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[FreeCourseSite.com] Udemy - [2022] Machine Learning and Deep Learning Bootcamp in Python

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FreeCourseSite com Udemy - 2022 Machine Learning and Deep Learning Bootcamp in Python

文件列表
  1. 01 - Introduction/001 Introduction.mp425.85MB
  2. 02 - Environment Setup/003 Installing TensorFlow and Keras.mp49.7MB
  3. 03 - Artificial Intelligence Basics/001 Why to learn artificial intelligence and machine learning.mp414.01MB
  4. 03 - Artificial Intelligence Basics/002 Types of artificial intelligence learning.mp436.97MB
  5. 03 - Artificial Intelligence Basics/003 Fundamentals of statistics.mp433.22MB
  6. 05 - Linear Regression/001 What is linear regression.mp439.99MB
  7. 05 - Linear Regression/002 Linear regression theory - optimization.mp445.04MB
  8. 05 - Linear Regression/003 Linear regression theory - gradient descent.mp439.45MB
  9. 05 - Linear Regression/004 Linear regression implementation I.mp490.81MB
  10. 05 - Linear Regression/005 Linear regression implementation II.mp412.2MB
  11. 06 - Logistic Regression/001 What is logistic regression.mp440.2MB
  12. 06 - Logistic Regression/002 Logistic regression and maximum likelihood estimation.mp422.68MB
  13. 06 - Logistic Regression/003 Logistic regression example I - sigmoid function.mp433.18MB
  14. 06 - Logistic Regression/004 Logistic regression example II- credit scoring.mp458.55MB
  15. 06 - Logistic Regression/005 Logistic regression example III - credit scoring.mp433.52MB
  16. 07 - Cross Validation/001 What is cross validation.mp424.41MB
  17. 07 - Cross Validation/002 Cross validation example.mp425.13MB
  18. 08 - K-Nearest Neighbor Classifier/001 What is the k-nearest neighbor classifier.mp413.69MB
  19. 08 - K-Nearest Neighbor Classifier/002 Concept of lazy learning.mp415.24MB
  20. 08 - K-Nearest Neighbor Classifier/003 Distance metrics - Euclidean-distance.mp421.73MB
  21. 08 - K-Nearest Neighbor Classifier/004 Bias and variance trade-off.mp414.7MB
  22. 08 - K-Nearest Neighbor Classifier/005 K-nearest neighbor implementation I.mp416.5MB
  23. 08 - K-Nearest Neighbor Classifier/006 K-nearest neighbor implementation II.mp448.51MB
  24. 08 - K-Nearest Neighbor Classifier/007 K-nearest neighbor implementation III.mp410.53MB
  25. 09 - Naive Bayes Classifier/001 What is the naive Bayes classifier.mp442.38MB
  26. 09 - Naive Bayes Classifier/002 Naive Bayes classifier illustration.mp49.23MB
  27. 09 - Naive Bayes Classifier/003 Naive Bayes classifier implementation.mp411.08MB
  28. 09 - Naive Bayes Classifier/004 What is text clustering.mp438.5MB
  29. 09 - Naive Bayes Classifier/005 Text clustering - inverse document frequency (TF-IDF).mp414.61MB
  30. 09 - Naive Bayes Classifier/006 Naive Bayes example - clustering news.mp478.91MB
  31. 10 - Support Vector Machines (SVMs)/001 What are Support Vector Machines (SVMs).mp420.14MB
  32. 10 - Support Vector Machines (SVMs)/002 Linearly separable problems.mp430.33MB
  33. 10 - Support Vector Machines (SVMs)/003 Non-linearly separable problems.mp422.96MB
  34. 10 - Support Vector Machines (SVMs)/004 Kernel functions.mp434.1MB
  35. 10 - Support Vector Machines (SVMs)/005 Support vector machine example I - simple.mp436.79MB
  36. 10 - Support Vector Machines (SVMs)/006 Support vector machine example II - iris dataset.mp415.1MB
  37. 10 - Support Vector Machines (SVMs)/007 Support vector machines example III - parameter tuning.mp417.83MB
  38. 10 - Support Vector Machines (SVMs)/008 Support vector machine example IV - digit recognition.mp422.1MB
  39. 10 - Support Vector Machines (SVMs)/009 Support vector machine example V - digit recognition.mp414.49MB
  40. 10 - Support Vector Machines (SVMs)/010 Advantages and disadvantages.mp46MB
  41. 11 - Decision Trees/001 Decision trees introduction - basics.mp427.41MB
  42. 11 - Decision Trees/002 Decision trees introduction - entropy.mp440.84MB
  43. 11 - Decision Trees/003 Decision trees introduction - information gain.mp438.24MB
  44. 11 - Decision Trees/004 The Gini-index approach.mp420.11MB
  45. 11 - Decision Trees/005 Decision trees introduction - pros and cons.mp45.74MB
  46. 11 - Decision Trees/006 Decision trees implementation I.mp413.18MB
  47. 11 - Decision Trees/007 Decision trees implementation II - parameter tuning.mp414.09MB
  48. 11 - Decision Trees/008 Decision tree implementation III - identifying cancer.mp432.45MB
  49. 12 - Random Forest Classifier/001 Pruning introduction.mp415.47MB
  50. 12 - Random Forest Classifier/002 Bagging introduction.mp416.08MB
  51. 12 - Random Forest Classifier/003 Random forest classifier introduction.mp412.29MB
  52. 12 - Random Forest Classifier/004 Random forests example I - iris dataset.mp413.5MB
  53. 12 - Random Forest Classifier/005 Random forests example II - credit scoring.mp49.94MB
  54. 12 - Random Forest Classifier/006 Random forests example III - OCR parameter tuning.mp431.9MB
  55. 13 - Boosting/001 Boosting introduction - basics.mp415.75MB
  56. 13 - Boosting/002 Boosting introduction - illustration.mp411.17MB
  57. 13 - Boosting/003 Boosting introduction - equations.mp413.42MB
  58. 13 - Boosting/004 Boosting introduction - final formula.mp436.78MB
  59. 13 - Boosting/005 Boosting implementation I - iris dataset.mp431.13MB
  60. 13 - Boosting/006 Boosting implementation II -wine classification.mp438.65MB
  61. 13 - Boosting/007 Boosting vs. bagging.mp46.87MB
  62. 14 - Principal Component Analysis (PCA)/001 Principal component analysis (PCA) introduction.mp438.24MB
  63. 14 - Principal Component Analysis (PCA)/002 Principal component analysis example.mp426.79MB
  64. 14 - Principal Component Analysis (PCA)/003 Principal component analysis example II.mp422.27MB
  65. 15 - Clustering/001 K-means clustering introduction.mp416.62MB
  66. 15 - Clustering/002 K-means clustering example.mp419.52MB
  67. 15 - Clustering/003 K-means clustering - text clustering.mp437.68MB
  68. 15 - Clustering/004 DBSCAN introduction.mp411.37MB
  69. 15 - Clustering/005 DBSCAN example.mp421.15MB
  70. 15 - Clustering/006 Hierarchical clustering introduction.mp416.58MB
  71. 15 - Clustering/007 Hierarchical clustering example.mp420.36MB
  72. 15 - Clustering/008 Hierarchical clustering - market segmentation.mp429MB
  73. 16 - Machine Learning Project I - Face Recognition/001 The Olivetti dataset.mp422.83MB
  74. 16 - Machine Learning Project I - Face Recognition/002 Understanding the dataset.mp445.89MB
  75. 16 - Machine Learning Project I - Face Recognition/003 Finding optimal number of principal components (eigenvectors).mp423.63MB
  76. 16 - Machine Learning Project I - Face Recognition/004 Understanding eigenfaces.mp462.97MB
  77. 16 - Machine Learning Project I - Face Recognition/005 Constructing the machine learning models.mp413.36MB
  78. 16 - Machine Learning Project I - Face Recognition/006 Using cross-validation.mp421.97MB
  79. 18 - Feed-Forward Neural Network Theory/001 Artificial neural networks - inspiration.mp424.16MB
  80. 18 - Feed-Forward Neural Network Theory/002 Artificial neural networks - layers.mp411.03MB
  81. 18 - Feed-Forward Neural Network Theory/003 Artificial neural networks - the model.mp421.55MB
  82. 18 - Feed-Forward Neural Network Theory/004 Why to use activation functions.mp428.39MB
  83. 18 - Feed-Forward Neural Network Theory/005 Neural networks - the big picture.mp434.99MB
  84. 18 - Feed-Forward Neural Network Theory/006 Using bias nodes in the neural network.mp44.32MB
  85. 18 - Feed-Forward Neural Network Theory/007 How to measure the error of the network.mp412.03MB
  86. 18 - Feed-Forward Neural Network Theory/008 Optimization with gradient descent.mp439.92MB
  87. 18 - Feed-Forward Neural Network Theory/009 Gradient descent with backpropagation.mp424.2MB
  88. 18 - Feed-Forward Neural Network Theory/010 Backpropagation explained.mp446.26MB
  89. 19 - Single Layer Networks Implementation/001 Simple neural network implementation - XOR problem.mp436.7MB
  90. 19 - Single Layer Networks Implementation/002 Simple neural network implementation - Iris dataset.mp484.96MB
  91. 19 - Single Layer Networks Implementation/003 Credit scoring with simple neural networks.mp423.17MB
  92. 20 - Deep Learning/001 Types of neural networks.mp48.01MB
  93. 21 - Deep Neural Networks Theory/001 Deep neural networks.mp49.28MB
  94. 21 - Deep Neural Networks Theory/002 Activation functions revisited.mp426.21MB
  95. 21 - Deep Neural Networks Theory/003 Loss functions.mp415.42MB
  96. 21 - Deep Neural Networks Theory/004 Gradient descent and stochastic gradient descent.mp440.09MB
  97. 21 - Deep Neural Networks Theory/005 Hyperparameters.mp426.92MB
  98. 22 - Deep Neural Networks Implementation/001 Deep neural network implementation I.mp417.33MB
  99. 22 - Deep Neural Networks Implementation/002 Deep neural network implementation II.mp418.9MB
  100. 22 - Deep Neural Networks Implementation/003 Deep neural network implementation III.mp426.09MB
  101. 22 - Deep Neural Networks Implementation/004 Multiclass classification implementation I.mp428.48MB
  102. 22 - Deep Neural Networks Implementation/005 Multiclass classification implementation II.mp426.72MB
  103. 23 - Machine Learning Project II - Smile Detector/001 Understanding the classification problem.mp44.78MB
  104. 23 - Machine Learning Project II - Smile Detector/002 Reading the images and constructing the dataset I.mp425.08MB
  105. 23 - Machine Learning Project II - Smile Detector/003 Reading the images and constructing the dataset II.mp438.06MB
  106. 23 - Machine Learning Project II - Smile Detector/004 Building the deep neural network model.mp49.55MB
  107. 23 - Machine Learning Project II - Smile Detector/005 Evaluating and testing the model.mp412.52MB
  108. 24 - Convolutional Neural Networks (CNNs) Theory/001 Convolutional neural networks basics.mp425MB
  109. 24 - Convolutional Neural Networks (CNNs) Theory/002 Feature selection.mp412.15MB
  110. 24 - Convolutional Neural Networks (CNNs) Theory/003 Convolutional neural networks - kernel.mp48.9MB
  111. 24 - Convolutional Neural Networks (CNNs) Theory/004 Convolutional neural networks - kernel II.mp48.88MB
  112. 24 - Convolutional Neural Networks (CNNs) Theory/005 Convolutional neural networks - pooling.mp425.58MB
  113. 24 - Convolutional Neural Networks (CNNs) Theory/006 Convolutional neural networks - flattening.mp426.77MB
  114. 24 - Convolutional Neural Networks (CNNs) Theory/007 Convolutional neural networks - illustration.mp431.87MB
  115. 25 - Convolutional Neural Networks (CNNs) Implementation/001 Handwritten digit classification I.mp454.32MB
  116. 25 - Convolutional Neural Networks (CNNs) Implementation/002 Handwritten digit classification II.mp455.59MB
  117. 25 - Convolutional Neural Networks (CNNs) Implementation/003 Handwritten digit classification III.mp435.17MB
  118. 26 - Machine Learning Project III - Identifying Objects with CNNs/001 What is the CIFAR-10 dataset.mp436.07MB
  119. 26 - Machine Learning Project III - Identifying Objects with CNNs/002 Preprocessing the data.mp47.66MB
  120. 26 - Machine Learning Project III - Identifying Objects with CNNs/003 Fitting the model.mp443.65MB
  121. 26 - Machine Learning Project III - Identifying Objects with CNNs/004 Tuning the parameters - regularization.mp460.49MB
  122. 27 - Recurrent Neural Networks (RNNs) Theory/001 Why do recurrent neural networks are important.mp421.3MB
  123. 27 - Recurrent Neural Networks (RNNs) Theory/002 Recurrent neural networks basics.mp428.63MB
  124. 27 - Recurrent Neural Networks (RNNs) Theory/003 Vanishing and exploding gradients problem.mp427.18MB
  125. 27 - Recurrent Neural Networks (RNNs) Theory/004 Long-short term memory (LSTM) model.mp433.39MB
  126. 27 - Recurrent Neural Networks (RNNs) Theory/005 Gated recurrent units (GRUs).mp46.41MB
  127. 28 - Recurrent Neural Networks (RNNs) Implementation/001 Time series analysis example I.mp414.07MB
  128. 28 - Recurrent Neural Networks (RNNs) Implementation/002 Time series analysis example II.mp413.04MB
  129. 28 - Recurrent Neural Networks (RNNs) Implementation/003 Time series analysis example III.mp420.06MB
  130. 28 - Recurrent Neural Networks (RNNs) Implementation/004 Time series analysis example IV.mp48.46MB
  131. 28 - Recurrent Neural Networks (RNNs) Implementation/005 Time series analysis example V.mp414.59MB
  132. 28 - Recurrent Neural Networks (RNNs) Implementation/006 Time series analysis example VI.mp412.37MB
  133. 29 - ### REINFORCEMENT LEARNING ###/002 Applications of reinforcement learning.mp46.6MB
  134. 30 - Markov Decision Process (MDP) Theory/001 Markov decision processes basics I.mp423.2MB
  135. 30 - Markov Decision Process (MDP) Theory/002 Markov decision processes basics II.mp414.15MB
  136. 30 - Markov Decision Process (MDP) Theory/003 Markov decision processes - equations.mp449.65MB
  137. 30 - Markov Decision Process (MDP) Theory/004 Markov decision processes - illustration.mp428.22MB
  138. 30 - Markov Decision Process (MDP) Theory/005 Bellman-equation.mp415.43MB
  139. 30 - Markov Decision Process (MDP) Theory/006 How to solve MDP problems.mp45.7MB
  140. 30 - Markov Decision Process (MDP) Theory/007 What is value iteration.mp424.23MB
  141. 30 - Markov Decision Process (MDP) Theory/008 What is policy iteration.mp46.98MB
  142. 31 - Exploration vs. Exploitation Problem/001 Exploration vs exploitation problem.mp47.65MB
  143. 31 - Exploration vs. Exploitation Problem/002 N-armed bandit problem introduction.mp419.56MB
  144. 31 - Exploration vs. Exploitation Problem/003 N-armed bandit problem implementation.mp453.31MB
  145. 31 - Exploration vs. Exploitation Problem/004 Applications AB testing in marketing.mp412.13MB
  146. 32 - Q Learning Theory/001 What is Q learning.mp411.79MB
  147. 32 - Q Learning Theory/002 Q learning introduction - the algorithm.mp415.46MB
  148. 32 - Q Learning Theory/003 Q learning illustration.mp421.44MB
  149. 33 - Q Learning Implementation (Tic Tac Toe)/001 Tic tac toe with Q learning implementation I.mp416.78MB
  150. 33 - Q Learning Implementation (Tic Tac Toe)/002 Tic tac toe with Q learning implementation II.mp419.81MB
  151. 33 - Q Learning Implementation (Tic Tac Toe)/003 Tic tac toe with Q learning implementation III.mp426.25MB
  152. 33 - Q Learning Implementation (Tic Tac Toe)/004 Tic tac toe with Q learning implementation IV.mp446.16MB
  153. 33 - Q Learning Implementation (Tic Tac Toe)/005 Tic tac toe with Q learning implementation V.mp421.72MB
  154. 33 - Q Learning Implementation (Tic Tac Toe)/006 Tic tac toe with Q learning implementation VI.mp499.45MB
  155. 33 - Q Learning Implementation (Tic Tac Toe)/007 Tic tac toe with Q learning implementation VII.mp449.8MB
  156. 33 - Q Learning Implementation (Tic Tac Toe)/008 Tic tac toe with Q learning implementation VIII.mp449.82MB
  157. 34 - Deep Q Learning Theory/001 What is deep Q learning.mp49.29MB
  158. 34 - Deep Q Learning Theory/003 Remember and replay.mp47MB
  159. 35 - Deep Q Learning Implementation (Tic Tac Toe)/001 Tic Tac Toe with deep Q learning implementation I.mp421.12MB
  160. 35 - Deep Q Learning Implementation (Tic Tac Toe)/002 Tic Tac Toe with deep Q learning implementation II.mp440.62MB
  161. 35 - Deep Q Learning Implementation (Tic Tac Toe)/003 Tic Tac Toe with deep Q learning implementation III.mp474.29MB
  162. 35 - Deep Q Learning Implementation (Tic Tac Toe)/004 Tic Tac Toe with deep Q learning implementation IV.mp415.43MB
  163. 35 - Deep Q Learning Implementation (Tic Tac Toe)/005 Tic Tac Toe with deep Q learning implementation V.mp431.32MB
  164. 36 - ### COMPUTER VISION ###/001 Evolution of computer vision related algorithms.mp48.68MB
  165. 37 - Handling Images and Pixels/001 Images and pixel intensities.mp410.74MB
  166. 37 - Handling Images and Pixels/002 Handling pixel intensities I.mp434.64MB
  167. 37 - Handling Images and Pixels/003 Handling pixel intensities II.mp413.21MB
  168. 37 - Handling Images and Pixels/004 Why convolution is so important in image processing.mp438.48MB
  169. 37 - Handling Images and Pixels/005 Image processing - blur operation.mp412.67MB
  170. 37 - Handling Images and Pixels/006 Image processing - edge detection kernel.mp414.58MB
  171. 37 - Handling Images and Pixels/007 Image processing - sharpen operation.mp49.03MB
  172. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/001 Lane detection - the problem.mp44.41MB
  173. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/002 Lane detection - handling videos.mp413.69MB
  174. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/003 Lane detection - first transformations.mp411.94MB
  175. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/004 What is Canny edge detection.mp416.43MB
  176. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/005 Getting the useful region of the image - masking.mp464.74MB
  177. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/006 Detecting lines - what is Hough transformation.mp445MB
  178. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/008 Drawing lines on video frames.mp432.69MB
  179. 38 - Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)/009 Testing lane detection algorithm.mp416.08MB
  180. 39 - Viola-Jones Face Detection Algorithm Theory/001 Viola-Jones algorithm.mp440.92MB
  181. 39 - Viola-Jones Face Detection Algorithm Theory/002 Haar-features.mp422.13MB
  182. 39 - Viola-Jones Face Detection Algorithm Theory/003 Integral images.mp424.53MB
  183. 39 - Viola-Jones Face Detection Algorithm Theory/004 Boosting in computer vision.mp423.41MB
  184. 39 - Viola-Jones Face Detection Algorithm Theory/005 Cascading.mp49.88MB
  185. 40 - Face Detection with Viola-Jones Method Implementation/001 Face detection implementation I - installing OpenCV.mp47.64MB
  186. 40 - Face Detection with Viola-Jones Method Implementation/002 Face detection implementation II - CascadeClassifier.mp470.68MB
  187. 40 - Face Detection with Viola-Jones Method Implementation/003 Face detection implementation III - CascadeClassifier parameters.mp418.36MB
  188. 40 - Face Detection with Viola-Jones Method Implementation/004 Face detection implementation IV - tuning the parameters.mp418MB
  189. 40 - Face Detection with Viola-Jones Method Implementation/005 Face detection implementation V - detecting faces real-time.mp418.85MB
  190. 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/001 Histogram of oriented gradients basics.mp419.24MB
  191. 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/002 Histogram of oriented gradients - gradient kernel.mp430.56MB
  192. 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/003 Histogram of oriented gradients - magnitude and angle.mp433.92MB
  193. 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/004 Histogram of oriented gradients - normalization.mp422.59MB
  194. 41 - Histogram of Oriented Gradients (HOG) Algorithm Theory/005 Histogram of oriented gradients - big picture.mp47.84MB
  195. 42 - Histogram of Oriented Gradients (HOG) Implementation/001 Showing the HOG features programatically.mp453.4MB
  196. 42 - Histogram of Oriented Gradients (HOG) Implementation/002 Face detection with HOG implementation I.mp415.45MB
  197. 42 - Histogram of Oriented Gradients (HOG) Implementation/003 Face detection with HOG implementation II.mp452.33MB
  198. 42 - Histogram of Oriented Gradients (HOG) Implementation/004 Face detection with HOG implementation III.mp436.08MB
  199. 42 - Histogram of Oriented Gradients (HOG) Implementation/005 Face detection with HOG implementation IV.mp432.31MB
  200. 43 - Convolutional Neural Networks (CNNs) Based Approaches/001 The standard convolutional neural network (CNN) way.mp418.36MB
  201. 43 - Convolutional Neural Networks (CNNs) Based Approaches/002 Region proposals and convolutional neural networks (CNNs).mp460.62MB
  202. 43 - Convolutional Neural Networks (CNNs) Based Approaches/003 Detecting bounding boxes with regression.mp422.1MB
  203. 43 - Convolutional Neural Networks (CNNs) Based Approaches/004 What is the Fast R-CNN model.mp46.42MB
  204. 43 - Convolutional Neural Networks (CNNs) Based Approaches/005 What is the Faster R-CNN model.mp43.97MB
  205. 44 - You Only Look Once (YOLO) Algorithm Theory/001 What is the YOLO approach.mp412.37MB
  206. 44 - You Only Look Once (YOLO) Algorithm Theory/002 YOLO algorithm - grid cells.mp438.38MB
  207. 44 - You Only Look Once (YOLO) Algorithm Theory/003 YOLO algorithm - intersection over union.mp451.4MB
  208. 44 - You Only Look Once (YOLO) Algorithm Theory/004 How to train the YOLO algorithm.mp425.08MB
  209. 44 - You Only Look Once (YOLO) Algorithm Theory/005 YOLO algorithm - loss function.mp416.29MB
  210. 44 - You Only Look Once (YOLO) Algorithm Theory/006 YOLO algorithm - non-max suppression.mp49.12MB
  211. 44 - You Only Look Once (YOLO) Algorithm Theory/007 Why to use the so-called anchor boxes.mp419.94MB
  212. 45 - You Only Look Once (YOLO) Algorithm Implementation/001 YOLO algorithm implementation I.mp422.81MB
  213. 45 - You Only Look Once (YOLO) Algorithm Implementation/002 YOLO algorithm implementation II.mp423.78MB
  214. 45 - You Only Look Once (YOLO) Algorithm Implementation/003 YOLO algorithm implementation III.mp424.71MB
  215. 45 - You Only Look Once (YOLO) Algorithm Implementation/004 YOLO algorithm implementation IV.mp469.67MB
  216. 45 - You Only Look Once (YOLO) Algorithm Implementation/005 YOLO algorithm implementation V.mp495.8MB
  217. 45 - You Only Look Once (YOLO) Algorithm Implementation/006 YOLO algorithm implementation VI.mp47.3MB
  218. 45 - You Only Look Once (YOLO) Algorithm Implementation/007 YOLO algorithm implementation VII.mp427.94MB
  219. 46 - Single-Shot MultiBox Detector (SSD) Theory/001 What is the SSD algorithm.mp418.05MB
  220. 46 - Single-Shot MultiBox Detector (SSD) Theory/002 Basic concept behind SSD algorithm (architecture).mp443.48MB
  221. 46 - Single-Shot MultiBox Detector (SSD) Theory/003 Bounding boxes and anchor boxes.mp470.53MB
  222. 46 - Single-Shot MultiBox Detector (SSD) Theory/004 Feature maps and convolution layers.mp413.86MB
  223. 46 - Single-Shot MultiBox Detector (SSD) Theory/005 Hard negative mining during training.mp46.12MB
  224. 46 - Single-Shot MultiBox Detector (SSD) Theory/006 Regularization (data augmentation) and non-max suppression during training.mp46.87MB
  225. 47 - SSD Algorithm Implementation/001 SSD implementation I.mp430.89MB
  226. 47 - SSD Algorithm Implementation/002 SSD implementation II.mp46.37MB
  227. 47 - SSD Algorithm Implementation/003 SSD implementation III.mp418.84MB
  228. 47 - SSD Algorithm Implementation/004 SSD implementation IV.mp450.57MB
  229. 47 - SSD Algorithm Implementation/005 SSD implementation V.mp414.99MB
  230. 48 - ### PYTHON PROGRAMMING CRASH COURSE ###/001 Python crash course introduction.mp43.97MB
  231. 49 - Appendix #1 - Python Basics/001 First steps in Python.mp47.38MB
  232. 49 - Appendix #1 - Python Basics/002 What are the basic data types.mp47.7MB
  233. 49 - Appendix #1 - Python Basics/003 Booleans.mp43.52MB
  234. 49 - Appendix #1 - Python Basics/004 Strings.mp414.57MB
  235. 49 - Appendix #1 - Python Basics/005 String slicing.mp412.66MB
  236. 49 - Appendix #1 - Python Basics/006 Type casting.mp48.18MB
  237. 49 - Appendix #1 - Python Basics/007 Operators.mp410.69MB
  238. 49 - Appendix #1 - Python Basics/008 Conditional statements.mp48.57MB
  239. 49 - Appendix #1 - Python Basics/009 How to use multiple conditions.mp415.96MB
  240. 49 - Appendix #1 - Python Basics/010 Logical operators.mp48.05MB
  241. 49 - Appendix #1 - Python Basics/011 Loops - for loop.mp49.56MB
  242. 49 - Appendix #1 - Python Basics/012 Loops - while loop.mp47.55MB
  243. 49 - Appendix #1 - Python Basics/013 What are nested loops.mp45.95MB
  244. 49 - Appendix #1 - Python Basics/014 Enumerate.mp47.69MB
  245. 49 - Appendix #1 - Python Basics/015 Break and continue.mp49.92MB
  246. 49 - Appendix #1 - Python Basics/016 Calculating Fibonacci-numbers.mp44.02MB
  247. 50 - Appendix #2 - Functions/001 What are functions.mp48.09MB
  248. 50 - Appendix #2 - Functions/002 Defining functions.mp49.6MB
  249. 50 - Appendix #2 - Functions/003 Positional arguments and keyword arguments.mp422.2MB
  250. 50 - Appendix #2 - Functions/004 Returning values.mp44.11MB
  251. 50 - Appendix #2 - Functions/005 Returning multiple values.mp46MB
  252. 50 - Appendix #2 - Functions/006 Yield operator.mp49.15MB
  253. 50 - Appendix #2 - Functions/007 Local and global variables.mp44.25MB
  254. 50 - Appendix #2 - Functions/008 What are the most relevant built-in functions.mp47.63MB
  255. 50 - Appendix #2 - Functions/009 What is recursion.mp417.38MB
  256. 50 - Appendix #2 - Functions/010 Local vs global variables.mp47.83MB
  257. 50 - Appendix #2 - Functions/011 The __main__ function.mp47.33MB
  258. 51 - Appendix #3 - Data Structures in Python/001 How to measure the running time of algorithms.mp418.29MB
  259. 51 - Appendix #3 - Data Structures in Python/002 Data structures introduction.mp46.72MB
  260. 51 - Appendix #3 - Data Structures in Python/003 What are array data structures I.mp412.26MB
  261. 51 - Appendix #3 - Data Structures in Python/004 What are array data structures II.mp412.3MB
  262. 51 - Appendix #3 - Data Structures in Python/005 Lists in Python.mp410.51MB
  263. 51 - Appendix #3 - Data Structures in Python/006 Lists in Python - advanced operations.mp418.63MB
  264. 51 - Appendix #3 - Data Structures in Python/007 Lists in Python - list comprehension.mp411.39MB
  265. 51 - Appendix #3 - Data Structures in Python/009 What are tuples.mp47.52MB
  266. 51 - Appendix #3 - Data Structures in Python/010 Mutability and immutability.mp48.7MB
  267. 51 - Appendix #3 - Data Structures in Python/011 What are linked list data structures.mp420.75MB
  268. 51 - Appendix #3 - Data Structures in Python/012 Doubly linked list implementation in Python.mp411.44MB
  269. 51 - Appendix #3 - Data Structures in Python/013 Hashing and O(1) running time complexity.mp423.11MB
  270. 51 - Appendix #3 - Data Structures in Python/014 Dictionaries in Python.mp419.44MB
  271. 51 - Appendix #3 - Data Structures in Python/015 Sets in Python.mp426.05MB
  272. 51 - Appendix #3 - Data Structures in Python/016 Sorting.mp423.77MB
  273. 52 - Appendix #4 - Object Oriented Programming (OOP)/001 What is object oriented programming (OOP).mp45.23MB
  274. 52 - Appendix #4 - Object Oriented Programming (OOP)/002 Class and objects basics.mp45.39MB
  275. 52 - Appendix #4 - Object Oriented Programming (OOP)/003 Using the constructor.mp417.82MB
  276. 52 - Appendix #4 - Object Oriented Programming (OOP)/004 Class variables and instance variables.mp414.67MB
  277. 52 - Appendix #4 - Object Oriented Programming (OOP)/005 Private variables and name mangling.mp415.3MB
  278. 52 - Appendix #4 - Object Oriented Programming (OOP)/006 What is inheritance in OOP.mp48.13MB
  279. 52 - Appendix #4 - Object Oriented Programming (OOP)/007 The super keyword.mp49.13MB
  280. 52 - Appendix #4 - Object Oriented Programming (OOP)/008 Function (method) override.mp46.46MB
  281. 52 - Appendix #4 - Object Oriented Programming (OOP)/009 What is polymorphism.mp416.18MB
  282. 52 - Appendix #4 - Object Oriented Programming (OOP)/010 Polymorphism and abstraction example.mp413.72MB
  283. 52 - Appendix #4 - Object Oriented Programming (OOP)/011 Modules.mp411.04MB
  284. 52 - Appendix #4 - Object Oriented Programming (OOP)/012 The __str__ function.mp47.67MB
  285. 52 - Appendix #4 - Object Oriented Programming (OOP)/013 Comparing objects - overriding functions.mp417.11MB
  286. 53 - Appendix #5 - NumPy/001 What is the key advantage of NumPy.mp48.16MB
  287. 53 - Appendix #5 - NumPy/002 Creating and updating arrays.mp416.76MB
  288. 53 - Appendix #5 - NumPy/003 Dimension of arrays.mp418.44MB
  289. 53 - Appendix #5 - NumPy/004 Indexes and slicing.mp416.72MB
  290. 53 - Appendix #5 - NumPy/005 Types.mp49.92MB
  291. 53 - Appendix #5 - NumPy/006 Reshape.mp416.97MB
  292. 53 - Appendix #5 - NumPy/007 Stacking and merging arrays.mp421.95MB
  293. 53 - Appendix #5 - NumPy/008 Filter.mp47.65MB
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