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种子名称： Packt Publishing - Deep Dive into Python Machine Learning
文件类型： 视频
文件数目： 187个文件
文件大小： 2.58 GB
收录时间： 2018-2-14 22:04
已经下载： 3次
资源热度： 198
最近下载： 2024-10-22 09:18

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Packt Publishing - Deep Dive into Python Machine Learning.torrent

01 - The Course Overview.mp414.93MB

02 - Python Basic Syntax and Block Structure.mp422.54MB

03 - Built-in Data Structures and Comprehensions.mp417.79MB

04 - First-Class Functions and Classes.mp412.33MB

05 - Extensive Standard Library.mp431.14MB

06 - New in Python 3.5.mp421.01MB

07 - Downloading and Installing Python.mp415.34MB

08 - Using the Command-Line and the Interactive Shell.mp47.1MB

09 - Installing Packages with pip.mp411.04MB

10 - Finding Packages in the Python Package Index.mp421.78MB

100 - Compressing an Image Using Vector Quantization.mp416.33MB

101 - Building a Mean Shift Clustering.mp411.26MB

102 - Grouping Data Using Agglomerative Clustering.mp413.54MB

103 - Evaluating the Performance of Clustering Algorithms.mp412.74MB

104 - Automatically Estimating the Number of Clusters Using DBSCAN.mp414.94MB

105 - Finding Patterns in Stock Market Data.mp411.34MB

106 - Building a Customer Segmentation Model.mp49.78MB

107 - Building Function Composition for Data Processing.mp413.67MB

108 - Building Machine Learning Pipelines.mp415.17MB

109 - Finding the Nearest Neighbors.mp48.05MB

11 - Creating an Empty Package.mp411.59MB

110 - Constructing a k-nearest Neighbors Classifier.mp419.77MB

111 - Constructing a k-nearest Neighbors Regressor.mp49.75MB

112 - Computing the Euclidean Distance Score.mp49.21MB

113 - Computing the Pearson Correlation Score.mp48.32MB

114 - Finding Similar Users in a Dataset.mp46.89MB

115 - Generating Movie Recommendations.mp410.2MB

116 - Preprocessing Data Using Tokenization.mp412.67MB

117 - Stemming Text Data.mp48.77MB

118 - Converting Text to Its Base Form Using Lemmatization.mp48.25MB

119 - Dividing Text Using Chunking.mp47.42MB

12 - Adding Modules to the Package.mp47.99MB

120 - Building a Bag-of-Words Model.mp411.71MB

121 - Building a Text Classifier.mp417.97MB

122 - Identifying the Gender.mp410MB

123 - Analyzing the Sentiment of a Sentence.mp414.39MB

124 - Identifying Patterns in Text Using Topic Modelling.mp419.76MB

125 - Reading and Plotting Audio Data.mp49.35MB

126 - Transforming Audio Signals into the Frequency Domain.mp49.32MB

127 - Generating Audio Signals with Custom Parameters.mp47.64MB

128 - Synthesizing Music.mp49.81MB

129 - Extracting Frequency Domain Features.mp48.13MB

13 - Importing One of the Package's Modules from Another.mp49.29MB

130 - Building Hidden Markov Models.mp49.6MB

131 - Building a Speech Recognizer.mp412.94MB

132 - Transforming Data into the Time Series Format.mp413.23MB

133 - Slicing Time Series Data.mp45.32MB

134 - Operating on Time Series Data.mp46.79MB

135 - Extracting Statistics from Time Series.mp410.76MB

136 - Building Hidden Markov Models for Sequential Data.mp417.7MB

137 - Building Conditional Random Fields for Sequential Text Data.mp419.05MB

138 - Analyzing Stock Market Data with Hidden Markov Models.mp411.84MB

139 - Operating on Images Using OpenCV-Python.mp416.06MB

14 - Adding Static Data Files to the Package.mp44.54MB

140 - Detecting Edges.mp413.63MB

141 - Histogram Equalization.mp411.46MB

142 - Detecting Corners and SIFT Feature Points.mp416.86MB

143 - Building a Star Feature Detector.mp47.35MB

144 - Creating Features Using Visual Codebook and Vector Quantization.mp419.96MB

145 - Training an Image Classifier Using Extremely Random Forests.mp411.41MB

146 - Building an object recognizer.mp47.72MB

147 - Capturing and Processing Video from a Webcam.mp46.95MB

148 - Building a Face Detector using Haar Cascades.mp411.01MB

149 - Building Eye and Nose Detectors.mp48.23MB

15 - PEP 8 and Writing Readable Code.mp423.79MB

150 - Performing Principal Component Analysis.mp47.98MB

151 - Performing Kernel Principal Component Analysis.mp48.42MB

152 - Performing Blind Source Separation.mp410.05MB

153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp420.53MB

154 - Building a Perceptron.mp49.19MB

155 - Building a Single-Layer Neural Network.mp45.93MB

156 - Building a deep neural network.mp49.15MB

157 - Creating a Vector Quantizer.mp48.36MB

158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp410.18MB

159 - Visualizing the Characters in an Optical Character Recognition Database.mp45.17MB

16 - Using Version Control.mp416.75MB

160 - Building an Optical Character Recognizer Using Neural Networks.mp410.37MB

161 - Plotting 3D Scatter plots.mp48.03MB

162 - Plotting Bubble Plots.mp43.66MB

163 - Animating Bubble Plots.mp49.43MB

164 - Drawing Pie Charts.mp45.57MB

165 - Plotting Date-Formatted Time Series Data.mp45.96MB

166 - Plotting Histograms.mp43.67MB

167 - Visualizing Heat Maps.mp44MB

168 - Animating Dynamic Signals.mp46.79MB

169 - The Course Overview.mp417.84MB

17 - Using venv to Create a Stable and Isolated Work Area.mp48.15MB

170 - What Is Deep Learning.mp47.37MB

171 - Open Source Libraries for Deep Learning.mp421.33MB

172 - Deep Learning Hello World! Classifying the MNIST Data.mp434.69MB

173 - Introduction to Backpropagation.mp49.32MB

174 - Understanding Deep Learning with Theano.mp419.26MB

175 - Optimizing a Simple Model in Pure Theano.mp433.58MB

176 - Keras Behind the Scenes.mp424.43MB

177 - Fully Connected or Dense Layers.mp421.89MB

178 - Convolutional and Pooling Layers.mp425.35MB

179 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks.mp420.32MB

18 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp438.58MB

180 - Loading Pre-trained Models with Theano.mp423.52MB

181 - Reusing Pre-trained Models in New Applications.mp431.83MB

182 - Theano for Loops – the scan Module.mp419.47MB

183 - Recurrent Layers.mp424.84MB

184 - Recurrent Versus Convolutional Layers.mp46.58MB

185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp429.72MB

186 - Bonus Challenge – Automatic Image Captioning.mp421.25MB

187 - Captioning TensorFlow – Google's Machine Learning Library.mp421.61MB

19 - Getting the Most Out of docstrings 2 - doctest.mp47.42MB

20 - Making a Package Executable via python -m.mp49.19MB

21 - Handling Command-Line Arguments with argparse.mp412.23MB

22 - Interacting with the User.mp48.64MB

23 - Executing Other Programs with Subprocess.mp445.53MB

24 - Using Shell Scripts or Batch Files to Run Our Programs.mp44.62MB

25 - Using concurrent.futures.mp446.73MB

26 - Using Multiprocessing.mp421.9MB

27 - Understanding Why This Isn't Like Parallel Processing.mp417.4MB

28 - Using the asyncio Event Loop and Coroutine Scheduler.mp413.35MB

29 - Waiting for Data to Become Available.mp46.66MB

30 - Synchronizing Multiple Tasks.mp413.32MB

31 - Communicating Across the Network.mp411.34MB

32 - Using Function Decorators.mp412.98MB

33 - Function Annotations.mp413.61MB

34 - Class Decorators.mp411.44MB

35 - Metaclasses.mp49.83MB

36 - Context Managers.mp411.35MB

37 - Descriptors.mp419.63MB

38 - Understanding the Principles of Unit Testing.mp48.5MB

39 - Using the unittest Package.mp417.13MB

40 - Using unittest.mock.mp410.55MB

41 - Using unittest's Test Discovery.mp49.72MB

42 - Using Nose for Unified Test Discover and Reporting.mp411MB

43 - What Does Reactive Programming Mean.mp44.82MB

44 - Building a Simple Reactive Programming Framework.mp414.64MB

45 - Using the Reactive Extensions for Python (RxPY).mp433.64MB

46 - Microservices and the Advantages of Process Isolation.mp48.2MB

47 - Building a High-Level Microservice with Flask.mp424.79MB

48 - Building a Low-Level Microservice with nameko.mp412.78MB

49 - Advantages and Disadvantages of Compiled Code.mp410.42MB

50 - Accessing a Dynamic Library Using ctypes.mp414.92MB

51 - Interfacing with C Code Using Cython.mp427.33MB

52 - The Course Overview.mp49.69MB

53 - Brief Introduction to Data Mining.mp48.59MB

54 - Data Mining Basic Concepts and Applications.mp414.24MB

55 - Why Python.mp45.22MB

56 - Basics of Python.mp49.58MB

57 - Installing IPython.mp43.88MB

58 - Installing the Numpy Library.mp48.8MB

59 - Installing the pandas Library.mp414.97MB

60 - Installing Matplotlib.mp411.96MB

61 - Installing scikit-learn.mp43.75MB

62 - Data Cleaning.mp49.19MB

63 - Data Preprocessing Techniques.mp48.41MB

64 - Linear Regression Basic Model Approach.mp414.03MB

65 - Evaluating Regression Models.mp49.14MB

66 - Basic Regression Model Implementation to Predict House Prices.mp435.83MB

67 - Regression Model Implementation to Predict Television Show Viewers.mp440.35MB

68 - Logistic Regression.mp46.92MB

69 - K – Nearest Neighbors Classifier.mp48.89MB

70 - Support Vector Machine.mp49.4MB

71 - Logistic Regression Model Implementation.mp447.17MB

72 - K – Nearest Neighbor Classifier Implementation.mp438.31MB

73 - Preprocessing Data Using Different Techniques.mp426.46MB

74 - Label Encoding.mp410.54MB

75 - Building a Linear Regressor.mp419.66MB

76 - Regression Accuracy and Model Persistence.mp417.5MB

77 - Building a Ridge Regressor.mp412.3MB

78 - Building a Polynomial Regressor.mp411.43MB

79 - Estimating housing prices.mp416.9MB

80 - Computing relative importance of features.mp47.58MB

81 - Estimating bicycle demand distribution.mp417.97MB

82 - Building a Simple Classifier.mp412.21MB

83 - Building a Logistic Regression Classifier.mp420.2MB

84 - Building a Naive Bayes’ Classifier.mp48.74MB

85 - Splitting the Dataset for Training and Testing.mp46.14MB

86 - Evaluating the Accuracy Using Cross-Validation.mp48.21MB

87 - Visualizing the Confusion Matrix and Extracting the Performance Report.mp415.79MB

88 - Evaluating Cars based on Their Characteristics.mp423.16MB

89 - Extracting Validation Curves.mp414.08MB

90 - Extracting Learning Curves.mp47.31MB

91 - Extracting the Income Bracket.mp415.04MB

92 - Building a Linear Classifier Using Support Vector Machine.mp420.2MB

93 - Building Nonlinear Classifier Using SVMs.mp48MB

94 - Tackling Class Imbalance.mp413.3MB

95 - Extracting Confidence Measurements.mp412.01MB

96 - Finding Optimal Hyper-Parameters.mp410.42MB

97 - Building an Event Predictor.mp416.95MB

98 - Estimating Traffic.mp410.82MB

99 - Clustering Data Using the k-means Algorithm.mp413.45MB