Packt Publishing - Deep Dive into Python Machine Learning

磁力链接/BT种子名称

Packt Publishing - Deep Dive into Python Machine Learning

磁力链接/BT种子简介

种子哈希：8d48ebb15a1af945bf781071adb6be1aa5602953
文件大小： 2.64G
已经下载：685次
下载速度：极快
收录时间：2017-09-25
最近下载：2024-11-03

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:8D48EBB15A1AF945BF781071ADB6BE1AA5602953
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 暗网禁地 91短视频 TikTok成人版 PornHub 草榴社区 乱伦社区

最近搜索

文件列表

  • Project_Files/Data Mining with Python- Implementing Classification and Regression.zip 17.2 kB
  • Project_Files/Deep Learning with Python [Video].zip 605.0 kB
  • Project_Files/Mastering Python - Second Edition [Video].zip 36.7 kB
  • Project_Files/Python Machine Learning Solutions [Video].zip 60.6 MB
  • 01 - The Course Overview.mp4 15.7 MB
  • 02 - Python Basic Syntax and Block Structure.mp4 23.6 MB
  • 03 - Built-in Data Structures and Comprehensions.mp4 18.7 MB
  • 04 - First-Class Functions and Classes.mp4 12.9 MB
  • 05 - Extensive Standard Library.mp4 32.7 MB
  • 06 - New in Python 3.5.mp4 22.0 MB
  • 07 - Downloading and Installing Python.mp4 16.1 MB
  • 08 - Using the Command-Line and the Interactive Shell.mp4 7.4 MB
  • 09 - Installing Packages with pip.mp4 11.6 MB
  • 10 - Finding Packages in the Python Package Index.mp4 22.8 MB
  • 100 - Compressing an Image Using Vector Quantization.mp4 17.1 MB
  • 101 - Building a Mean Shift Clustering.mp4 11.8 MB
  • 102 - Grouping Data Using Agglomerative Clustering.mp4 14.2 MB
  • 103 - Evaluating the Performance of Clustering Algorithms.mp4 13.4 MB
  • 104 - Automatically Estimating the Number of Clusters Using DBSCAN.mp4 15.7 MB
  • 105 - Finding Patterns in Stock Market Data.mp4 11.9 MB
  • 106 - Building a Customer Segmentation Model.mp4 10.3 MB
  • 107 - Building Function Composition for Data Processing.mp4 14.3 MB
  • 108 - Building Machine Learning Pipelines.mp4 15.9 MB
  • 109 - Finding the Nearest Neighbors.mp4 8.4 MB
  • 11 - Creating an Empty Package.mp4 12.2 MB
  • 110 - Constructing a k-nearest Neighbors Classifier.mp4 20.7 MB
  • 111 - Constructing a k-nearest Neighbors Regressor.mp4 10.2 MB
  • 112 - Computing the Euclidean Distance Score.mp4 9.7 MB
  • 113 - Computing the Pearson Correlation Score.mp4 8.7 MB
  • 114 - Finding Similar Users in a Dataset.mp4 7.2 MB
  • 115 - Generating Movie Recommendations.mp4 10.7 MB
  • 116 - Preprocessing Data Using Tokenization.mp4 13.3 MB
  • 117 - Stemming Text Data.mp4 9.2 MB
  • 118 - Converting Text to Its Base Form Using Lemmatization.mp4 8.6 MB
  • 119 - Dividing Text Using Chunking.mp4 7.8 MB
  • 12 - Adding Modules to the Package.mp4 8.4 MB
  • 120 - Building a Bag-of-Words Model.mp4 12.3 MB
  • 121 - Building a Text Classifier.mp4 18.8 MB
  • 122 - Identifying the Gender.mp4 10.5 MB
  • 123 - Analyzing the Sentiment of a Sentence.mp4 15.1 MB
  • 124 - Identifying Patterns in Text Using Topic Modelling.mp4 20.7 MB
  • 125 - Reading and Plotting Audio Data.mp4 9.8 MB
  • 126 - Transforming Audio Signals into the Frequency Domain.mp4 9.8 MB
  • 127 - Generating Audio Signals with Custom Parameters.mp4 8.0 MB
  • 128 - Synthesizing Music.mp4 10.3 MB
  • 129 - Extracting Frequency Domain Features.mp4 8.5 MB
  • 13 - Importing One of the Package's Modules from Another.mp4 9.7 MB
  • 130 - Building Hidden Markov Models.mp4 10.1 MB
  • 131 - Building a Speech Recognizer.mp4 13.6 MB
  • 132 - Transforming Data into the Time Series Format.mp4 13.9 MB
  • 133 - Slicing Time Series Data.mp4 5.6 MB
  • 134 - Operating on Time Series Data.mp4 7.1 MB
  • 135 - Extracting Statistics from Time Series.mp4 11.3 MB
  • 136 - Building Hidden Markov Models for Sequential Data.mp4 18.6 MB
  • 137 - Building Conditional Random Fields for Sequential Text Data.mp4 20.0 MB
  • 138 - Analyzing Stock Market Data with Hidden Markov Models.mp4 12.4 MB
  • 139 - Operating on Images Using OpenCV-Python.mp4 16.8 MB
  • 14 - Adding Static Data Files to the Package.mp4 4.8 MB
  • 140 - Detecting Edges.mp4 14.3 MB
  • 141 - Histogram Equalization.mp4 12.0 MB
  • 142 - Detecting Corners and SIFT Feature Points.mp4 17.7 MB
  • 143 - Building a Star Feature Detector.mp4 7.7 MB
  • 144 - Creating Features Using Visual Codebook and Vector Quantization.mp4 20.9 MB
  • 145 - Training an Image Classifier Using Extremely Random Forests.mp4 12.0 MB
  • 146 - Building an object recognizer.mp4 8.1 MB
  • 147 - Capturing and Processing Video from a Webcam.mp4 7.3 MB
  • 148 - Building a Face Detector using Haar Cascades.mp4 11.5 MB
  • 149 - Building Eye and Nose Detectors.mp4 8.6 MB
  • 15 - PEP 8 and Writing Readable Code.mp4 24.9 MB
  • 150 - Performing Principal Component Analysis.mp4 8.4 MB
  • 151 - Performing Kernel Principal Component Analysis.mp4 8.8 MB
  • 152 - Performing Blind Source Separation.mp4 10.5 MB
  • 153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp4 21.5 MB
  • 154 - Building a Perceptron.mp4 9.6 MB
  • 155 - Building a Single-Layer Neural Network.mp4 6.2 MB
  • 156 - Building a deep neural network.mp4 9.6 MB
  • 157 - Creating a Vector Quantizer.mp4 8.8 MB
  • 158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp4 10.7 MB
  • 159 - Visualizing the Characters in an Optical Character Recognition Database.mp4 5.4 MB
  • 16 - Using Version Control.mp4 17.6 MB
  • 160 - Building an Optical Character Recognizer Using Neural Networks.mp4 10.9 MB
  • 161 - Plotting 3D Scatter plots.mp4 8.4 MB
  • 162 - Plotting Bubble Plots.mp4 3.8 MB
  • 163 - Animating Bubble Plots.mp4 9.9 MB
  • 164 - Drawing Pie Charts.mp4 5.8 MB
  • 165 - Plotting Date-Formatted Time Series Data.mp4 6.3 MB
  • 166 - Plotting Histograms.mp4 3.8 MB
  • 167 - Visualizing Heat Maps.mp4 4.2 MB
  • 168 - Animating Dynamic Signals.mp4 7.1 MB
  • 169 - The Course Overview.mp4 18.7 MB
  • 17 - Using venv to Create a Stable and Isolated Work Area.mp4 8.5 MB
  • 170 - What Is Deep Learning.mp4 7.7 MB
  • 171 - Open Source Libraries for Deep Learning.mp4 22.4 MB
  • 172 - Deep Learning Hello World! Classifying the MNIST Data.mp4 36.4 MB
  • 173 - Introduction to Backpropagation.mp4 9.8 MB
  • 174 - Understanding Deep Learning with Theano.mp4 20.2 MB
  • 175 - Optimizing a Simple Model in Pure Theano.mp4 35.2 MB
  • 176 - Keras Behind the Scenes.mp4 25.6 MB
  • 177 - Fully Connected or Dense Layers.mp4 22.9 MB
  • 178 - Convolutional and Pooling Layers.mp4 26.6 MB
  • 179 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks.mp4 21.3 MB
  • 18 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp4 40.5 MB
  • 180 - Loading Pre-trained Models with Theano.mp4 24.7 MB
  • 181 - Reusing Pre-trained Models in New Applications.mp4 33.4 MB
  • 182 - Theano for Loops – the scan Module.mp4 20.4 MB
  • 183 - Recurrent Layers.mp4 26.0 MB
  • 184 - Recurrent Versus Convolutional Layers.mp4 6.9 MB
  • 185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp4 31.2 MB
  • 186 - Bonus Challenge – Automatic Image Captioning.mp4 22.3 MB
  • 187 - Captioning TensorFlow – Google's Machine Learning Library.mp4 22.7 MB
  • 19 - Getting the Most Out of docstrings 2 - doctest.mp4 7.8 MB
  • 20 - Making a Package Executable via python -m.mp4 9.6 MB
  • 21 - Handling Command-Line Arguments with argparse.mp4 12.8 MB
  • 22 - Interacting with the User.mp4 9.1 MB
  • 23 - Executing Other Programs with Subprocess.mp4 47.7 MB
  • 24 - Using Shell Scripts or Batch Files to Run Our Programs.mp4 4.8 MB
  • 25 - Using concurrent.futures.mp4 49.0 MB
  • 26 - Using Multiprocessing.mp4 23.0 MB
  • 27 - Understanding Why This Isn't Like Parallel Processing.mp4 18.2 MB
  • 28 - Using the asyncio Event Loop and Coroutine Scheduler.mp4 14.0 MB
  • 29 - Waiting for Data to Become Available.mp4 7.0 MB
  • 30 - Synchronizing Multiple Tasks.mp4 14.0 MB
  • 31 - Communicating Across the Network.mp4 11.9 MB
  • 32 - Using Function Decorators.mp4 13.6 MB
  • 33 - Function Annotations.mp4 14.3 MB
  • 34 - Class Decorators.mp4 12.0 MB
  • 35 - Metaclasses.mp4 10.3 MB
  • 36 - Context Managers.mp4 11.9 MB
  • 37 - Descriptors.mp4 20.6 MB
  • 38 - Understanding the Principles of Unit Testing.mp4 8.9 MB
  • 39 - Using the unittest Package.mp4 18.0 MB
  • 40 - Using unittest.mock.mp4 11.1 MB
  • 41 - Using unittest's Test Discovery.mp4 10.2 MB
  • 42 - Using Nose for Unified Test Discover and Reporting.mp4 11.5 MB
  • 43 - What Does Reactive Programming Mean.mp4 5.1 MB
  • 44 - Building a Simple Reactive Programming Framework.mp4 15.4 MB
  • 45 - Using the Reactive Extensions for Python (RxPY).mp4 35.3 MB
  • 46 - Microservices and the Advantages of Process Isolation.mp4 8.6 MB
  • 47 - Building a High-Level Microservice with Flask.mp4 26.0 MB
  • 48 - Building a Low-Level Microservice with nameko.mp4 13.4 MB
  • 49 - Advantages and Disadvantages of Compiled Code.mp4 10.9 MB
  • 50 - Accessing a Dynamic Library Using ctypes.mp4 15.6 MB
  • 51 - Interfacing with C Code Using Cython.mp4 28.7 MB
  • 52 - The Course Overview.mp4 10.2 MB
  • 53 - Brief Introduction to Data Mining.mp4 9.0 MB
  • 54 - Data Mining Basic Concepts and Applications.mp4 14.9 MB
  • 55 - Why Python.mp4 5.5 MB
  • 56 - Basics of Python.mp4 10.0 MB
  • 57 - Installing IPython.mp4 4.1 MB
  • 58 - Installing the Numpy Library.mp4 9.2 MB
  • 59 - Installing the pandas Library.mp4 15.7 MB
  • 60 - Installing Matplotlib.mp4 12.5 MB
  • 61 - Installing scikit-learn.mp4 3.9 MB
  • 62 - Data Cleaning.mp4 9.6 MB
  • 63 - Data Preprocessing Techniques.mp4 8.8 MB
  • 64 - Linear Regression Basic Model Approach.mp4 14.7 MB
  • 65 - Evaluating Regression Models.mp4 9.6 MB
  • 66 - Basic Regression Model Implementation to Predict House Prices.mp4 37.6 MB
  • 67 - Regression Model Implementation to Predict Television Show Viewers.mp4 42.3 MB
  • 68 - Logistic Regression.mp4 7.3 MB
  • 69 - K – Nearest Neighbors Classifier.mp4 9.3 MB
  • 70 - Support Vector Machine.mp4 9.9 MB
  • 71 - Logistic Regression Model Implementation.mp4 49.5 MB
  • 72 - K – Nearest Neighbor Classifier Implementation.mp4 40.2 MB
  • 73 - Preprocessing Data Using Different Techniques.mp4 27.7 MB
  • 74 - Label Encoding.mp4 11.1 MB
  • 75 - Building a Linear Regressor.mp4 20.6 MB
  • 76 - Regression Accuracy and Model Persistence.mp4 18.4 MB
  • 77 - Building a Ridge Regressor.mp4 12.9 MB
  • 78 - Building a Polynomial Regressor.mp4 12.0 MB
  • 79 - Estimating housing prices.mp4 17.7 MB
  • 80 - Computing relative importance of features.mp4 7.9 MB
  • 81 - Estimating bicycle demand distribution.mp4 18.8 MB
  • 82 - Building a Simple Classifier.mp4 12.8 MB
  • 83 - Building a Logistic Regression Classifier.mp4 21.2 MB
  • 84 - Building a Naive Bayes’ Classifier.mp4 9.2 MB
  • 85 - Splitting the Dataset for Training and Testing.mp4 6.4 MB
  • 86 - Evaluating the Accuracy Using Cross-Validation.mp4 8.6 MB
  • 87 - Visualizing the Confusion Matrix and Extracting the Performance Report.mp4 16.6 MB
  • 88 - Evaluating Cars based on Their Characteristics.mp4 24.3 MB
  • 89 - Extracting Validation Curves.mp4 14.8 MB
  • 90 - Extracting Learning Curves.mp4 7.7 MB
  • 91 - Extracting the Income Bracket.mp4 15.8 MB
  • 92 - Building a Linear Classifier Using Support Vector Machine.mp4 21.2 MB
  • 93 - Building Nonlinear Classifier Using SVMs.mp4 8.4 MB
  • 94 - Tackling Class Imbalance.mp4 13.9 MB
  • 95 - Extracting Confidence Measurements.mp4 12.6 MB
  • 96 - Finding Optimal Hyper-Parameters.mp4 10.9 MB
  • 97 - Building an Event Predictor.mp4 17.8 MB
  • 98 - Estimating Traffic.mp4 11.3 MB
  • 99 - Clustering Data Using the k-means Algorithm.mp4 14.1 MB

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！
>