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Packt Publishing Deep Dive into Python chine Learning
  • ZIPProject_Files\\/Deep Learning with Python [Video].zip590.78KB
  • ZIPProject_Files\\/Python chine Learning Solutions [Video].zip57.83MB
  • MP401 - The Course Overview.mp414.93MB
  • MP402 - Python Basic Syntax and Block Structure.mp422.54MB
  • MP403 - Built-in Data Structures and Comprehensions.mp417.79MB
  • MP404 - First-Class Functions and Classes.mp412.33MB
  • MP405 - Extensive Standard Library.mp431.14MB
  • MP406 - New in Python 3.5.mp421.01MB
  • MP407 - Downloading and Installing Python.mp415.34MB
  • MP408 - Using the Comnd-Line and the Interactive Shell.mp47.10MB
  • MP409 - Installing Packages with pip.mp411.04MB
  • MP410 - Finding Packages in the Python Package Index.mp421.78MB
  • MP4100 - Compressing an Ige Using Vector Quantization.mp416.33MB
  • MP4101 - Building a Mean Shift Clustering.mp411.26MB
  • MP4102 - Grouping Data Using Agglomerative Clustering.mp413.54MB
  • MP4103 - Evaluating the Perfornce of Clustering Algorithms.mp412.74MB
  • MP4104 - Autotically Estimating the Number of Clusters Using DBSCAN.mp414.94MB
  • MP4105 - Finding Patterns in Stock rket Data.mp411.34MB
  • MP4106 - Building a Customer Segmentation Model.mp49.78MB
  • MP4107 - Building Function Composition for Data Processing.mp413.67MB
  • MP4108 - Building chine Learning Pipelines.mp415.17MB
  • MP4109 - Finding the Nearest Neighbors.mp48.05MB
  • MP411 - Creating an Empty Package.mp411.59MB
  • MP4110 - Constructing a k-nearest Neighbors Classifier.mp419.77MB
  • MP4111 - Constructing a k-nearest Neighbors Regressor.mp49.75MB
  • MP4112 - Computing the Euclidean Distance Score.mp49.21MB
  • MP4113 - Computing the Pearson Correlation Score.mp48.32MB
  • MP4 - Finding Similar Users in a Dataset.mp46.89MB
  • MP4115 - Generating Movie Recommendations.mp410.20MB
  • MP4116 - Preprocessing Data Using Tokenization.mp412.67MB
  • MP4117 - Stemming Text Data.mp48.77MB
  • MP4118 - Converting Text to Its ba<x>se Form Using Lemtization.mp48.25MB
  • MP4119 - Dividing Text Using Chunking.mp47.42MB
  • MP412 - Adding Modules to the Package.mp47.99MB
  • MP4120 - Building a Bag-of-Words Model.mp411.71MB
  • MP4121 - Building a Text Classifier.mp417.97MB
  • MP4122 - Identifying the Gender.mp410.00MB
  • MP4123 - Analyzing the Sentiment of a Sentence.mp414.39MB
  • MP4124 - Identifying Patterns in Text Using ic Modelling.mp419.76MB
  • MP4125 - Reading and Plotting Audio Data.mp49.35MB
  • MP4126 - Transforming Audio Signals into the Frequency Doin.mp49.32MB
  • MP4127 - Generating Audio Signals with Custom Parameters.mp47.64MB
  • MP4128 - Synthesizing Music.mp49.81MB
  • MP4129 - Extracting Frequency Doin Features.mp48.13MB
  • MP413 - Importing One of the Package\s Modules from Another.mp49.29MB
  • MP4130 - Building Hidden rkov Models.mp49.60MB
  • MP4131 - Building a Speech Recognizer.mp412.94MB
  • MP4132 - Transforming Data into the Time Series Fort.mp413.23MB
  • MP4133 - Slicing Time Series Data.mp45.32MB
  • MP4134 - Operating on Time Series Data.mp46.79MB
  • MP4135 - Extracting Statistics from Time Series.mp410.76MB
  • MP4136 - Building Hidden rkov Models for Sequential Data.mp417.70MB
  • MP4137 - Building Conditional Random Fields for Sequential Text Data.mp419.05MB
  • MP4138 - Analyzing Stock rket Data with Hidden Markov Models.mp411.84MB
  • MP4139 - Operating on Iges Using OpenCV-Python.mp416.06MB
  • MP414 - Adding Static Data Files to the Package.mp44.54MB
  • MP4140 - Detecting Edges.mp413.63MB
  • MP4141 - Histogram Equalization.mp411.46MB
  • MP4142 - Detecting Corners and SIFT Feature Points.mp416.86MB
  • MP4143 - Building a Star Feature Detector.mp47.35MB
  • MP4144 - Creating Features Using Visual Codebook and Vector Quantization.mp419.96MB
  • MP4145 - Training an Ige Classifier Using Extremely Random Forests.mp411.41MB
  • MP4146 - Building an ob<x>ject recognizer.mp47.72MB
  • MP4147 - Capturing and Processing Video from a Webcam.mp46.95MB
  • MP4148 - Building a Face Detector using Haar Cascades.mp411.01MB
  • MP4149 - Building Eye and Nose Detectors.mp48.23MB
  • MP415 - PEP 8 and Writing Readable Code.mp423.79MB
  • MP4150 - Performing Principal Component Analysis.mp47.98MB
  • MP4151 - Performing Kernel Principal Component Analysis.mp48.42MB
  • MP4152 - Performing Blind Source Separation.mp410.05MB
  • MP4153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp420.53MB
  • MP4154 - Building a Perceptron.mp49.19MB
  • MP4155 - Building a Single-la<x>yer Neural Network.mp45.93MB
  • MP4156 - Building a deep neural network.mp49.15MB
  • MP4157 - Creating a Vector Quantizer.mp48.36MB
  • MP4158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp410.18MB
  • MP4159 - Visualizing the Characters in an Optical Character Recognition Databa<x>se.mp45.17MB
  • MP416 - Using Version Control.mp416.75MB
  • MP4160 - Building an Optical Character Recognizer Using Neural Networks.mp410.37MB
  • MP4161 - Plotting 3D Scatter plots.mp48.03MB
  • MP4162 - Plotting Bubble Plots.mp43.66MB
  • MP4163 - Aniting Bubble Plots.mp49.43MB
  • MP41 - Drawing Pie Charts.mp45.57MB
  • MP4165 - Plotting Date-Fortted Time Series Data.mp45.96MB
  • MP4166 - Plotting Histograms.mp43.67MB
  • MP4167 - Visualizing Heat ps.mp44.00MB
  • MP4168 - Aniting Dynamic Signals.mp46.79MB
  • MP4169 - The Course Overview.mp417.84MB
  • MP417 - Using venv to Create a Stable and Isolated Work Area.mp48.15MB
  • MP4170 - What Is Deep Learning.mp47.37MB
  • MP4171 - Open Source Libraries for Deep Learning.mp421.33MB
  • MP4172 - Deep Learning Hello World! Classifying the MNIST Data.mp434.69MB
  • MP4173 - Introduction to Backpropagation.mp49.32MB
  • MP4174 - Understanding Deep Learning with Theano.mp419.26MB
  • MP4175 - Optimizing a Simple Model in Pure Theano.mp433.58MB
  • MP4176 - Keras Behind the Scenes.mp424.43MB
  • MP4177 - Fully Connected or Dense la<x>yers.mp421.89MB
  • MP4178 - Convolutional and Pooling la<x>yers.mp425.35MB
  • MP4179 - Large Scale Datasets IgeNet and Very Deep Neural Networks.mp420.32MB
  • MP418 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp438.58MB
  • MP4180 - Loading Pre-trained Models with Theano.mp423.52MB
  • MP4181 - Reusing Pre-trained Models in New Applications.mp431.83MB
  • MP4182 - Theano for Loops – the scan Module.mp419.47MB
  • MP4183 - Recurrent la<x>yers.mp424.84MB
  • MP4184 - Recurrent Versus Convolutional la<x>yers.mp46.58MB
  • MP4185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp429.72MB
  • MP4186 - Bonus Challenge – Autotic Image Captioning.mp421.25MB
  • MP4187 - Captioning TensorFlow – Google\s chine Learning Library.mp421.61MB
  • MP419 - Getting the Most Out of docstrings 2 - doctest.mp47.42MB
  • MP420 - king a Package Executable via python -m.mp49.19MB
  • MP421 - Handling Comnd-Line Arguments with argparse.mp412.23MB
  • MP422 - Interacting with the User.mp48.64MB
  • MP423 - Executing Other Programs with Subprocess.mp445.53MB
  • MP424 - Using Shell sc<x>ripts or Batch Files to Run Our Programs.mp44.62MB
  • MP425 - Using concurrent.futures.mp446.73MB
  • MP426 - Using Multiprocessing.mp421.90MB
  • MP427 - Understanding Why This Isn\t Like Parallel Processing.mp417.40MB
  • MP428 - Using the asyncio Event Loop and Coroutine Scheduler.mp413.35MB
  • MP429 - Waiting for Data to Become ailable.mp46.66MB
  • MP430 - Synchronizing Multiple Tasks.mp413.32MB
  • MP431 - Communicating Across the Network.mp411.34MB
  • MP432 - Using Function Decorators.mp412.98MB
  • MP433 - Function Annotations.mp413.61MB
  • MP434 - Class Decorators.mp411.44MB
  • MP435 - me<x>taclasses.mp49.83MB
  • MP436 - Context nagers.mp411.35MB
  • MP437 - Desc<x>riptors.mp419.63MB
  • MP438 - Understanding the Principles of Unit Testing.mp48.50MB
  • MP439 - Using the unittest Package.mp417.13MB
  • MP440 - Using unittest.mock.mp410.55MB
  • MP441 - Using unittest\s Test Discovery.mp49.72MB
  • MP442 - Using Nose for Unified Test Discover and Reporting.mp411.00MB
  • MP443 - What Does Reactive Programming Mean.mp44.82MB
  • MP444 - Building a Simple Reactive Programming fr<x>amework.mp414.64MB
  • MP445 - Using the Reactive Extensions for Python (RxPY).mp433.64MB
  • MP446 - Microservices and the Advantages of Process Isolation.mp48.20MB
  • MP447 - Building a High-Level Microservice with Flask.mp424.79MB
  • MP448 - Building a Low-Level Microservice with nameko.mp412.78MB
  • MP449 - Advantages and Disadvantages of Compiled Code.mp410.42MB
  • MP450 - Accessing a Dynamic Library Using ctypes.mp414.92MB
  • MP451 - Interfacing with C Code Using Cython.mp427.33MB
  • MP452 - The Course Overview.mp49.69MB
  • MP453 - Brief Introduction to Data Mining.mp48.59MB
  • MP454 - Data Mining Basic Concepts and Applications.mp414.24MB
  • MP455 - Why Python.mp45.22MB
  • MP456 - Basics of Python.mp49.58MB
  • MP457 - Installing IPython.mp43.88MB
  • MP458 - Installing the Numpy Library.mp48.80MB
  • MP459 - Installing the pandas Library.mp414.97MB
  • MP460 - Installing tplotlib.mp411.96MB
  • MP461 - Installing scikit-learn.mp43.75MB
  • MP462 - Data Cleaning.mp49.19MB
  • MP463 - Data Preprocessing Techniques.mp48.41MB
  • MP4 - Linear Regression Basic Model Approach.mp414.03MB
  • MP465 - Evaluating Regression Models.mp49.14MB
  • MP466 - Basic Regression Model Implementation to Predict House Prices.mp435.83MB
  • MP467 - Regression Model Implementation to Predict Television Show Viewers.mp440.35MB
  • MP468 - Logistic Regression.mp46.92MB
  • MP469 - K – Nearest Neighbors Classifier.mp48.89MB
  • MP470 - Support Vector chine.mp49.40MB
  • MP471 - Logistic Regression Model Implementation.mp447.17MB
  • MP472 - K – Nearest Neighbor Classifier Implementation.mp438.31MB
  • MP473 - Preprocessing Data Using Different Techniques.mp426.46MB
  • MP474 - Label Encoding.mp410.54MB
  • MP475 - Building a Linear Regressor.mp419.66MB
  • MP476 - Regression Accuracy and Model Persistence.mp417.50MB
  • MP477 - Building a Ridge Regressor.mp412.30MB
  • MP478 - Building a Polynomial Regressor.mp411.43MB
  • MP479 - Estiting housing prices.mp416.90MB
  • MP480 - Computing relative importance of features.mp47.58MB
  • MP481 - Estiting bicycle demand distribution.mp417.97MB
  • MP482 - Building a Simple Classifier.mp412.21MB
  • MP483 - Building a Logistic Regression Classifier.mp420.20MB
  • MP484 - Building a Naive Bayes’ Classifier.mp48.74MB
  • MP485 - Splitting the Dataset for Training and Testing.mp46.14MB
  • MP486 - Evaluating the Accuracy Using Cross-Validation.mp48.21MB
  • MP487 - Visualizing the Confusion trix and Extracting the Performance Report.mp415.79MB
  • MP488 - Evaluating Cars ba<x>sed on Their Characteristics.mp423.16MB
  • MP489 - Extracting Validation Curves.mp414.08MB
  • MP490 - Extracting Learning Curves.mp47.31MB
  • MP491 - Extracting the Income Bracket.mp415.04MB
  • MP492 - Building a Linear Classifier Using Support Vector chine.mp420.20MB
  • MP493 - Building Nonlinear Classifier Using SVMs.mp48.00MB
  • MP494 - Tackling Class Imbalance.mp413.30MB
  • MP495 - Extracting Confidence Measurements.mp412.01MB
  • MP496 - Finding Optil Hyper-Parameters.mp410.42MB
  • MP497 - Building an Event Predictor.mp416.95MB
  • MP498 - Estiting Traffic.mp410.82MB
  • MP499 - Clustering Data Using the k-means Algorithm.mp413.45MB
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