6.24Кб |
| 002 Naive Bayes classifier illustration.mp4 |
9.23Мб |
| 002 N-armed bandit problem introduction_en.srt |
10.81Кб |
| 002 N-armed bandit problem introduction.mp4 |
19.56Мб |
| 002 Preprocessing the data_en.srt |
3.53Кб |
| 002 Preprocessing the data.mp4 |
7.66Мб |
| 002 Principal component analysis example_en.srt |
14.22Кб |
| 002 Principal component analysis example.mp4 |
26.79Мб |
| 002 Q learning introduction - the algorithm_en.srt |
9.32Кб |
| 002 Q learning introduction - the algorithm.mp4 |
15.46Мб |
| 002 Reading the images and constructing the dataset I_en.srt |
7.63Кб |
| 002 Reading the images and constructing the dataset I.mp4 |
25.08Мб |
| 002 Recurrent neural networks basics_en.srt |
11.80Кб |
| 002 Recurrent neural networks basics.mp4 |
28.63Мб |
| 002 Region proposals and convolutional neural networks (CNNs)_en.srt |
13.67Кб |
| 002 Region proposals and convolutional neural networks (CNNs).mp4 |
60.62Мб |
| 002 Simple neural network implementation - Iris dataset_en.srt |
17.75Кб |
| 002 Simple neural network implementation - Iris dataset.mp4 |
84.96Мб |
| 002 SSD implementation II_en.srt |
3.08Кб |
| 002 SSD implementation II.mp4 |
6.37Мб |
| 002 Tic Tac Toe with deep Q learning implementation II_en.srt |
8.87Кб |
| 002 Tic Tac Toe with deep Q learning implementation II.mp4 |
40.62Мб |
| 002 Tic tac toe with Q learning implementation II_en.srt |
10.38Кб |
| 002 Tic tac toe with Q learning implementation II.mp4 |
19.81Мб |
| 002 Time series analysis example II_en.srt |
7.47Кб |
| 002 Time series analysis example II.mp4 |
13.04Мб |
| 002 Types of artificial intelligence learning_en.srt |
11.73Кб |
| 002 Types of artificial intelligence learning.mp4 |
36.97Мб |
| 002 Understanding the dataset_en.srt |
7.82Кб |
| 002 Understanding the dataset.mp4 |
45.89Мб |
| 002 What are the basic data types_en.srt |
6.35Кб |
| 002 What are the basic data types.mp4 |
7.70Мб |
| 002 YOLO algorithm - grid cells_en.srt |
8.94Кб |
| 002 YOLO algorithm - grid cells.mp4 |
38.38Мб |
| 002 YOLO algorithm implementation II_en.srt |
11.90Кб |
| 002 YOLO algorithm implementation II.mp4 |
23.78Мб |
| 003 Artificial neural networks - the model_en.srt |
6.60Кб |
| 003 Artificial neural networks - the model.mp4 |
21.55Мб |
| 003 Booleans_en.srt |
2.43Кб |
| 003 Booleans.mp4 |
3.52Мб |
| 003 Boosting introduction - equations_en.srt |
8.95Кб |
| 003 Boosting introduction - equations.mp4 |
13.42Мб |
| 003 Bounding boxes and anchor boxes_en.srt |
13.48Кб |
| 003 Bounding boxes and anchor boxes.mp4 |
70.53Мб |
| 003 Convolutional neural networks - kernel_en.srt |
5.63Кб |
| 003 Convolutional neural networks - kernel.mp4 |
8.90Мб |
| 003 Credit scoring with simple neural networks_en.srt |
5.52Кб |
| 003 Credit scoring with simple neural networks.mp4 |
23.17Мб |
| 003 Decision trees introduction - information gain_en.srt |
10.52Кб |
| 003 Decision trees introduction - information gain.mp4 |
38.24Мб |
| 003 Deep neural network implementation III_en.srt |
6.84Кб |
| 003 Deep neural network implementation III.mp4 |
26.09Мб |
| 003 Detecting bounding boxes with regression_en.srt |
9.20Кб |
| 003 Detecting bounding boxes with regression.mp4 |
22.10Мб |
| 003 Dimension of arrays_en.srt |
12.22Кб |
| 003 Dimension of arrays.mp4 |
18.44Мб |
| 003 Distance metrics - Euclidean-distance_en.srt |
9.30Кб |
| 003 Distance metrics - Euclidean-distance.mp4 |
21.73Мб |
| 003 Face detection implementation III - CascadeClassifier parameters_en.srt |
5.39Кб |
| 003 Face detection implementation III - CascadeClassifier parameters.mp4 |
18.36Мб |
| 003 Face detection with HOG implementation II_en.srt |
16.75Кб |
| 003 Face detection with HOG implementation II.mp4 |
52.33Мб |
| 003 Finding optimal number of principal components (eigenvectors)_en.srt |
7.78Кб |
| 003 Finding optimal number of principal components (eigenvectors).mp4 |
23.63Мб |
| 003 Fitting the model_en.srt |
7.40Кб |
| 003 Fitting the model.mp4 |
43.65Мб |
| 003 Fundamentals of statistics_en.srt |
10.33Кб |
| 003 Fundamentals of statistics.mp4 |
33.22Мб |
| 003 Handling pixel intensities II_en.srt |
7.24Кб |
| 003 Handling pixel intensities II.mp4 |
13.21Мб |
| 003 Handwritten digit classification III_en.srt |
7.51Кб |
| 003 Handwritten digit classification III.mp4 |
35.17Мб |
| 003 Histogram of oriented gradients - magnitude and angle_en.srt |
10.31Кб |
| 003 Histogram of oriented gradients - magnitude and angle.mp4 |
33.92Мб |
| 003 Installing TensorFlow and Keras_en.srt |
2.84Кб |
| 003 Installing TensorFlow and Keras.mp4 |
9.70Мб |
| 003 Integral images_en.srt |
8.01Кб |
| 003 Integral images.mp4 |
24.53Мб |
| 003 K-means clustering - text clustering_en.srt |
13.14Кб |
| 003 K-means clustering - text clustering.mp4 |
37.68Мб |
| 003 Lane detection - first transformations_en.srt |
5.84Кб |
| 003 Lane detection - first transformations.mp4 |
11.94Мб |
| 003 Linear regression theory - gradient descent_en.srt |
10.71Кб |
| 003 Linear regression theory - gradient descent.mp4 |
39.45Мб |
| 003 Logistic regression example I - sigmoid function_en.srt |
14.73Кб |
| 003 Logistic regression example I - sigmoid function.mp4 |
33.18Мб |
| 003 Loss functions_en.srt |
7.72Кб |
| 003 Loss functions.mp4 |
15.42Мб |
| 003 Markov decision processes - equations_en.srt |
14.72Кб |
| 003 Markov decision processes - equations.mp4 |
49.65Мб |
| 003 Naive Bayes classifier implementation_en.srt |
5.20Кб |
| 003 Naive Bayes classifier implementation.mp4 |
11.08Мб |
| 003 N-armed bandit problem implementation_en.srt |
15.15Кб |
| 003 N-armed bandit problem implementation.mp4 |
53.31Мб |
| 003 Non-linearly separable problems_en.srt |
9.22Кб |
| 003 Non-linearly separable problems.mp4 |
22.96Мб |
| 003 Positional arguments and keyword arguments_en.srt |
12.83Кб |
| 003 Positional arguments and keyword arguments.mp4 |
22.20Мб |
| 003 Principal component analysis example II_en.srt |
12.16Кб |
| 003 Principal component analysis example II.mp4 |
22.27Мб |
| 003 Q learning illustration_en.srt |
14.70Кб |
| 003 Q learning illustration.mp4 |
21.44Мб |
| 003 Random forest classifier introduction_en.srt |
7.08Кб |
| 003 Random forest classifier introduction.mp4 |
12.29Мб |
| 003 Reading the images and constructing the dataset II_en.srt |
5.86Кб |
| 003 Reading the images and constructing the dataset II.mp4 |
38.06Мб |
| 003 Remember and replay_en.srt |
4.65Кб |
| 003 Remember and replay.mp4 |
7.00Мб |
| 003 SSD implementation III_en.srt |
6.65Кб |
| 003 SSD implementation III.mp4 |
18.84Мб |
| 003 Tic Tac Toe with deep Q learning implementation III_en.srt |
13.94Кб |
| 003 Tic Tac Toe with deep Q learning implementation III.mp4 |
74.29Мб |
| 003 Tic tac toe with Q learning implementation III_en.srt |
9.56Кб |
| 003 Tic tac toe with Q learning implementation III.mp4 |
26.25Мб |
| 003 Time series analysis example III_en.srt |
8.97Кб |
| 003 Time series analysis example III.mp4 |
20.06Мб |
| 003 Using the constructor_en.srt |
7.55Кб |
| 003 Using the constructor.mp4 |
17.82Мб |
| 003 Vanishing and exploding gradients problem_en.srt |
11.96Кб |
| 003 Vanishing and exploding gradients problem.mp4 |
27.18Мб |
| 003 What are array data structures I_en.srt |
9.16Кб |
| 003 What are array data structures I.mp4 |
12.26Мб |
| 003 YOLO algorithm implementation III_en.srt |
11.42Кб |
| 003 YOLO algorithm implementation III.mp4 |
24.71Мб |
| 003 YOLO algorithm - intersection over union_en.srt |
12.00Кб |
| 003 YOLO algorithm - intersection over union.mp4 |
51.40Мб |
| 004 Applications AB testing in marketing_en.srt |
5.63Кб |
| 004 Applications AB testing in marketing.mp4 |
12.13Мб |
| 004 Bias and variance trade-off_en.srt |
5.16Кб |
| 004 Bias and variance trade-off.mp4 |
14.70Мб |
| 004 Boosting in computer vision_en.srt |
8.14Кб |
| 004 Boosting in computer vision.mp4 |
23.41Мб |
| 004 Boosting introduction - final formula_en.srt |
10.59Кб |
| 004 Boosting introduction - final formula.mp4 |
36.78Мб |
| 004 Building the deep neural network model_en.srt |
4.42Кб |
| 004 Building the deep neural network model.mp4 |
9.55Мб |
| 004 Class variables and instance variables_en.srt |
5.66Кб |
| 004 Class variables and instance variables.mp4 |
14.67Мб |
| 004 Convolutional neural networks - kernel II_en.srt |
7.63Кб |
| 004 Convolutional neural networks - kernel II.mp4 |
8.88Мб |
| 004 DBSCAN introduction_en.srt |
9.50Кб |
| 004 DBSCAN introduction.mp4 |
11.37Мб |
| 004 Face detection implementation IV - tuning the parameters_en.srt |
6.22Кб |
| 004 Face detection implementation IV - tuning the parameters.mp4 |
18.00Мб |
| 004 Face detection with HOG implementation III_en.srt |
6.47Кб |
| 004 Face detection with HOG implementation III.mp4 |
36.08Мб |
| 004 Feature maps and convolution layers_en.srt |
6.32Кб |
| 004 Feature maps and convolution layers.mp4 |
13.86Мб |
| 004 Gradient descent and stochastic gradient descent_en.srt |
9.51Кб |
| 004 Gradient descent and stochastic gradient descent.mp4 |
40.09Мб |
| 004 Histogram of oriented gradients - normalization_en.srt |
6.43Кб |
| 004 Histogram of oriented gradients - normalization.mp4 |
22.59Мб |
| 004 How to train the YOLO algorithm_en.srt |
10.08Кб |
| 004 How to train the YOLO algorithm.mp4 |
25.08Мб |
| 004 Indexes and slicing_en.srt |
10.50Кб |
| 004 Indexes and slicing.mp4 |
16.72Мб |
| 004 Kernel functions_en.srt |
13.04Кб |
| 004 Kernel functions.mp4 |
34.10Мб |
| 004 Linear regression implementation I_en.srt |
18.37Кб |
| 004 Linear regression implementation I.mp4 |
90.81Мб |
| 004 Logistic regression example II- credit scoring_en.srt |
14.25Кб |
| 004 Logistic regression example II- credit scoring.mp4 |
58.55Мб |
| 004 Long-short term memory (LSTM) model_en.srt |
14.00Кб |
| 004 Long-short term memory (LSTM) model.mp4 |
33.39Мб |
| 004 Markov decision processes - illustration_en.srt |
9.37Кб |
| 004 Markov decision processes - illustration.mp4 |
28.22Мб |
| 004 Mathematical formulation of deep Q learning.html |
272б |
| 004 Mathematical formulation of principle component analysis (PCA).html |
282б |
| 004 Mathematical formulation of Q learning.html |
262б |
| 004 Multiclass classification implementation I_en.srt |
10.71Кб |
| 004 Multiclass classification implementation I.mp4 |
28.48Мб |
| 004 Random forests example I - iris dataset_en.srt |
5.57Кб |
| 004 Random forests example I - iris dataset.mp4 |
13.50Мб |
| 004 Returning values_en.srt |
3.06Кб |
| 004 Returning values.mp4 |
4.11Мб |
| 004 SSD implementation IV_en.srt |
10.59Кб |
| 004 SSD implementation IV.mp4 |
50.57Мб |
| 004 Strings_en.srt |
9.85Кб |
| 004 Strings.mp4 |
14.57Мб |
| 004 The Gini-index approach_en.srt |
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| 004 The Gini-index approach.mp4 |
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| 004 Tic Tac Toe with deep Q learning implementation IV_en.srt |
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| 004 Tic Tac Toe with deep Q learning implementation IV.mp4 |
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| 004 Tic tac toe with Q learning implementation IV_en.srt |
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| 004 Tic tac toe with Q learning implementation IV.mp4 |
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| 004 Time series analysis example IV_en.srt |
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| 004 Time series analysis example IV.mp4 |
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| 004 Tuning the parameters - regularization_en.srt |
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| 004 Tuning the parameters - regularization.mp4 |
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| 004 Understanding eigenfaces_en.srt |
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| 004 Understanding eigenfaces.mp4 |
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| 004 What are array data structures II_en.srt |
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| 004 What are array data structures II.mp4 |
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| 004 What is Canny edge detection_en.srt |
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| 004 What is Canny edge detection.mp4 |
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| 004 What is text clustering_en.srt |
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| 004 What is text clustering.mp4 |
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| 004 What is the Fast R-CNN model_en.srt |
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| 004 What is the Fast R-CNN model.mp4 |
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| 004 Why convolution is so important in image processing_en.srt |
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| 004 Why convolution is so important in image processing.mp4 |
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| 004 Why to use activation functions_en.srt |
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| 004 Why to use activation functions.mp4 |
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| 004 YOLO algorithm implementation IV_en.srt |
15.95Кб |
| 004 YOLO algorithm implementation IV.mp4 |
69.67Мб |
| 005 Bellman-equation_en.srt |
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| 005 Bellman-equation.mp4 |
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| 005 Boosting implementation I - iris dataset_en.srt |
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| 005 Boosting implementation I - iris dataset.mp4 |
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| 005 Cascading_en.srt |
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| 005 Cascading.mp4 |
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| 005 Constructing the machine learning models_en.srt |
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| 005 Constructing the machine learning models.mp4 |
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| 005 Convolutional neural networks - pooling_en.srt |
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| 005 Convolutional neural networks - pooling.mp4 |
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| 005 DBSCAN example_en.srt |
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| 005 DBSCAN example.mp4 |
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| 005 Decision trees introduction - pros and cons_en.srt |
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| 005 Decision trees introduction - pros and cons.mp4 |
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| 005 Evaluating and testing the model_en.srt |
4.78Кб |
| 005 Evaluating and testing the model.mp4 |
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| 005 Face detection implementation V - detecting faces real-time_en.srt |
6.96Кб |
| 005 Face detection implementation V - detecting faces real-time.mp4 |
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| 005 Face detection with HOG implementation IV_en.srt |
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| 005 Face detection with HOG implementation IV.mp4 |
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| 005 Gated recurrent units (GRUs)_en.srt |
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| 005 Gated recurrent units (GRUs).mp4 |
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| 005 Getting the useful region of the image - masking_en.srt |
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| 005 Getting the useful region of the image - masking.mp4 |
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| 005 Hard negative mining during training_en.srt |
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| 005 Hard negative mining during training.mp4 |
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| 005 Histogram of oriented gradients - big picture_en.srt |
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| 005 Histogram of oriented gradients - big picture.mp4 |
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| 005 Hyperparameters_en.srt |
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| 005 Hyperparameters.mp4 |
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| 005 Image processing - blur operation_en.srt |
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| 005 Image processing - blur operation.mp4 |
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| 005 K-nearest neighbor implementation I_en.srt |
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| 005 K-nearest neighbor implementation I.mp4 |
16.50Мб |
| 005 Linear regression implementation II_en.srt |
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| 005 Linear regression implementation II.mp4 |
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| 005 Lists in Python_en.srt |
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| 005 Lists in Python.mp4 |
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| 005 Logistic regression example III - credit scoring_en.srt |
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| 005 Logistic regression example III - credit scoring.mp4 |
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| 005 Multiclass classification implementation II_en.srt |
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| 005 Multiclass classification implementation II.mp4 |
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| 005 Neural networks - the big picture_en.srt |
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| 005 Neural networks - the big picture.mp4 |
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| 005 Private variables and name mangling_en.srt |
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| 005 Private variables and name mangling.mp4 |
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| 005 Random forests example II - credit scoring_en.srt |
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| 005 Random forests example II - credit scoring.mp4 |
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| 005 Returning multiple values_en.srt |
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| 005 Returning multiple values.mp4 |
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| 005 SSD implementation V_en.srt |
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| 005 SSD implementation V.mp4 |
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| 005 String slicing_en.srt |
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| 005 String slicing.mp4 |
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| 005 Support vector machine example I - simple_en.srt |
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| 005 Support vector machine example I - simple.mp4 |
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| 005 Text clustering - inverse document frequency (TF-IDF)_en.srt |
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| 005 Text clustering - inverse document frequency (TF-IDF).mp4 |
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| 005 Tic Tac Toe with deep Q learning implementation V_en.srt |
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| 005 Tic Tac Toe with deep Q learning implementation V.mp4 |
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| 005 Tic tac toe with Q learning implementation V_en.srt |
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| 005 Tic tac toe with Q learning implementation V.mp4 |
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| 005 Time series analysis example V_en.srt |
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| 005 Time series analysis example V.mp4 |
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| 005 Types_en.srt |
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| 005 Types.mp4 |
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| 005 What is the Faster R-CNN model_en.srt |
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| 005 What is the Faster R-CNN model.mp4 |
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| 005 YOLO algorithm implementation V_en.srt |
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| 005 YOLO algorithm implementation V.mp4 |
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| 005 YOLO algorithm - loss function_en.srt |
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| 005 YOLO algorithm - loss function.mp4 |
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| 006 Boosting implementation II -wine classification_en.srt |
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| 006 Boosting implementation II -wine classification.mp4 |
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| 006 Convolutional neural networks - flattening_en.srt |
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| 006 Convolutional neural networks - flattening.mp4 |
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| 006 Decision trees implementation I_en.srt |
7.65Кб |
| 006 Decision trees implementation I.mp4 |
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| 006 Detecting lines - what is Hough transformation_en.srt |
14.89Кб |
| 006 Detecting lines - what is Hough transformation.mp4 |
45.00Мб |
| 006 Hierarchical clustering introduction_en.srt |
9.61Кб |
| 006 Hierarchical clustering introduction.mp4 |
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| 006 How to solve MDP problems_en.srt |
3.05Кб |
| 006 How to solve MDP problems.mp4 |
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| 006 Image processing - edge detection kernel_en.srt |
7.47Кб |
| 006 Image processing - edge detection kernel.mp4 |
14.58Мб |
| 006 K-nearest neighbor implementation II_en.srt |
11.17Кб |
| 006 K-nearest neighbor implementation II.mp4 |
48.51Мб |
| 006 Lists in Python - advanced operations_en.srt |
9.61Кб |
| 006 Lists in Python - advanced operations.mp4 |
18.63Мб |
| 006 Mathematical formulation of deep neural networks.html |
290б |
| 006 Mathematical formulation of linear regression.html |
275б |
| 006 Mathematical formulation of logistic regression.html |
263б |
| 006 Mathematical formulation of recurrent neural networks.html |
258б |
| 006 Naive Bayes example - clustering news_en.srt |
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| 006 Naive Bayes example - clustering news.mp4 |
78.91Мб |
| 006 Original academic research article.html |
318б |
| 006 Original academic research articles.html |
311б |
| 006 Original academic research articles.html |
453б |
| 006 Random forests example III - OCR parameter tuning_en.srt |
13.07Кб |
| 006 Random forests example III - OCR parameter tuning.mp4 |
31.90Мб |
| 006 Regularization (data augmentation) and non-max suppression during training_en.srt |
3.05Кб |
| 006 Regularization (data augmentation) and non-max suppression during training.mp4 |
6.87Мб |
| 006 Reshape_en.srt |
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| 006 Reshape.mp4 |
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| 006 Support vector machine example II - iris dataset_en.srt |
8.49Кб |
| 006 Support vector machine example II - iris dataset.mp4 |
15.10Мб |
| 006 Tic tac toe with Q learning implementation VI_en.srt |
16.18Кб |
| 006 Tic tac toe with Q learning implementation VI.mp4 |
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| 006 Time series analysis example VI_en.srt |
5.78Кб |
| 006 Time series analysis example VI.mp4 |
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| 006 Type casting_en.srt |
5.31Кб |
| 006 Type casting.mp4 |
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| 006 Using bias nodes in the neural network_en.srt |
2.28Кб |
| 006 Using bias nodes in the neural network.mp4 |
4.32Мб |
| 006 Using cross-validation_en.srt |
3.61Кб |
| 006 Using cross-validation.mp4 |
21.97Мб |
| 006 What is inheritance in OOP_en.srt |
4.69Кб |
| 006 What is inheritance in OOP.mp4 |
8.13Мб |
| 006 Yield operator_en.srt |
6.38Кб |
| 006 Yield operator.mp4 |
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| 006 YOLO algorithm implementation VI_en.srt |
2.57Кб |
| 006 YOLO algorithm implementation VI.mp4 |
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| 006 YOLO algorithm - non-max suppression_en.srt |
4.09Кб |
| 006 YOLO algorithm - non-max suppression.mp4 |
9.12Мб |
| 007 Boosting vs. bagging_en.srt |
4.06Кб |
| 007 Boosting vs. bagging.mp4 |
6.87Мб |
| 007 Convolutional neural networks - illustration_en.srt |
3.56Кб |
| 007 Convolutional neural networks - illustration.mp4 |
31.87Мб |
| 007 Decision trees implementation II - parameter tuning_en.srt |
5.88Кб |
| 007 Decision trees implementation II - parameter tuning.mp4 |
14.09Мб |
| 007 Hierarchical clustering example_en.srt |
10.55Кб |
| 007 Hierarchical clustering example.mp4 |
20.36Мб |
| 007 Hough transformation illustration.html |
191б |
| 007 How to measure the error of the network_en.srt |
6.53Кб |
| 007 How to measure the error of the network.mp4 |
12.03Мб |
| 007 Image processing - sharpen operation_en.srt |
4.57Кб |
| 007 Image processing - sharpen operation.mp4 |
9.03Мб |
| 007 K-nearest neighbor implementation III_en.srt |
5.40Кб |
| 007 K-nearest neighbor implementation III.mp4 |
10.53Мб |
| 007 Lists in Python - list comprehension_en.srt |
7.02Кб |
| 007 Lists in Python - list comprehension.mp4 |
11.39Мб |
| 007 Local and global variables_en.srt |
2.61Кб |
| 007 Local and global variables.mp4 |
4.25Мб |
| 007 Mathematical formulation of naive Bayes classifier.html |
246б |
| 007 Mathematical formulation of random forest classifiers.html |
263б |
| 007 Operators_en.srt |
6.63Кб |
| 007 Operators.mp4 |
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| 007 Original academic research article.html |
241б |
| 007 Stacking and merging arrays_en.srt |
8.43Кб |
| 007 Stacking and merging arrays.mp4 |
21.95Мб |
| 007 Support vector machines example III - parameter tuning_en.srt |
9.53Кб |
| 007 Support vector machines example III - parameter tuning.mp4 |
17.83Мб |
| 007 The super keyword_en.srt |
5.59Кб |
| 007 The super keyword.mp4 |
9.13Мб |
| 007 Tic tac toe with Q learning implementation VII_en.srt |
8.12Кб |
| 007 Tic tac toe with Q learning implementation VII.mp4 |
49.80Мб |
| 007 What is value iteration_en.srt |
8.26Кб |
| 007 What is value iteration.mp4 |
24.23Мб |
| 007 Why to use the so-called anchor boxes_en.srt |
8.79Кб |
| 007 Why to use the so-called anchor boxes.mp4 |
19.94Мб |
| 007 YOLO algorithm implementation VII_en.srt |
4.57Кб |
| 007 YOLO algorithm implementation VII.mp4 |
27.94Мб |
| 008 (!!!) Python lists and arrays.html |
628б |
| 008 Conditional statements_en.srt |
5.36Кб |
| 008 Conditional statements.mp4 |
8.57Мб |
| 008 Decision tree implementation III - identifying cancer_en.srt |
6.66Кб |
| 008 Decision tree implementation III - identifying cancer.mp4 |
32.45Мб |
| 008 Drawing lines on video frames_en.srt |
11.80Кб |
| 008 Drawing lines on video frames.mp4 |
32.69Мб |
| 008 Filter_en.srt |
4.65Кб |
| 008 Filter.mp4 |
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| 008 Function (method) override_en.srt |
3.16Кб |
| 008 Function (method) override.mp4 |
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| 008 Hierarchical clustering - market segmentation_en.srt |
12.46Кб |
| 008 Hierarchical clustering - market segmentation.mp4 |
29.00Мб |
| 008 Mathematical formulation of boosting.html |
290б |
| 008 Mathematical formulation of convolution neural networks.html |
290б |
| 008 Mathematical formulation of k-nearest neighbor classifier.html |
276б |
| 008 Optimization with gradient descent_en.srt |
11.36Кб |
| 008 Optimization with gradient descent.mp4 |
39.92Мб |
| 008 Original academic research article.html |
266б |
| 008 Support vector machine example IV - digit recognition_en.srt |
12.40Кб |
| 008 Support vector machine example IV - digit recognition.mp4 |
22.10Мб |
| 008 Tic tac toe with Q learning implementation VIII_en.srt |
8.82Кб |
| 008 Tic tac toe with Q learning implementation VIII.mp4 |
49.82Мб |
| 008 What are the most relevant built-in functions_en.srt |
5.51Кб |
| 008 What are the most relevant built-in functions.mp4 |
7.63Мб |
| 008 What is policy iteration_en.srt |
5.12Кб |
| 008 What is policy iteration.mp4 |
6.98Мб |
| 009 Gradient descent with backpropagation_en.srt |
8.39Кб |
| 009 Gradient descent with backpropagation.mp4 |
24.20Мб |
| 009 How to use multiple conditions_en.srt |
10.08Кб |
| 009 How to use multiple conditions.mp4 |
15.96Мб |
| 009 Mathematical formulation of clustering.html |
629б |
| 009 Mathematical formulation of decision trees.html |
356б |
| 009 Mathematical formulation of reinforcement learning.html |
255б |
| 009 Running time comparison arrays and lists.html |
1.34Кб |
| 009 Support vector machine example V - digit recognition_en.srt |
7.14Кб |
| 009 Support vector machine example V - digit recognition.mp4 |
14.49Мб |
| 009 Testing lane detection algorithm_en.srt |
3.28Кб |
| 009 Testing lane detection algorithm.mp4 |
16.08Мб |
| 009 What are tuples_en.srt |
4.99Кб |
| 009 What are tuples.mp4 |
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| 009 What is polymorphism_en.srt |
5.86Кб |
| 009 What is polymorphism.mp4 |
16.18Мб |
| 009 What is recursion_en.srt |
11.56Кб |
| 009 What is recursion.mp4 |
17.38Мб |
| 010 Advantages and disadvantages_en.srt |
3.72Кб |
| 010 Advantages and disadvantages.mp4 |
6.00Мб |
| 010 Backpropagation explained_en.srt |
16.09Кб |
| 010 Backpropagation explained.mp4 |
46.26Мб |
| 010 Local vs global variables_en.srt |
5.33Кб |
| 010 Local vs global variables.mp4 |
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| 010 Logical operators_en.srt |
4.46Кб |
| 010 Logical operators.mp4 |
8.05Мб |
| 010 Mutability and immutability_en.srt |
6.03Кб |
| 010 Mutability and immutability.mp4 |
8.70Мб |
| 010 Polymorphism and abstraction example_en.srt |
6.63Кб |
| 010 Polymorphism and abstraction example.mp4 |
13.72Мб |
| 011 Loops - for loop_en.srt |
7.75Кб |
| 011 Loops - for loop.mp4 |
9.56Мб |
| 011 Mathematical formulation of feed-forward neural networks.html |
261б |
| 011 Mathematical formulation of Support Vector Machines (SVMs).html |
419б |
| 011 Modules_en.srt |
7.52Кб |
| 011 Modules.mp4 |
11.04Мб |
| 011 The __main__ function_en.srt |
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| 011 The __main__ function.mp4 |
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| 011 What are linked list data structures_en.srt |
11.72Кб |
| 011 What are linked list data structures.mp4 |
20.75Мб |
| 012 Doubly linked list implementation in Python_en.srt |
6.85Кб |
| 012 Doubly linked list implementation in Python.mp4 |
11.44Мб |
| 012 Loops - while loop_en.srt |
5.54Кб |
| 012 Loops - while loop.mp4 |
7.55Мб |
| 012 The __str__ function_en.srt |
4.02Кб |
| 012 The __str__ function.mp4 |
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| 013 Comparing objects - overriding functions_en.srt |
10.12Кб |
| 013 Comparing objects - overriding functions.mp4 |
17.11Мб |
| 013 Hashing and O(1) running time complexity_en.srt |
11.15Кб |
| 013 Hashing and O(1) running time complexity.mp4 |
23.11Мб |
| 013 What are nested loops_en.srt |
3.51Кб |
| 013 What are nested loops.mp4 |
5.95Мб |
| 014 Dictionaries in Python_en.srt |
12.04Кб |
| 014 Dictionaries in Python.mp4 |
19.44Мб |
| 014 Enumerate_en.srt |
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| 014 Enumerate.mp4 |
7.69Мб |
| 015 Break and continue_en.srt |
7.01Кб |
| 015 Break and continue.mp4 |
9.92Мб |
| 015 Sets in Python_en.srt |
10.93Кб |
| 015 Sets in Python.mp4 |
26.05Мб |
| 016 Calculating Fibonacci-numbers_en.srt |
3.24Кб |
| 016 Calculating Fibonacci-numbers.mp4 |
4.02Мб |
| 016 Sorting_en.srt |
12.93Кб |
| 016 Sorting.mp4 |
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