[GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python

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[GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python

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文件列表

  • 54 - COURSE MATERIALS (DOWNLOADS)/001 PythonMachineLearning.zip 592.6 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/006 Tic tac toe with Q learning implementation VI.mp4 104.3 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/005 YOLO algorithm implementation V.mp4 100.5 MB
  • 05 - Linear Regression/004 Linear regression implementation I.mp4 95.2 MB
  • 19 - Single Layer Networks Implementation/002 Simple neural network implementation - Iris dataset.mp4 89.1 MB
  • 09 - Naive Bayes Classifier/006 Naive Bayes example - clustering news.mp4 82.7 MB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/003 Tic Tac Toe with deep Q learning implementation III.mp4 77.9 MB
  • 40 - Face Detection with Viola-Jones Method Implementation/002 Face detection implementation II - CascadeClassifier.mp4 74.1 MB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/003 Bounding boxes and anchor boxes.mp4 74.0 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/004 YOLO algorithm implementation IV.mp4 73.1 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/005 Getting the useful region of the image - masking.mp4 67.9 MB
  • 16 - Machine Learning Project I - Face Recognition/004 Understanding eigenfaces.mp4 66.0 MB
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/002 Region proposals and convolutional neural networks (CNNs).mp4 63.6 MB
  • 26 - Machine Learning Project III - Identifying Objects with CNNs/004 Tuning the parameters - regularization.mp4 63.4 MB
  • 06 - Logistic Regression/004 Logistic regression example II- credit scoring.mp4 61.4 MB
  • 25 - Convolutional Neural Networks (CNNs) Implementation/002 Handwritten digit classification II.mp4 58.3 MB
  • 25 - Convolutional Neural Networks (CNNs) Implementation/001 Handwritten digit classification I.mp4 57.0 MB
  • 42 - Histogram of Oriented Gradients (HOG) Implementation/001 Showing the HOG features programatically.mp4 56.0 MB
  • 31 - Exploration vs. Exploitation Problem/003 N-armed bandit problem implementation.mp4 55.9 MB
  • 42 - Histogram of Oriented Gradients (HOG) Implementation/003 Face detection with HOG implementation II.mp4 54.9 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/003 YOLO algorithm - intersection over union.mp4 53.9 MB
  • 47 - SSD Algorithm Implementation/004 SSD implementation IV.mp4 53.0 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/008 Tic tac toe with Q learning implementation VIII.mp4 52.2 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/007 Tic tac toe with Q learning implementation VII.mp4 52.2 MB
  • 30 - Markov Decision Process (MDP) Theory/003 Markov decision processes - equations.mp4 52.1 MB
  • 08 - K-Nearest Neighbor Classifier/006 K-nearest neighbor implementation II.mp4 50.9 MB
  • 18 - Feed-Forward Neural Network Theory/010 Backpropagation explained.mp4 48.5 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/004 Tic tac toe with Q learning implementation IV.mp4 48.4 MB
  • 16 - Machine Learning Project I - Face Recognition/002 Understanding the dataset.mp4 48.1 MB
  • 05 - Linear Regression/002 Linear regression theory - optimization.mp4 47.2 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/006 Detecting lines - what is Hough transformation.mp4 47.2 MB
  • 26 - Machine Learning Project III - Identifying Objects with CNNs/003 Fitting the model.mp4 45.8 MB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/002 Basic concept behind SSD algorithm (architecture).mp4 45.6 MB
  • 09 - Naive Bayes Classifier/001 What is the naive Bayes classifier.mp4 44.4 MB
  • 39 - Viola-Jones Face Detection Algorithm Theory/001 Viola-Jones algorithm.mp4 42.9 MB
  • 11 - Decision Trees/002 Decision trees introduction - entropy.mp4 42.8 MB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/002 Tic Tac Toe with deep Q learning implementation II.mp4 42.6 MB
  • 06 - Logistic Regression/001 What is logistic regression.mp4 42.2 MB
  • 21 - Deep Neural Networks Theory/004 Gradient descent and stochastic gradient descent.mp4 42.0 MB
  • 05 - Linear Regression/001 What is linear regression.mp4 41.9 MB
  • 18 - Feed-Forward Neural Network Theory/008 Optimization with gradient descent.mp4 41.9 MB
  • 05 - Linear Regression/003 Linear regression theory - gradient descent.mp4 41.4 MB
  • 13 - Boosting/006 Boosting implementation II -wine classification.mp4 40.5 MB
  • 09 - Naive Bayes Classifier/004 What is text clustering.mp4 40.4 MB
  • 37 - Handling Images and Pixels/004 Why convolution is so important in image processing.mp4 40.3 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/002 YOLO algorithm - grid cells.mp4 40.2 MB
  • 14 - Principal Component Analysis (PCA)/001 Principal component analysis (PCA) introduction.mp4 40.1 MB
  • 11 - Decision Trees/003 Decision trees introduction - information gain.mp4 40.1 MB
  • 23 - Machine Learning Project II - Smile Detector/003 Reading the images and constructing the dataset II.mp4 39.9 MB
  • 15 - Clustering/003 K-means clustering - text clustering.mp4 39.5 MB
  • 03 - Artificial Intelligence Basics/002 Types of artificial intelligence learning.mp4 38.8 MB
  • 10 - Support Vector Machines (SVMs)/005 Support vector machine example I - simple.mp4 38.6 MB
  • 13 - Boosting/004 Boosting introduction - final formula.mp4 38.6 MB
  • 19 - Single Layer Networks Implementation/001 Simple neural network implementation - XOR problem.mp4 38.5 MB
  • 42 - Histogram of Oriented Gradients (HOG) Implementation/004 Face detection with HOG implementation III.mp4 37.8 MB
  • 26 - Machine Learning Project III - Identifying Objects with CNNs/001 What is the CIFAR-10 dataset.mp4 37.8 MB
  • 25 - Convolutional Neural Networks (CNNs) Implementation/003 Handwritten digit classification III.mp4 36.9 MB
  • 18 - Feed-Forward Neural Network Theory/005 Neural networks - the big picture.mp4 36.7 MB
  • 37 - Handling Images and Pixels/002 Handling pixel intensities I.mp4 36.3 MB
  • 10 - Support Vector Machines (SVMs)/004 Kernel functions.mp4 35.8 MB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/003 Histogram of oriented gradients - magnitude and angle.mp4 35.6 MB
  • 06 - Logistic Regression/005 Logistic regression example III - credit scoring.mp4 35.1 MB
  • 27 - Recurrent Neural Networks (RNNs) Theory/004 Long-short term memory (LSTM) model.mp4 35.0 MB
  • 03 - Artificial Intelligence Basics/003 Fundamentals of statistics.mp4 34.8 MB
  • 06 - Logistic Regression/003 Logistic regression example I - sigmoid function.mp4 34.8 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/008 Drawing lines on video frames.mp4 34.3 MB
  • 11 - Decision Trees/008 Decision tree implementation III - identifying cancer.mp4 34.0 MB
  • 42 - Histogram of Oriented Gradients (HOG) Implementation/005 Face detection with HOG implementation IV.mp4 33.9 MB
  • 12 - Random Forest Classifier/006 Random forests example III - OCR parameter tuning.mp4 33.5 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/007 Convolutional neural networks - illustration.mp4 33.4 MB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/005 Tic Tac Toe with deep Q learning implementation V.mp4 32.8 MB
  • 13 - Boosting/005 Boosting implementation I - iris dataset.mp4 32.6 MB
  • 47 - SSD Algorithm Implementation/001 SSD implementation I.mp4 32.4 MB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/002 Histogram of oriented gradients - gradient kernel.mp4 32.0 MB
  • 10 - Support Vector Machines (SVMs)/002 Linearly separable problems.mp4 31.8 MB
  • 15 - Clustering/008 Hierarchical clustering - market segmentation.mp4 30.4 MB
  • 27 - Recurrent Neural Networks (RNNs) Theory/002 Recurrent neural networks basics.mp4 30.0 MB
  • 22 - Deep Neural Networks Implementation/004 Multiclass classification implementation I.mp4 29.9 MB
  • 18 - Feed-Forward Neural Network Theory/004 Why to use activation functions.mp4 29.8 MB
  • 30 - Markov Decision Process (MDP) Theory/004 Markov decision processes - illustration.mp4 29.6 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/007 YOLO algorithm implementation VII.mp4 29.3 MB
  • 11 - Decision Trees/001 Decision trees introduction - basics.mp4 28.7 MB
  • 27 - Recurrent Neural Networks (RNNs) Theory/003 Vanishing and exploding gradients problem.mp4 28.5 MB
  • 21 - Deep Neural Networks Theory/005 Hyperparameters.mp4 28.2 MB
  • 14 - Principal Component Analysis (PCA)/002 Principal component analysis example.mp4 28.1 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/006 Convolutional neural networks - flattening.mp4 28.1 MB
  • 22 - Deep Neural Networks Implementation/005 Multiclass classification implementation II.mp4 28.0 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/003 Tic tac toe with Q learning implementation III.mp4 27.5 MB
  • 21 - Deep Neural Networks Theory/002 Activation functions revisited.mp4 27.5 MB
  • 22 - Deep Neural Networks Implementation/003 Deep neural network implementation III.mp4 27.4 MB
  • 51 - Appendix #3 - Data Structures in Python/015 Sets in Python.mp4 27.3 MB
  • 01 - Introduction/001 Introduction.mp4 27.1 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/005 Convolutional neural networks - pooling.mp4 26.8 MB
  • 07 - Cross Validation/002 Cross validation example.mp4 26.3 MB
  • 23 - Machine Learning Project II - Smile Detector/002 Reading the images and constructing the dataset I.mp4 26.3 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/004 How to train the YOLO algorithm.mp4 26.3 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/001 Convolutional neural networks basics.mp4 26.2 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/003 YOLO algorithm implementation III.mp4 25.9 MB
  • 39 - Viola-Jones Face Detection Algorithm Theory/003 Integral images.mp4 25.7 MB
  • 07 - Cross Validation/001 What is cross validation.mp4 25.6 MB
  • 30 - Markov Decision Process (MDP) Theory/007 What is value iteration.mp4 25.4 MB
  • 18 - Feed-Forward Neural Network Theory/009 Gradient descent with backpropagation.mp4 25.4 MB
  • 18 - Feed-Forward Neural Network Theory/001 Artificial neural networks - inspiration.mp4 25.3 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/002 YOLO algorithm implementation II.mp4 24.9 MB
  • 51 - Appendix #3 - Data Structures in Python/016 Sorting.mp4 24.9 MB
  • 16 - Machine Learning Project I - Face Recognition/003 Finding optimal number of principal components (eigenvectors).mp4 24.8 MB
  • 39 - Viola-Jones Face Detection Algorithm Theory/004 Boosting in computer vision.mp4 24.5 MB
  • 30 - Markov Decision Process (MDP) Theory/001 Markov decision processes basics I.mp4 24.3 MB
  • 19 - Single Layer Networks Implementation/003 Credit scoring with simple neural networks.mp4 24.3 MB
  • 51 - Appendix #3 - Data Structures in Python/013 Hashing and O(1) running time complexity.mp4 24.2 MB
  • 10 - Support Vector Machines (SVMs)/003 Non-linearly separable problems.mp4 24.1 MB
  • 16 - Machine Learning Project I - Face Recognition/001 The Olivetti dataset.mp4 23.9 MB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/001 YOLO algorithm implementation I.mp4 23.9 MB
  • 06 - Logistic Regression/002 Logistic regression and maximum likelihood estimation.mp4 23.8 MB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/004 Histogram of oriented gradients - normalization.mp4 23.7 MB
  • 14 - Principal Component Analysis (PCA)/003 Principal component analysis example II.mp4 23.4 MB
  • 50 - Appendix #2 - Functions/003 Positional arguments and keyword arguments.mp4 23.3 MB
  • 39 - Viola-Jones Face Detection Algorithm Theory/002 Haar-features.mp4 23.2 MB
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/003 Detecting bounding boxes with regression.mp4 23.2 MB
  • 10 - Support Vector Machines (SVMs)/008 Support vector machine example IV - digit recognition.mp4 23.2 MB
  • 16 - Machine Learning Project I - Face Recognition/006 Using cross-validation.mp4 23.0 MB
  • 53 - Appendix #5 - NumPy/007 Stacking and merging arrays.mp4 23.0 MB
  • 08 - K-Nearest Neighbor Classifier/003 Distance metrics - Euclidean-distance.mp4 22.8 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/005 Tic tac toe with Q learning implementation V.mp4 22.8 MB
  • 18 - Feed-Forward Neural Network Theory/003 Artificial neural networks - the model.mp4 22.6 MB
  • 32 - Q Learning Theory/003 Q learning illustration.mp4 22.5 MB
  • 27 - Recurrent Neural Networks (RNNs) Theory/001 Why do recurrent neural networks are important.mp4 22.3 MB
  • 15 - Clustering/005 DBSCAN example.mp4 22.2 MB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/001 Tic Tac Toe with deep Q learning implementation I.mp4 22.1 MB
  • 51 - Appendix #3 - Data Structures in Python/011 What are linked list data structures.mp4 21.8 MB
  • 15 - Clustering/007 Hierarchical clustering example.mp4 21.3 MB
  • 10 - Support Vector Machines (SVMs)/001 What are Support Vector Machines (SVMs).mp4 21.1 MB
  • 11 - Decision Trees/004 The Gini-index approach.mp4 21.1 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/003 Time series analysis example III.mp4 21.0 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/007 Why to use the so-called anchor boxes.mp4 20.9 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/002 Tic tac toe with Q learning implementation II.mp4 20.8 MB
  • 31 - Exploration vs. Exploitation Problem/002 N-armed bandit problem introduction.mp4 20.5 MB
  • 15 - Clustering/002 K-means clustering example.mp4 20.5 MB
  • 51 - Appendix #3 - Data Structures in Python/014 Dictionaries in Python.mp4 20.4 MB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/001 Histogram of oriented gradients basics.mp4 20.2 MB
  • 22 - Deep Neural Networks Implementation/002 Deep neural network implementation II.mp4 19.8 MB
  • 40 - Face Detection with Viola-Jones Method Implementation/005 Face detection implementation V - detecting faces real-time.mp4 19.8 MB
  • 47 - SSD Algorithm Implementation/003 SSD implementation III.mp4 19.8 MB
  • 51 - Appendix #3 - Data Structures in Python/006 Lists in Python - advanced operations.mp4 19.5 MB
  • 53 - Appendix #5 - NumPy/003 Dimension of arrays.mp4 19.3 MB
  • 40 - Face Detection with Viola-Jones Method Implementation/003 Face detection implementation III - CascadeClassifier parameters.mp4 19.3 MB
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/001 The standard convolutional neural network (CNN) way.mp4 19.2 MB
  • 51 - Appendix #3 - Data Structures in Python/001 How to measure the running time of algorithms.mp4 19.2 MB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/001 What is the SSD algorithm.mp4 18.9 MB
  • 40 - Face Detection with Viola-Jones Method Implementation/004 Face detection implementation IV - tuning the parameters.mp4 18.9 MB
  • 10 - Support Vector Machines (SVMs)/007 Support vector machines example III - parameter tuning.mp4 18.7 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/003 Using the constructor.mp4 18.7 MB
  • 50 - Appendix #2 - Functions/009 What is recursion.mp4 18.2 MB
  • 22 - Deep Neural Networks Implementation/001 Deep neural network implementation I.mp4 18.2 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/013 Comparing objects - overriding functions.mp4 17.9 MB
  • 53 - Appendix #5 - NumPy/006 Reshape.mp4 17.8 MB
  • 33 - Q Learning Implementation (Tic Tac Toe)/001 Tic tac toe with Q learning implementation I.mp4 17.6 MB
  • 53 - Appendix #5 - NumPy/002 Creating and updating arrays.mp4 17.6 MB
  • 53 - Appendix #5 - NumPy/004 Indexes and slicing.mp4 17.5 MB
  • 15 - Clustering/001 K-means clustering introduction.mp4 17.4 MB
  • 15 - Clustering/006 Hierarchical clustering introduction.mp4 17.4 MB
  • 08 - K-Nearest Neighbor Classifier/005 K-nearest neighbor implementation I.mp4 17.3 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/004 What is Canny edge detection.mp4 17.2 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/005 YOLO algorithm - loss function.mp4 17.1 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/009 What is polymorphism.mp4 17.0 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/009 Testing lane detection algorithm.mp4 16.9 MB
  • 12 - Random Forest Classifier/002 Bagging introduction.mp4 16.9 MB
  • 49 - Appendix #1 - Python Basics/009 How to use multiple conditions.mp4 16.7 MB
  • 13 - Boosting/001 Boosting introduction - basics.mp4 16.5 MB
  • 12 - Random Forest Classifier/001 Pruning introduction.mp4 16.2 MB
  • 32 - Q Learning Theory/002 Q learning introduction - the algorithm.mp4 16.2 MB
  • 42 - Histogram of Oriented Gradients (HOG) Implementation/002 Face detection with HOG implementation I.mp4 16.2 MB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/004 Tic Tac Toe with deep Q learning implementation IV.mp4 16.2 MB
  • 30 - Markov Decision Process (MDP) Theory/005 Bellman-equation.mp4 16.2 MB
  • 21 - Deep Neural Networks Theory/003 Loss functions.mp4 16.2 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/005 Private variables and name mangling.mp4 16.0 MB
  • 08 - K-Nearest Neighbor Classifier/002 Concept of lazy learning.mp4 16.0 MB
  • 10 - Support Vector Machines (SVMs)/006 Support vector machine example II - iris dataset.mp4 15.8 MB
  • 47 - SSD Algorithm Implementation/005 SSD implementation V.mp4 15.7 MB
  • 08 - K-Nearest Neighbor Classifier/004 Bias and variance trade-off.mp4 15.4 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/004 Class variables and instance variables.mp4 15.4 MB
  • 09 - Naive Bayes Classifier/005 Text clustering - inverse document frequency (TF-IDF).mp4 15.3 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/005 Time series analysis example V.mp4 15.3 MB
  • 37 - Handling Images and Pixels/006 Image processing - edge detection kernel.mp4 15.3 MB
  • 49 - Appendix #1 - Python Basics/004 Strings.mp4 15.3 MB
  • 10 - Support Vector Machines (SVMs)/009 Support vector machine example V - digit recognition.mp4 15.2 MB
  • 30 - Markov Decision Process (MDP) Theory/002 Markov decision processes basics II.mp4 14.8 MB
  • 11 - Decision Trees/007 Decision trees implementation II - parameter tuning.mp4 14.8 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/001 Time series analysis example I.mp4 14.8 MB
  • 03 - Artificial Intelligence Basics/001 Why to learn artificial intelligence and machine learning.mp4 14.7 MB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/004 Feature maps and convolution layers.mp4 14.5 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/010 Polymorphism and abstraction example.mp4 14.4 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/002 Lane detection - handling videos.mp4 14.4 MB
  • 08 - K-Nearest Neighbor Classifier/001 What is the k-nearest neighbor classifier.mp4 14.4 MB
  • 12 - Random Forest Classifier/004 Random forests example I - iris dataset.mp4 14.2 MB
  • 13 - Boosting/003 Boosting introduction - equations.mp4 14.1 MB
  • 16 - Machine Learning Project I - Face Recognition/005 Constructing the machine learning models.mp4 14.0 MB
  • 37 - Handling Images and Pixels/003 Handling pixel intensities II.mp4 13.9 MB
  • 11 - Decision Trees/006 Decision trees implementation I.mp4 13.8 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/002 Time series analysis example II.mp4 13.7 MB
  • 37 - Handling Images and Pixels/005 Image processing - blur operation.mp4 13.3 MB
  • 49 - Appendix #1 - Python Basics/005 String slicing.mp4 13.3 MB
  • 23 - Machine Learning Project II - Smile Detector/005 Evaluating and testing the model.mp4 13.1 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/001 What is the YOLO approach.mp4 13.0 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/006 Time series analysis example VI.mp4 13.0 MB
  • 51 - Appendix #3 - Data Structures in Python/004 What are array data structures II.mp4 12.9 MB
  • 12 - Random Forest Classifier/003 Random forest classifier introduction.mp4 12.9 MB
  • 51 - Appendix #3 - Data Structures in Python/003 What are array data structures I.mp4 12.9 MB
  • 05 - Linear Regression/005 Linear regression implementation II.mp4 12.8 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/002 Feature selection.mp4 12.7 MB
  • 31 - Exploration vs. Exploitation Problem/004 Applications AB testing in marketing.mp4 12.7 MB
  • 18 - Feed-Forward Neural Network Theory/007 How to measure the error of the network.mp4 12.6 MB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/003 Lane detection - first transformations.mp4 12.5 MB
  • 32 - Q Learning Theory/001 What is Q learning.mp4 12.4 MB
  • 51 - Appendix #3 - Data Structures in Python/012 Doubly linked list implementation in Python.mp4 12.0 MB
  • 51 - Appendix #3 - Data Structures in Python/007 Lists in Python - list comprehension.mp4 11.9 MB
  • 15 - Clustering/004 DBSCAN introduction.mp4 11.9 MB
  • 13 - Boosting/002 Boosting introduction - illustration.mp4 11.7 MB
  • 09 - Naive Bayes Classifier/003 Naive Bayes classifier implementation.mp4 11.6 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/011 Modules.mp4 11.6 MB
  • 18 - Feed-Forward Neural Network Theory/002 Artificial neural networks - layers.mp4 11.6 MB
  • 37 - Handling Images and Pixels/001 Images and pixel intensities.mp4 11.3 MB
  • 49 - Appendix #1 - Python Basics/007 Operators.mp4 11.2 MB
  • 08 - K-Nearest Neighbor Classifier/007 K-nearest neighbor implementation III.mp4 11.0 MB
  • 51 - Appendix #3 - Data Structures in Python/005 Lists in Python.mp4 11.0 MB
  • 12 - Random Forest Classifier/005 Random forests example II - credit scoring.mp4 10.4 MB
  • 53 - Appendix #5 - NumPy/005 Types.mp4 10.4 MB
  • 49 - Appendix #1 - Python Basics/015 Break and continue.mp4 10.4 MB
  • 39 - Viola-Jones Face Detection Algorithm Theory/005 Cascading.mp4 10.4 MB
  • 02 - Environment Setup/003 Installing TensorFlow and Keras.mp4 10.2 MB
  • 50 - Appendix #2 - Functions/002 Defining functions.mp4 10.1 MB
  • 49 - Appendix #1 - Python Basics/011 Loops - for loop.mp4 10.0 MB
  • 23 - Machine Learning Project II - Smile Detector/004 Building the deep neural network model.mp4 10.0 MB
  • 34 - Deep Q Learning Theory/001 What is deep Q learning.mp4 9.7 MB
  • 21 - Deep Neural Networks Theory/001 Deep neural networks.mp4 9.7 MB
  • 09 - Naive Bayes Classifier/002 Naive Bayes classifier illustration.mp4 9.7 MB
  • 50 - Appendix #2 - Functions/006 Yield operator.mp4 9.6 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/007 The super keyword.mp4 9.6 MB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/006 YOLO algorithm - non-max suppression.mp4 9.6 MB
  • 37 - Handling Images and Pixels/007 Image processing - sharpen operation.mp4 9.5 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/003 Convolutional neural networks - kernel.mp4 9.3 MB
  • 24 - Convolutional Neural Networks (CNNs) Theory/004 Convolutional neural networks - kernel II.mp4 9.3 MB
  • 51 - Appendix #3 - Data Structures in Python/010 Mutability and immutability.mp4 9.1 MB
  • 36 - ### COMPUTER VISION ###/001 Evolution of computer vision related algorithms.mp4 9.1 MB
  • 49 - Appendix #1 - Python Basics/008 Conditional statements.mp4 9.0 MB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/004 Time series analysis example IV.mp4 8.9 MB
  • 49 - Appendix #1 - Python Basics/006 Type casting.mp4 8.6 MB
  • 53 - Appendix #5 - NumPy/001 What is the key advantage of NumPy.mp4 8.6 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/006 What is inheritance in OOP.mp4 8.5 MB
  • 50 - Appendix #2 - Functions/001 What are functions.mp4 8.5 MB
  • 49 - Appendix #1 - Python Basics/010 Logical operators.mp4 8.4 MB
  • 20 - Deep Learning/001 Types of neural networks.mp4 8.4 MB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/005 Histogram of oriented gradients - big picture.mp4 8.2 MB
  • 50 - Appendix #2 - Functions/010 Local vs global variables.mp4 8.2 MB
  • 49 - Appendix #1 - Python Basics/002 What are the basic data types.mp4 8.1 MB
  • 49 - Appendix #1 - Python Basics/014 Enumerate.mp4 8.1 MB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/012 The __str__ function.mp4 8.0 MB
  • 26 - Machine Learning Project III - Identifying Objects with CNNs/002 Preprocessing the data.mp4 8.0 MB
  • 53 - Appendix #5 - NumPy/008 Filter.mp4 8.0 MB
  • 31 - Exploration vs. Exploitation Problem/001 Exploration vs exploitation problem.mp4 8.0 MB
  • 40 - Face Detection with Viola-Jones Method Implementation/001 Face detection implementation I - installing OpenCV.mp4 8.0 MB
  • 50 - Appendix #2 - Functions/008 What are the most relevant built-in functions.mp4 8.0 MB
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  • 29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning.mp4 6.9 MB
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  • 48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction.mp4 4.2 MB
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  • 31 - Exploration vs. Exploitation Problem/004 Applications AB testing in marketing_en.srt 5.8 kB
  • 24 - Convolutional Neural Networks (CNNs) Theory/003 Convolutional neural networks - kernel_en.srt 5.8 kB
  • 16 - Machine Learning Project I - Face Recognition/005 Constructing the machine learning models_en.srt 5.8 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/007 The super keyword_en.srt 5.7 kB
  • 12 - Random Forest Classifier/004 Random forests example I - iris dataset_en.srt 5.7 kB
  • 39 - Viola-Jones Face Detection Algorithm Theory/005 Cascading_en.srt 5.7 kB
  • 49 - Appendix #1 - Python Basics/012 Loops - while loop_en.srt 5.7 kB
  • 19 - Single Layer Networks Implementation/003 Credit scoring with simple neural networks_en.srt 5.7 kB
  • 50 - Appendix #2 - Functions/008 What are the most relevant built-in functions_en.srt 5.6 kB
  • 24 - Convolutional Neural Networks (CNNs) Theory/002 Feature selection_en.srt 5.6 kB
  • 08 - K-Nearest Neighbor Classifier/007 K-nearest neighbor implementation III_en.srt 5.5 kB
  • 40 - Face Detection with Viola-Jones Method Implementation/003 Face detection implementation III - CascadeClassifier parameters_en.srt 5.5 kB
  • 49 - Appendix #1 - Python Basics/008 Conditional statements_en.srt 5.5 kB
  • 50 - Appendix #2 - Functions/010 Local vs global variables_en.srt 5.5 kB
  • 49 - Appendix #1 - Python Basics/006 Type casting_en.srt 5.4 kB
  • 35 - Deep Q Learning Implementation (Tic Tac Toe)/001 Tic Tac Toe with deep Q learning implementation I_en.srt 5.4 kB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/001 What is the SSD algorithm_en.srt 5.4 kB
  • 09 - Naive Bayes Classifier/003 Naive Bayes classifier implementation_en.srt 5.3 kB
  • 08 - K-Nearest Neighbor Classifier/004 Bias and variance trade-off_en.srt 5.3 kB
  • 33 - Q Learning Implementation (Tic Tac Toe)/001 Tic tac toe with Q learning implementation I_en.srt 5.3 kB
  • 30 - Markov Decision Process (MDP) Theory/008 What is policy iteration_en.srt 5.2 kB
  • 08 - K-Nearest Neighbor Classifier/002 Concept of lazy learning_en.srt 5.2 kB
  • 01 - Introduction/001 Introduction_en.srt 5.2 kB
  • 36 - ### COMPUTER VISION ###/001 Evolution of computer vision related algorithms_en.srt 5.1 kB
  • 51 - Appendix #3 - Data Structures in Python/009 What are tuples_en.srt 5.1 kB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/001 Histogram of oriented gradients basics_en.srt 5.1 kB
  • 49 - Appendix #1 - Python Basics/014 Enumerate_en.srt 5.1 kB
  • 20 - Deep Learning/001 Types of neural networks_en.srt 5.0 kB
  • 23 - Machine Learning Project II - Smile Detector/005 Evaluating and testing the model_en.srt 4.9 kB
  • 31 - Exploration vs. Exploitation Problem/001 Exploration vs exploitation problem_en.srt 4.8 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/006 What is inheritance in OOP_en.srt 4.8 kB
  • 53 - Appendix #5 - NumPy/008 Filter_en.srt 4.8 kB
  • 34 - Deep Q Learning Theory/003 Remember and replay_en.srt 4.8 kB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/007 YOLO algorithm implementation VII_en.srt 4.7 kB
  • 47 - SSD Algorithm Implementation/005 SSD implementation V_en.srt 4.7 kB
  • 37 - Handling Images and Pixels/007 Image processing - sharpen operation_en.srt 4.7 kB
  • 50 - Appendix #2 - Functions/011 The __main__ function_en.srt 4.7 kB
  • 27 - Recurrent Neural Networks (RNNs) Theory/005 Gated recurrent units (GRUs)_en.srt 4.6 kB
  • 51 - Appendix #3 - Data Structures in Python/002 Data structures introduction_en.srt 4.6 kB
  • 49 - Appendix #1 - Python Basics/010 Logical operators_en.srt 4.6 kB
  • 12 - Random Forest Classifier/005 Random forests example II - credit scoring_en.srt 4.5 kB
  • 23 - Machine Learning Project II - Smile Detector/004 Building the deep neural network model_en.srt 4.5 kB
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/005 Histogram of oriented gradients - big picture_en.srt 4.5 kB
  • 34 - Deep Q Learning Theory/002 Deep Q learning and ε-greedy strategy_en.srt 4.3 kB
  • 44 - You Only Look Once (YOLO) Algorithm Theory/006 YOLO algorithm - non-max suppression_en.srt 4.2 kB
  • 13 - Boosting/007 Boosting vs. bagging_en.srt 4.2 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/012 The __str__ function_en.srt 4.1 kB
  • 50 - Appendix #2 - Functions/005 Returning multiple values_en.srt 3.9 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/002 Class and objects basics_en.srt 3.8 kB
  • 10 - Support Vector Machines (SVMs)/010 Advantages and disadvantages_en.srt 3.8 kB
  • 16 - Machine Learning Project I - Face Recognition/006 Using cross-validation_en.srt 3.7 kB
  • 29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning_en.srt 3.6 kB
  • 24 - Convolutional Neural Networks (CNNs) Theory/007 Convolutional neural networks - illustration_en.srt 3.6 kB
  • 26 - Machine Learning Project III - Identifying Objects with CNNs/002 Preprocessing the data_en.srt 3.6 kB
  • 49 - Appendix #1 - Python Basics/013 What are nested loops_en.srt 3.6 kB
  • 28 - Recurrent Neural Networks (RNNs) Implementation/004 Time series analysis example IV_en.srt 3.6 kB
  • 40 - Face Detection with Viola-Jones Method Implementation/001 Face detection implementation I - installing OpenCV_en.srt 3.6 kB
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/004 What is the Fast R-CNN model_en.srt 3.5 kB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/005 Hard negative mining during training_en.srt 3.4 kB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/009 Testing lane detection algorithm_en.srt 3.4 kB
  • 49 - Appendix #1 - Python Basics/016 Calculating Fibonacci-numbers_en.srt 3.3 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/001 What is object oriented programming (OOP)_en.srt 3.3 kB
  • 52 - Appendix #4 - Object Oriented Programming (OOP)/008 Function (method) override_en.srt 3.2 kB
  • 11 - Decision Trees/005 Decision trees introduction - pros and cons_en.srt 3.2 kB
  • 48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction_en.srt 3.2 kB
  • 47 - SSD Algorithm Implementation/002 SSD implementation II_en.srt 3.2 kB
  • 50 - Appendix #2 - Functions/004 Returning values_en.srt 3.1 kB
  • 30 - Markov Decision Process (MDP) Theory/006 How to solve MDP problems_en.srt 3.1 kB
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/006 Regularization (data augmentation) and non-max suppression during training_en.srt 3.1 kB
  • 23 - Machine Learning Project II - Smile Detector/001 Understanding the classification problem_en.srt 3.1 kB
  • 02 - Environment Setup/003 Installing TensorFlow and Keras_en.srt 2.9 kB
  • 50 - Appendix #2 - Functions/007 Local and global variables_en.srt 2.7 kB
  • 45 - You Only Look Once (YOLO) Algorithm Implementation/006 YOLO algorithm implementation VI_en.srt 2.6 kB
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/005 What is the Faster R-CNN model_en.srt 2.6 kB
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/001 Lane detection - the problem_en.srt 2.5 kB
  • 49 - Appendix #1 - Python Basics/003 Booleans_en.srt 2.5 kB
  • 18 - Feed-Forward Neural Network Theory/006 Using bias nodes in the neural network_en.srt 2.3 kB
  • 02 - Environment Setup/002 Installing PyCharm and Python on Mac.html 1.6 kB
  • 02 - Environment Setup/001 Installing PyCharm and Python on Windows.html 1.6 kB
  • 53 - Appendix #5 - NumPy/009 Running time comparison arrays and lists.html 1.4 kB
  • 29 - ### REINFORCEMENT LEARNING ###/001 What is reinforcement learning.html 899 Bytes
  • 15 - Clustering/009 Mathematical formulation of clustering.html 629 Bytes
  • 51 - Appendix #3 - Data Structures in Python/008 (!!!) Python lists and arrays.html 628 Bytes
  • 04 - ### MACHINE LEARNING ###/001 Machine learning section.html 471 Bytes
  • 43 - Convolutional Neural Networks (CNNs) Based Approaches/006 Original academic research articles.html 453 Bytes
  • 10 - Support Vector Machines (SVMs)/011 Mathematical formulation of Support Vector Machines (SVMs).html 419 Bytes
  • 11 - Decision Trees/009 Mathematical formulation of decision trees.html 356 Bytes
  • 17 - ### NEURAL NETWORKS AND DEEP LEARNING ###/001 Neural networks and deep learning section.html 346 Bytes
  • 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/006 Original academic research article.html 318 Bytes
  • 39 - Viola-Jones Face Detection Algorithm Theory/006 Original academic research articles.html 311 Bytes
  • 13 - Boosting/008 Mathematical formulation of boosting.html 290 Bytes
  • 21 - Deep Neural Networks Theory/006 Mathematical formulation of deep neural networks.html 290 Bytes
  • 24 - Convolutional Neural Networks (CNNs) Theory/008 Mathematical formulation of convolution neural networks.html 290 Bytes
  • 14 - Principal Component Analysis (PCA)/004 Mathematical formulation of principle component analysis (PCA).html 282 Bytes
  • 08 - K-Nearest Neighbor Classifier/008 Mathematical formulation of k-nearest neighbor classifier.html 276 Bytes
  • 05 - Linear Regression/006 Mathematical formulation of linear regression.html 275 Bytes
  • 34 - Deep Q Learning Theory/004 Mathematical formulation of deep Q learning.html 272 Bytes
  • 44 - You Only Look Once (YOLO) Algorithm Theory/008 Original academic research article.html 266 Bytes
  • 06 - Logistic Regression/006 Mathematical formulation of logistic regression.html 263 Bytes
  • 12 - Random Forest Classifier/007 Mathematical formulation of random forest classifiers.html 263 Bytes
  • 32 - Q Learning Theory/004 Mathematical formulation of Q learning.html 262 Bytes
  • 18 - Feed-Forward Neural Network Theory/011 Mathematical formulation of feed-forward neural networks.html 261 Bytes
  • 27 - Recurrent Neural Networks (RNNs) Theory/006 Mathematical formulation of recurrent neural networks.html 258 Bytes
  • 30 - Markov Decision Process (MDP) Theory/009 Mathematical formulation of reinforcement learning.html 255 Bytes
  • 09 - Naive Bayes Classifier/007 Mathematical formulation of naive Bayes classifier.html 246 Bytes
  • 46 - Single-Shot MultiBox Detector (SSD) Theory/007 Original academic research article.html 241 Bytes
  • 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/007 Hough transformation illustration.html 191 Bytes
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  • 54 - COURSE MATERIALS (DOWNLOADS)/001 Course materials (source code and slides).html 66 Bytes
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