Packt-Publishing-Deep-Di-1gbTr
Project_Files\\/Deep Learning with Python [Video].zip590.78KB
Project_Files\\/Python chine Learning Solutions [Video].zip57.83MB
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 Comnd-Line and the Interactive Shell.mp47.10MB
09 - Installing Packages with pip.mp411.04MB
10 - Finding Packages in the Python Package Index.mp421.78MB
100 - Compressing an Ige Using Vector Quantization.mp416.33MB
101 - Building a Mean Shift Clustering.mp411.26MB
102 - Grouping Data Using Agglomerative Clustering.mp413.54MB
103 - Evaluating the Perfornce of Clustering Algorithms.mp412.74MB
104 - Autotically Estimating the Number of Clusters Using DBSCAN.mp414.94MB
105 - Finding Patterns in Stock rket Data.mp411.34MB
106 - Building a Customer Segmentation Model.mp49.78MB
107 - Building Function Composition for Data Processing.mp413.67MB
108 - Building chine 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
- Finding Similar Users in a Dataset.mp46.89MB
115 - Generating Movie Recommendations.mp410.20MB
116 - Preprocessing Data Using Tokenization.mp412.67MB
117 - Stemming Text Data.mp48.77MB
118 - Converting Text to Its ba<x>se Form Using Lemtization.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.mp410.00MB
123 - Analyzing the Sentiment of a Sentence.mp414.39MB
124 - Identifying Patterns in Text Using ic Modelling.mp419.76MB
125 - Reading and Plotting Audio Data.mp49.35MB
126 - Transforming Audio Signals into the Frequency Doin.mp49.32MB
127 - Generating Audio Signals with Custom Parameters.mp47.64MB
128 - Synthesizing Music.mp49.81MB
129 - Extracting Frequency Doin Features.mp48.13MB
13 - Importing One of the Package\s Modules from Another.mp49.29MB
130 - Building Hidden rkov Models.mp49.60MB
131 - Building a Speech Recognizer.mp412.94MB
132 - Transforming Data into the Time Series Fort.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 rkov Models for Sequential Data.mp417.70MB
137 - Building Conditional Random Fields for Sequential Text Data.mp419.05MB
138 - Analyzing Stock rket Data with Hidden Markov Models.mp411.84MB
139 - Operating on Iges 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 Ige Classifier Using Extremely Random Forests.mp411.41MB
146 - Building an ob<x>ject 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-la<x>yer 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 Databa<x>se.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 - Aniting Bubble Plots.mp49.43MB
1 - Drawing Pie Charts.mp45.57MB
165 - Plotting Date-Fortted Time Series Data.mp45.96MB
166 - Plotting Histograms.mp43.67MB
167 - Visualizing Heat ps.mp44.00MB
168 - Aniting 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 la<x>yers.mp421.89MB
178 - Convolutional and Pooling la<x>yers.mp425.35MB
179 - Large Scale Datasets IgeNet 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 la<x>yers.mp424.84MB
184 - Recurrent Versus Convolutional la<x>yers.mp46.58MB
185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp429.72MB
186 - Bonus Challenge – Autotic Image Captioning.mp421.25MB
187 - Captioning TensorFlow – Google\s chine Learning Library.mp421.61MB
19 - Getting the Most Out of docstrings 2 - doctest.mp47.42MB
20 - king a Package Executable via python -m.mp49.19MB
21 - Handling Comnd-Line Arguments with argparse.mp412.23MB
22 - Interacting with the User.mp48.64MB
23 - Executing Other Programs with Subprocess.mp445.53MB
24 - Using Shell sc<x>ripts or Batch Files to Run Our Programs.mp44.62MB
25 - Using concurrent.futures.mp446.73MB
26 - Using Multiprocessing.mp421.90MB
27 - Understanding Why This Isn\t Like Parallel Processing.mp417.40MB
28 - Using the asyncio Event Loop and Coroutine Scheduler.mp413.35MB
29 - Waiting for Data to Become ailable.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 - me<x>taclasses.mp49.83MB
36 - Context nagers.mp411.35MB
37 - Desc<x>riptors.mp419.63MB
38 - Understanding the Principles of Unit Testing.mp48.50MB
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.mp411.00MB
43 - What Does Reactive Programming Mean.mp44.82MB
44 - Building a Simple Reactive Programming fr<x>amework.mp414.64MB
45 - Using the Reactive Extensions for Python (RxPY).mp433.64MB
46 - Microservices and the Advantages of Process Isolation.mp48.20MB
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.mp48.80MB
59 - Installing the pandas Library.mp414.97MB
60 - Installing tplotlib.mp411.96MB
61 - Installing scikit-learn.mp43.75MB
62 - Data Cleaning.mp49.19MB
63 - Data Preprocessing Techniques.mp48.41MB
- 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 chine.mp49.40MB
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.mp417.50MB
77 - Building a Ridge Regressor.mp412.30MB
78 - Building a Polynomial Regressor.mp411.43MB
79 - Estiting housing prices.mp416.90MB
80 - Computing relative importance of features.mp47.58MB
81 - Estiting bicycle demand distribution.mp417.97MB
82 - Building a Simple Classifier.mp412.21MB
83 - Building a Logistic Regression Classifier.mp420.20MB
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 trix and Extracting the Performance Report.mp415.79MB
88 - Evaluating Cars ba<x>sed 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 chine.mp420.20MB
93 - Building Nonlinear Classifier Using SVMs.mp48.00MB
94 - Tackling Class Imbalance.mp413.30MB
95 - Extracting Confidence Measurements.mp412.01MB
96 - Finding Optil Hyper-Parameters.mp410.42MB
97 - Building an Event Predictor.mp416.95MB
98 - Estiting Traffic.mp410.82MB
99 - Clustering Data Using the k-means Algorithm.mp413.45MB
- CreateTime2022-02-27
- UpdateTime2022-03-03
- FileTotalCount191
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