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

BT种子名称

分享给好友

BT种子基本信息

种子哈希：ce9f78f66c8fee6ff002f4129bc938d7d1e6cb6d
文档大小：7.6 GB
文档个数：652个文档
下载次数：1208次
下载速度：极快
收录时间：2022-04-14
最近下载：2025-05-16
DMCA/屏蔽：DMCA/屏蔽

下载磁力链接

magnet:?xt=urn:btih:CE9F78F66C8FEE6FF002F4129BC938D7D1E6CB6D
复制磁力链接到utorrent、Bitcomet、迅雷、115、百度网盘等下载工具进行下载。

下载BT种子

磁力链接种子下载迅雷下载二维码迅雷看看
磁力链接在线播放？试试百度云网盘离线下载播放！

[GigaCourse.Com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python的二维码

文档列表

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
49 - Appendix #1 - Python Basics/012 Loops - while loop.mp4 7.9 MB
51 - Appendix #3 - Data Structures in Python/009 What are tuples.mp4 7.9 MB
49 - Appendix #1 - Python Basics/001 First steps in Python.mp4 7.7 MB
50 - Appendix #2 - Functions/011 The __main__ function.mp4 7.7 MB
45 - You Only Look Once (YOLO) Algorithm Implementation/006 YOLO algorithm implementation VI.mp4 7.7 MB
34 - Deep Q Learning Theory/003 Remember and replay.mp4 7.3 MB
30 - Markov Decision Process (MDP) Theory/008 What is policy iteration.mp4 7.3 MB
13 - Boosting/007 Boosting vs. bagging.mp4 7.2 MB
46 - Single-Shot MultiBox Detector (SSD) Theory/006 Regularization (data augmentation) and non-max suppression during training.mp4 7.2 MB
51 - Appendix #3 - Data Structures in Python/002 Data structures introduction.mp4 7.0 MB
29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning.mp4 6.9 MB
52 - Appendix #4 - Object Oriented Programming (OOP)/008 Function (method) override.mp4 6.8 MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/004 What is the Fast R-CNN model.mp4 6.7 MB
27 - Recurrent Neural Networks (RNNs) Theory/005 Gated recurrent units (GRUs).mp4 6.7 MB
47 - SSD Algorithm Implementation/002 SSD implementation II.mp4 6.7 MB
46 - Single-Shot MultiBox Detector (SSD) Theory/005 Hard negative mining during training.mp4 6.4 MB
50 - Appendix #2 - Functions/005 Returning multiple values.mp4 6.3 MB
10 - Support Vector Machines (SVMs)/010 Advantages and disadvantages.mp4 6.3 MB
49 - Appendix #1 - Python Basics/013 What are nested loops.mp4 6.2 MB
11 - Decision Trees/005 Decision trees introduction - pros and cons.mp4 6.0 MB
30 - Markov Decision Process (MDP) Theory/006 How to solve MDP problems.mp4 6.0 MB
52 - Appendix #4 - Object Oriented Programming (OOP)/002 Class and objects basics.mp4 5.6 MB
52 - Appendix #4 - Object Oriented Programming (OOP)/001 What is object oriented programming (OOP).mp4 5.5 MB
23 - Machine Learning Project II - Smile Detector/001 Understanding the classification problem.mp4 5.0 MB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/001 Lane detection - the problem.mp4 4.6 MB
18 - Feed-Forward Neural Network Theory/006 Using bias nodes in the neural network.mp4 4.5 MB
50 - Appendix #2 - Functions/007 Local and global variables.mp4 4.5 MB
50 - Appendix #2 - Functions/004 Returning values.mp4 4.3 MB
49 - Appendix #1 - Python Basics/016 Calculating Fibonacci-numbers.mp4 4.2 MB
43 - Convolutional Neural Networks (CNNs) Based Approaches/005 What is the Faster R-CNN model.mp4 4.2 MB
48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction.mp4 4.2 MB
49 - Appendix #1 - Python Basics/003 Booleans.mp4 3.7 MB
09 - Naive Bayes Classifier/006 Naive Bayes example - clustering news_en.srt 18.9 kB
10 - Support Vector Machines (SVMs)/002 Linearly separable problems_en.srt 18.8 kB
05 - Linear Regression/004 Linear regression implementation I_en.srt 18.8 kB
19 - Single Layer Networks Implementation/001 Simple neural network implementation - XOR problem_en.srt 18.2 kB
19 - Single Layer Networks Implementation/002 Simple neural network implementation - Iris dataset_en.srt 18.2 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/005 Getting the useful region of the image - masking_en.srt 17.9 kB
37 - Handling Images and Pixels/004 Why convolution is so important in image processing_en.srt 17.4 kB
25 - Convolutional Neural Networks (CNNs) Implementation/002 Handwritten digit classification II_en.srt 17.2 kB
42 - Histogram of Oriented Gradients (HOG) Implementation/003 Face detection with HOG implementation II_en.srt 17.2 kB
33 - Q Learning Implementation (Tic Tac Toe)/006 Tic tac toe with Q learning implementation VI_en.srt 16.6 kB
06 - Logistic Regression/001 What is logistic regression_en.srt 16.5 kB
18 - Feed-Forward Neural Network Theory/010 Backpropagation explained_en.srt 16.5 kB
45 - You Only Look Once (YOLO) Algorithm Implementation/004 YOLO algorithm implementation IV_en.srt 16.3 kB
25 - Convolutional Neural Networks (CNNs) Implementation/001 Handwritten digit classification I_en.srt 15.8 kB
31 - Exploration vs. Exploitation Problem/003 N-armed bandit problem implementation_en.srt 15.5 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/006 Detecting lines - what is Hough transformation_en.srt 15.2 kB
39 - Viola-Jones Face Detection Algorithm Theory/001 Viola-Jones algorithm_en.srt 15.2 kB
13 - Boosting/006 Boosting implementation II -wine classification_en.srt 15.2 kB
45 - You Only Look Once (YOLO) Algorithm Implementation/005 YOLO algorithm implementation V_en.srt 15.1 kB
06 - Logistic Regression/003 Logistic regression example I - sigmoid function_en.srt 15.1 kB
30 - Markov Decision Process (MDP) Theory/003 Markov decision processes - equations_en.srt 15.1 kB
32 - Q Learning Theory/003 Q learning illustration_en.srt 15.1 kB
15 - Clustering/001 K-means clustering introduction_en.srt 14.9 kB
10 - Support Vector Machines (SVMs)/005 Support vector machine example I - simple_en.srt 14.8 kB
51 - Appendix #3 - Data Structures in Python/001 How to measure the running time of algorithms_en.srt 14.7 kB
06 - Logistic Regression/004 Logistic regression example II- credit scoring_en.srt 14.6 kB
14 - Principal Component Analysis (PCA)/002 Principal component analysis example_en.srt 14.6 kB
09 - Naive Bayes Classifier/001 What is the naive Bayes classifier_en.srt 14.5 kB
27 - Recurrent Neural Networks (RNNs) Theory/004 Long-short term memory (LSTM) model_en.srt 14.3 kB
35 - Deep Q Learning Implementation (Tic Tac Toe)/003 Tic Tac Toe with deep Q learning implementation III_en.srt 14.3 kB
42 - Histogram of Oriented Gradients (HOG) Implementation/001 Showing the HOG features programatically_en.srt 14.2 kB
43 - Convolutional Neural Networks (CNNs) Based Approaches/002 Region proposals and convolutional neural networks (CNNs)_en.srt 14.0 kB
46 - Single-Shot MultiBox Detector (SSD) Theory/003 Bounding boxes and anchor boxes_en.srt 13.8 kB
15 - Clustering/003 K-means clustering - text clustering_en.srt 13.5 kB
12 - Random Forest Classifier/006 Random forests example III - OCR parameter tuning_en.srt 13.4 kB
10 - Support Vector Machines (SVMs)/004 Kernel functions_en.srt 13.3 kB
51 - Appendix #3 - Data Structures in Python/016 Sorting_en.srt 13.2 kB
50 - Appendix #2 - Functions/003 Positional arguments and keyword arguments_en.srt 13.1 kB
26 - Machine Learning Project III - Identifying Objects with CNNs/004 Tuning the parameters - regularization_en.srt 13.1 kB
40 - Face Detection with Viola-Jones Method Implementation/002 Face detection implementation II - CascadeClassifier_en.srt 12.9 kB
15 - Clustering/008 Hierarchical clustering - market segmentation_en.srt 12.8 kB
21 - Deep Neural Networks Theory/002 Activation functions revisited_en.srt 12.7 kB
10 - Support Vector Machines (SVMs)/008 Support vector machine example IV - digit recognition_en.srt 12.7 kB
11 - Decision Trees/004 The Gini-index approach_en.srt 12.7 kB
05 - Linear Regression/001 What is linear regression_en.srt 12.6 kB
18 - Feed-Forward Neural Network Theory/005 Neural networks - the big picture_en.srt 12.6 kB
53 - Appendix #5 - NumPy/003 Dimension of arrays_en.srt 12.5 kB
14 - Principal Component Analysis (PCA)/003 Principal component analysis example II_en.srt 12.4 kB
51 - Appendix #3 - Data Structures in Python/014 Dictionaries in Python_en.srt 12.3 kB
44 - You Only Look Once (YOLO) Algorithm Theory/003 YOLO algorithm - intersection over union_en.srt 12.3 kB
27 - Recurrent Neural Networks (RNNs) Theory/003 Vanishing and exploding gradients problem_en.srt 12.2 kB
45 - You Only Look Once (YOLO) Algorithm Implementation/002 YOLO algorithm implementation II_en.srt 12.2 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/008 Drawing lines on video frames_en.srt 12.1 kB
27 - Recurrent Neural Networks (RNNs) Theory/002 Recurrent neural networks basics_en.srt 12.1 kB
15 - Clustering/005 DBSCAN example_en.srt 12.1 kB
03 - Artificial Intelligence Basics/002 Types of artificial intelligence learning_en.srt 12.0 kB
51 - Appendix #3 - Data Structures in Python/011 What are linked list data structures_en.srt 12.0 kB
50 - Appendix #2 - Functions/009 What is recursion_en.srt 11.8 kB
45 - You Only Look Once (YOLO) Algorithm Implementation/003 YOLO algorithm implementation III_en.srt 11.7 kB
18 - Feed-Forward Neural Network Theory/008 Optimization with gradient descent_en.srt 11.6 kB
05 - Linear Regression/002 Linear regression theory - optimization_en.srt 11.6 kB
08 - K-Nearest Neighbor Classifier/006 K-nearest neighbor implementation II_en.srt 11.4 kB
51 - Appendix #3 - Data Structures in Python/013 Hashing and O(1) running time complexity_en.srt 11.4 kB
14 - Principal Component Analysis (PCA)/001 Principal component analysis (PCA) introduction_en.srt 11.3 kB
09 - Naive Bayes Classifier/004 What is text clustering_en.srt 11.2 kB
51 - Appendix #3 - Data Structures in Python/015 Sets in Python_en.srt 11.2 kB
11 - Decision Trees/002 Decision trees introduction - entropy_en.srt 11.1 kB
31 - Exploration vs. Exploitation Problem/002 N-armed bandit problem introduction_en.srt 11.1 kB
22 - Deep Neural Networks Implementation/004 Multiclass classification implementation I_en.srt 11.0 kB
05 - Linear Regression/003 Linear regression theory - gradient descent_en.srt 11.0 kB
39 - Viola-Jones Face Detection Algorithm Theory/002 Haar-features_en.srt 10.9 kB
13 - Boosting/004 Boosting introduction - final formula_en.srt 10.8 kB
47 - SSD Algorithm Implementation/004 SSD implementation IV_en.srt 10.8 kB
15 - Clustering/007 Hierarchical clustering example_en.srt 10.8 kB
15 - Clustering/002 K-means clustering example_en.srt 10.8 kB
11 - Decision Trees/003 Decision trees introduction - information gain_en.srt 10.8 kB
53 - Appendix #5 - NumPy/004 Indexes and slicing_en.srt 10.8 kB
33 - Q Learning Implementation (Tic Tac Toe)/002 Tic tac toe with Q learning implementation II_en.srt 10.6 kB
11 - Decision Trees/001 Decision trees introduction - basics_en.srt 10.6 kB
03 - Artificial Intelligence Basics/003 Fundamentals of statistics_en.srt 10.6 kB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/003 Histogram of oriented gradients - magnitude and angle_en.srt 10.6 kB
16 - Machine Learning Project I - Face Recognition/001 The Olivetti dataset_en.srt 10.5 kB
16 - Machine Learning Project I - Face Recognition/004 Understanding eigenfaces_en.srt 10.4 kB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/002 Histogram of oriented gradients - gradient kernel_en.srt 10.4 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/013 Comparing objects - overriding functions_en.srt 10.4 kB
12 - Random Forest Classifier/002 Bagging introduction_en.srt 10.3 kB
53 - Appendix #5 - NumPy/006 Reshape_en.srt 10.3 kB
49 - Appendix #1 - Python Basics/009 How to use multiple conditions_en.srt 10.3 kB
44 - You Only Look Once (YOLO) Algorithm Theory/004 How to train the YOLO algorithm_en.srt 10.3 kB
33 - Q Learning Implementation (Tic Tac Toe)/004 Tic tac toe with Q learning implementation IV_en.srt 10.2 kB
49 - Appendix #1 - Python Basics/004 Strings_en.srt 10.1 kB
46 - Single-Shot MultiBox Detector (SSD) Theory/002 Basic concept behind SSD algorithm (architecture)_en.srt 10.0 kB
51 - Appendix #3 - Data Structures in Python/004 What are array data structures II_en.srt 9.8 kB
15 - Clustering/006 Hierarchical clustering introduction_en.srt 9.8 kB
51 - Appendix #3 - Data Structures in Python/006 Lists in Python - advanced operations_en.srt 9.8 kB
42 - Histogram of Oriented Gradients (HOG) Implementation/005 Face detection with HOG implementation IV_en.srt 9.8 kB
53 - Appendix #5 - NumPy/002 Creating and updating arrays_en.srt 9.8 kB
33 - Q Learning Implementation (Tic Tac Toe)/003 Tic tac toe with Q learning implementation III_en.srt 9.8 kB
10 - Support Vector Machines (SVMs)/007 Support vector machines example III - parameter tuning_en.srt 9.8 kB
21 - Deep Neural Networks Theory/004 Gradient descent and stochastic gradient descent_en.srt 9.7 kB
15 - Clustering/004 DBSCAN introduction_en.srt 9.7 kB
30 - Markov Decision Process (MDP) Theory/004 Markov decision processes - illustration_en.srt 9.6 kB
32 - Q Learning Theory/002 Q learning introduction - the algorithm_en.srt 9.5 kB
08 - K-Nearest Neighbor Classifier/003 Distance metrics - Euclidean-distance_en.srt 9.5 kB
10 - Support Vector Machines (SVMs)/003 Non-linearly separable problems_en.srt 9.4 kB
43 - Convolutional Neural Networks (CNNs) Based Approaches/003 Detecting bounding boxes with regression_en.srt 9.4 kB
51 - Appendix #3 - Data Structures in Python/003 What are array data structures I_en.srt 9.4 kB
18 - Feed-Forward Neural Network Theory/004 Why to use activation functions_en.srt 9.3 kB
08 - K-Nearest Neighbor Classifier/005 K-nearest neighbor implementation I_en.srt 9.3 kB
22 - Deep Neural Networks Implementation/001 Deep neural network implementation I_en.srt 9.2 kB
28 - Recurrent Neural Networks (RNNs) Implementation/003 Time series analysis example III_en.srt 9.2 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/004 What is Canny edge detection_en.srt 9.2 kB
13 - Boosting/003 Boosting introduction - equations_en.srt 9.2 kB
44 - You Only Look Once (YOLO) Algorithm Theory/002 YOLO algorithm - grid cells_en.srt 9.2 kB
35 - Deep Q Learning Implementation (Tic Tac Toe)/002 Tic Tac Toe with deep Q learning implementation II_en.srt 9.1 kB
45 - You Only Look Once (YOLO) Algorithm Implementation/001 YOLO algorithm implementation I_en.srt 9.1 kB
33 - Q Learning Implementation (Tic Tac Toe)/008 Tic tac toe with Q learning implementation VIII_en.srt 9.0 kB
44 - You Only Look Once (YOLO) Algorithm Theory/007 Why to use the so-called anchor boxes_en.srt 9.0 kB
13 - Boosting/005 Boosting implementation I - iris dataset_en.srt 9.0 kB
12 - Random Forest Classifier/001 Pruning introduction_en.srt 9.0 kB
22 - Deep Neural Networks Implementation/002 Deep neural network implementation II_en.srt 8.9 kB
10 - Support Vector Machines (SVMs)/006 Support vector machine example II - iris dataset_en.srt 8.7 kB
53 - Appendix #5 - NumPy/007 Stacking and merging arrays_en.srt 8.6 kB
18 - Feed-Forward Neural Network Theory/009 Gradient descent with backpropagation_en.srt 8.6 kB
30 - Markov Decision Process (MDP) Theory/002 Markov decision processes basics II_en.srt 8.6 kB
30 - Markov Decision Process (MDP) Theory/007 What is value iteration_en.srt 8.5 kB
49 - Appendix #1 - Python Basics/005 String slicing_en.srt 8.4 kB
37 - Handling Images and Pixels/002 Handling pixel intensities I_en.srt 8.4 kB
43 - Convolutional Neural Networks (CNNs) Based Approaches/001 The standard convolutional neural network (CNN) way_en.srt 8.4 kB
39 - Viola-Jones Face Detection Algorithm Theory/004 Boosting in computer vision_en.srt 8.3 kB
33 - Q Learning Implementation (Tic Tac Toe)/007 Tic tac toe with Q learning implementation VII_en.srt 8.3 kB
26 - Machine Learning Project III - Identifying Objects with CNNs/001 What is the CIFAR-10 dataset_en.srt 8.2 kB
39 - Viola-Jones Face Detection Algorithm Theory/003 Integral images_en.srt 8.2 kB
07 - Cross Validation/001 What is cross validation_en.srt 8.1 kB
08 - K-Nearest Neighbor Classifier/001 What is the k-nearest neighbor classifier_en.srt 8.1 kB
24 - Convolutional Neural Networks (CNNs) Theory/005 Convolutional neural networks - pooling_en.srt 8.1 kB
24 - Convolutional Neural Networks (CNNs) Theory/001 Convolutional neural networks basics_en.srt 8.1 kB
16 - Machine Learning Project I - Face Recognition/002 Understanding the dataset_en.srt 8.0 kB
16 - Machine Learning Project I - Face Recognition/003 Finding optimal number of principal components (eigenvectors)_en.srt 8.0 kB
49 - Appendix #1 - Python Basics/011 Loops - for loop_en.srt 7.9 kB
21 - Deep Neural Networks Theory/003 Loss functions_en.srt 7.9 kB
42 - Histogram of Oriented Gradients (HOG) Implementation/002 Face detection with HOG implementation I_en.srt 7.9 kB
03 - Artificial Intelligence Basics/001 Why to learn artificial intelligence and machine learning_en.srt 7.9 kB
47 - SSD Algorithm Implementation/001 SSD implementation I_en.srt 7.8 kB
11 - Decision Trees/006 Decision trees implementation I_en.srt 7.8 kB
24 - Convolutional Neural Networks (CNNs) Theory/004 Convolutional neural networks - kernel II_en.srt 7.8 kB
23 - Machine Learning Project II - Smile Detector/002 Reading the images and constructing the dataset I_en.srt 7.8 kB
44 - You Only Look Once (YOLO) Algorithm Theory/001 What is the YOLO approach_en.srt 7.8 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/003 Using the constructor_en.srt 7.7 kB
22 - Deep Neural Networks Implementation/005 Multiclass classification implementation II_en.srt 7.7 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/011 Modules_en.srt 7.7 kB
25 - Convolutional Neural Networks (CNNs) Implementation/003 Handwritten digit classification III_en.srt 7.7 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/002 Lane detection - handling videos_en.srt 7.7 kB
37 - Handling Images and Pixels/006 Image processing - edge detection kernel_en.srt 7.7 kB
28 - Recurrent Neural Networks (RNNs) Implementation/002 Time series analysis example II_en.srt 7.6 kB
10 - Support Vector Machines (SVMs)/001 What are Support Vector Machines (SVMs)_en.srt 7.6 kB
26 - Machine Learning Project III - Identifying Objects with CNNs/003 Fitting the model_en.srt 7.6 kB
35 - Deep Q Learning Implementation (Tic Tac Toe)/004 Tic Tac Toe with deep Q learning implementation IV_en.srt 7.5 kB
13 - Boosting/002 Boosting introduction - illustration_en.srt 7.4 kB
51 - Appendix #3 - Data Structures in Python/005 Lists in Python_en.srt 7.4 kB
49 - Appendix #1 - Python Basics/001 First steps in Python_en.srt 7.4 kB
37 - Handling Images and Pixels/003 Handling pixel intensities II_en.srt 7.4 kB
21 - Deep Neural Networks Theory/005 Hyperparameters_en.srt 7.3 kB
30 - Markov Decision Process (MDP) Theory/001 Markov decision processes basics I_en.srt 7.3 kB
10 - Support Vector Machines (SVMs)/009 Support vector machine example V - digit recognition_en.srt 7.3 kB
30 - Markov Decision Process (MDP) Theory/005 Bellman-equation_en.srt 7.3 kB
18 - Feed-Forward Neural Network Theory/001 Artificial neural networks - inspiration_en.srt 7.3 kB
12 - Random Forest Classifier/003 Random forest classifier introduction_en.srt 7.3 kB
06 - Logistic Regression/005 Logistic regression example III - credit scoring_en.srt 7.3 kB
37 - Handling Images and Pixels/005 Image processing - blur operation_en.srt 7.2 kB
51 - Appendix #3 - Data Structures in Python/007 Lists in Python - list comprehension_en.srt 7.2 kB
49 - Appendix #1 - Python Basics/015 Break and continue_en.srt 7.2 kB
40 - Face Detection with Viola-Jones Method Implementation/005 Face detection implementation V - detecting faces real-time_en.srt 7.1 kB
07 - Cross Validation/002 Cross validation example_en.srt 7.1 kB
32 - Q Learning Theory/001 What is Q learning_en.srt 7.1 kB
37 - Handling Images and Pixels/001 Images and pixel intensities_en.srt 7.1 kB
50 - Appendix #2 - Functions/002 Defining functions_en.srt 7.1 kB
21 - Deep Neural Networks Theory/001 Deep neural networks_en.srt 7.0 kB
51 - Appendix #3 - Data Structures in Python/012 Doubly linked list implementation in Python_en.srt 7.0 kB
22 - Deep Neural Networks Implementation/003 Deep neural network implementation III_en.srt 7.0 kB
44 - You Only Look Once (YOLO) Algorithm Theory/005 YOLO algorithm - loss function_en.srt 6.9 kB
06 - Logistic Regression/002 Logistic regression and maximum likelihood estimation_en.srt 6.8 kB
18 - Feed-Forward Neural Network Theory/002 Artificial neural networks - layers_en.srt 6.8 kB
11 - Decision Trees/008 Decision tree implementation III - identifying cancer_en.srt 6.8 kB
47 - SSD Algorithm Implementation/003 SSD implementation III_en.srt 6.8 kB
49 - Appendix #1 - Python Basics/007 Operators_en.srt 6.8 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/010 Polymorphism and abstraction example_en.srt 6.8 kB
24 - Convolutional Neural Networks (CNNs) Theory/006 Convolutional neural networks - flattening_en.srt 6.8 kB
18 - Feed-Forward Neural Network Theory/003 Artificial neural networks - the model_en.srt 6.8 kB
18 - Feed-Forward Neural Network Theory/007 How to measure the error of the network_en.srt 6.7 kB
42 - Histogram of Oriented Gradients (HOG) Implementation/004 Face detection with HOG implementation III_en.srt 6.6 kB
41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/004 Histogram of oriented gradients - normalization_en.srt 6.6 kB
09 - Naive Bayes Classifier/005 Text clustering - inverse document frequency (TF-IDF)_en.srt 6.6 kB
50 - Appendix #2 - Functions/006 Yield operator_en.srt 6.5 kB
35 - Deep Q Learning Implementation (Tic Tac Toe)/005 Tic Tac Toe with deep Q learning implementation V_en.srt 6.5 kB
49 - Appendix #1 - Python Basics/002 What are the basic data types_en.srt 6.5 kB
46 - Single-Shot MultiBox Detector (SSD) Theory/004 Feature maps and convolution layers_en.srt 6.5 kB
33 - Q Learning Implementation (Tic Tac Toe)/005 Tic tac toe with Q learning implementation V_en.srt 6.4 kB
09 - Naive Bayes Classifier/002 Naive Bayes classifier illustration_en.srt 6.4 kB
40 - Face Detection with Viola-Jones Method Implementation/004 Face detection implementation IV - tuning the parameters_en.srt 6.4 kB
34 - Deep Q Learning Theory/001 What is deep Q learning_en.srt 6.3 kB
51 - Appendix #3 - Data Structures in Python/010 Mutability and immutability_en.srt 6.2 kB
11 - Decision Trees/007 Decision trees implementation II - parameter tuning_en.srt 6.0 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/009 What is polymorphism_en.srt 6.0 kB
23 - Machine Learning Project II - Smile Detector/003 Reading the images and constructing the dataset II_en.srt 6.0 kB
38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/003 Lane detection - first transformations_en.srt 6.0 kB
13 - Boosting/001 Boosting introduction - basics_en.srt 6.0 kB
53 - Appendix #5 - NumPy/001 What is the key advantage of NumPy_en.srt 5.9 kB
53 - Appendix #5 - NumPy/005 Types_en.srt 5.9 kB
28 - Recurrent Neural Networks (RNNs) Implementation/006 Time series analysis example VI_en.srt 5.9 kB
50 - Appendix #2 - Functions/001 What are functions_en.srt 5.9 kB
28 - Recurrent Neural Networks (RNNs) Implementation/001 Time series analysis example I_en.srt 5.9 kB
27 - Recurrent Neural Networks (RNNs) Theory/001 Why do recurrent neural networks are important_en.srt 5.8 kB
28 - Recurrent Neural Networks (RNNs) Implementation/005 Time series analysis example V_en.srt 5.8 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/004 Class variables and instance variables_en.srt 5.8 kB
52 - Appendix #4 - Object Oriented Programming (OOP)/005 Private variables and name mangling_en.srt 5.8 kB
05 - Linear Regression/005 Linear regression implementation II_en.srt 5.8 kB
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
0. Websites you may like/[CourseClub.Me].url 122 Bytes
02 - Environment Setup/[CourseClub.Me].url 122 Bytes
08 - K-Nearest Neighbor Classifier/[CourseClub.Me].url 122 Bytes
14 - Principal Component Analysis (PCA)/[CourseClub.Me].url 122 Bytes
22 - Deep Neural Networks Implementation/[CourseClub.Me].url 122 Bytes
29 - ### REINFORCEMENT LEARNING ###/[CourseClub.Me].url 122 Bytes
36 - ### COMPUTER VISION ###/[CourseClub.Me].url 122 Bytes
43 - Convolutional Neural Networks (CNNs) Based Approaches/[CourseClub.Me].url 122 Bytes
46 - Single-Shot MultiBox Detector (SSD) Theory/[CourseClub.Me].url 122 Bytes
52 - Appendix #4 - Object Oriented Programming (OOP)/[CourseClub.Me].url 122 Bytes
[CourseClub.Me].url 122 Bytes
54 - COURSE MATERIALS (DOWNLOADS)/001 Course materials (source code and slides).html 66 Bytes
0. Websites you may like/[FreeAllCourse.Com].url 55 Bytes
02 - Environment Setup/[FreeAllCourse.Com].url 55 Bytes
08 - K-Nearest Neighbor Classifier/[FreeAllCourse.Com].url 55 Bytes
14 - Principal Component Analysis (PCA)/[FreeAllCourse.Com].url 55 Bytes
22 - Deep Neural Networks Implementation/[FreeAllCourse.Com].url 55 Bytes
29 - ### REINFORCEMENT LEARNING ###/[FreeAllCourse.Com].url 55 Bytes
36 - ### COMPUTER VISION ###/[FreeAllCourse.Com].url 55 Bytes
43 - Convolutional Neural Networks (CNNs) Based Approaches/[FreeAllCourse.Com].url 55 Bytes
46 - Single-Shot MultiBox Detector (SSD) Theory/[FreeAllCourse.Com].url 55 Bytes
52 - Appendix #4 - Object Oriented Programming (OOP)/[FreeAllCourse.Com].url 55 Bytes
[FreeAllCourse.Com].url 55 Bytes
0. Websites you may like/[GigaCourse.Com].url 49 Bytes
02 - Environment Setup/[GigaCourse.Com].url 49 Bytes
08 - K-Nearest Neighbor Classifier/[GigaCourse.Com].url 49 Bytes
14 - Principal Component Analysis (PCA)/[GigaCourse.Com].url 49 Bytes
22 - Deep Neural Networks Implementation/[GigaCourse.Com].url 49 Bytes
29 - ### REINFORCEMENT LEARNING ###/[GigaCourse.Com].url 49 Bytes
36 - ### COMPUTER VISION ###/[GigaCourse.Com].url 49 Bytes
43 - Convolutional Neural Networks (CNNs) Based Approaches/[GigaCourse.Com].url 49 Bytes
46 - Single-Shot MultiBox Detector (SSD) Theory/[GigaCourse.Com].url 49 Bytes
52 - Appendix #4 - Object Oriented Programming (OOP)/[GigaCourse.Com].url 49 Bytes
[GigaCourse.Com].url 49 Bytes

==查看完整文档列表==

上一个：[hotaru]JEAN-008 女子アスリートにサプリと偽り媚薬を飲ませ続けエッチな体質に変化させて出演交渉！！マミさん・陸上部編1.5 GB

24小时热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-EniaHD.mkv 5077次下载 WEB DL DDP5
The.Super.Cube.S01E09.1080p.iQ.WEB-DL.AAC2.0.H.264-VARYG.mkv 5040次下载 WEB DL Cube
Star.Wars.Andor.S02E05.I.Have.Friends.Everywhere.1080p.DS... 4944次下载 WEB DL Star
新桥恋人.电影港地址发布页 www.dygang.me 收藏不迷路 4365次下载 me www 发布页
Night at the Museum Battle of the Smithsonian 2009 2160p... 4161次下载 KiNGDOM WEB DL
www.UIndex.org - ... 4126次下载 www MeGusta HEVC
Palma.2.2024.WEB-DL.1080p.ExKinoRay.mkv 4000次下载 WEB DL 2.2024
Guns N' Roses - Greatest Hits Live Broadcast Collection... 3915次下载 Live Remastered Hits

本周热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
(同人CG集) [白色絵の具 (無味ムスミ)] 友達の母が巨乳で金髪の淫乱美女でした.zip 6346次下载 zip 巨乳 CG
You.S05E02.720p.HEVC.x265-MeGusta[EZTVx.to].mkv 6221次下载 S05E02 HEVC 720p
up-mod-minecraft-play-with-friends-v1-21-80-28-... 6023次下载 982108028 mod play
the.last.of.us.s02e02.1080p.web.h264-successful... 5335次下载 web last h264
异世降临.6v电影地址发布页 www.6v123.net 收藏不迷路 5220次下载 www 发布页 6v123
Opasnaya.blizost.S01.2025.WEB-DL.1080p 5127次下载 WEB DL S01
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Andor.S02E02.Sagrona.Teema.1080p.HEVC.x265-MeGu... 5100次下载 HEVC Andor mkv
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-E... 5077次下载 WEB DL DDP5

种子标签

种子评价

优质的种子 (0) 假种子 (0) 有密码 (0) 低质量 (0) 有病毒 (0) 无法下载 (0)
欢迎对种子质量进行评价。

人气女优

北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

分享BT种子/磁力链接

亲，你知道吗？下载的人越多速度越快，赶快把本页面分享给好友一起下载吧^_^

磁力管家