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【课程】从Python到TensorFlow，精通深度学习

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2017-09-14 14:02:22
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【课程】从Python到TensorFlow，精通深度学习
学习
英语
课程
编程
深度学习
Python
Deep Learning
TensorFlow
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视频选集
（1/99）
01 - The Course Overview
03:42
02 - Downloading and Installing Python
03:03
03 - Using the Command Line and the Interactive Shell
03:31
04 - Installing Packages with pip
02:55
05 - Finding Packages in the Python Package Index
03:09
06 - Creating an Empty Package
04:10
07 - Adding Modules to the Package
04:37
08 - Importing One of the Package's Modules from Another
05:02
09 - Adding Data Files to the Package
02:33
10 - PEP 8 and Writing Readable Code
06:20
11 - Using Version Control
06:01
12 - Using venv to Create a Stable and Isolated Work Area
03:27
13 - Getting the Most Out of docstrings Part 1 – PEP 257 and Sphinx
06:24
14 - Getting the Most Out of docstrings Part 2 – doctest
02:52
15 - Making a Package Executable via python – m
04:15
16 - Handling Command-line Arguments with argparse
05:21
17 - Text-mode Interactivity
03:39
18 - Executing Other Programs
05:04
19 - Using Shell Scripts or Batch Files to Launch Programs
01:54
20 - Using concurrent.futures
09:58
21 - Using Multiprocessing
08:38
22 - Understanding Why Asynchronous I_O Isn't Like Parallel Processing
05:51
23 - Using the asyncio Event Loop and Coroutine Scheduler
05:26
24 - Futures
06:05
25 - Making Asynchronous Tasks Interoperate
05:51
26 - Communicating across the Network
03:16
27 - Using Function Decorators
05:02
28 - Using Function Annotations
04:55
29 - Using Class Decorators
04:29
30 - Using Metaclasses
04:56
31 - Using Context Managers
04:42
32 - Using Descriptors
05:38
33 - Understanding the Principles of Unit Testing
03:32
34 - Using unittest
05:37
35 - Using unittest.mock
05:40
36 - Using unittest's Test Discovery
03:56
37 - Using Nose for Unified Test Discovery and Reporting
03:59
38 - The Course Overview
03:55
39 - Brief Introduction to Data Mining
04:38
40 - Data Mining Basic Concepts and Applications
07:06
41 - Why Python
03:32
42 - Basics of Python
05:55
43 - Installing IPython
02:10
44 - Installing the Numpy Library
04:33
45 - Installing the pandas Library
05:33
46 - Installing Matplotlib
02:42
47 - Installing scikit-learn
02:37
48 - Data Cleaning
05:32
49 - Data Preprocessing Techniques
05:09
50 - Linear Regression Basic Model Approach
08:24
51 - Evaluating Regression Models
05:32
52 - Basic Regression Model Implementation to Predict House Prices
09:21
53 - Regression Model Implementation to Predict Television Show Viewers
09:46
54 - Logistic Regression
04:02
55 - K – Nearest Neighbors Classifier
05:52
56 - Support Vector Machine
05:42
57 - Logistic Regression Model Implementation
10:45
58 - K – Nearest Neighbor Classifier Implementation
10:44
59 - The Course Overview
03:52
60 - What Is Deep Learning
04:09
61 - Open Source Libraries for Deep Learning
04:31
62 - Deep Learning Hello World! Classifying the MNIST Data
07:58
63 - Introduction to Backpropagation
05:24
64 - Understanding Deep Learning with Theano
05:05
65 - Optimizing a Simple Model in Pure Theano
07:54
66 - Keras Behind the Scenes
05:25
67 - Fully Connected or Dense Layers
04:46
68 - Convolutional and Pooling Layers
06:40
69 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks
05:17
70 - Loading Pre-trained Models with Theano
05:16
71 - Reusing Pre-trained Models in New Applications
07:22
72 - Theano for Loops – the scan Module
05:19
73 - Recurrent Layers
06:28
74 - Recurrent Versus Convolutional Layers
03:43
75 - Recurrent Networks –Training a Sentiment Analysis Model for Text
06:51
76 - Bonus Challenge – Automatic Image Captioning
04:41
77 - Captioning TensorFlow – Google's Machine Learning Library
05:16
78 - The Course Overview
03:00
79 - Installing TensorFlow
05:34
80 - Simple Computations
05:32
81 - Logistic Regression Model Building
06:59
82 - Logistic Regression Training
04:54
83 - Basic Neural Nets
05:17
84 - Single Hidden Layer Model
05:06
85 - Single Hidden Layer Explained
04:33
86 - Multiple Hidden Layer Model
05:23
87 - Multiple Hidden Layer Results
04:43
88 - Convolutional Layer Motivation
05:04
89 - Convolutional Layer Application
06:56
90 - Pooling Layer Motivation
03:59
91 - Pooling Layer Application
04:18
92 - Deep CNN
06:29
93 - Deeper CNN
04:08
94 - Wrapping Up Deep CNN
04:56
95 - Introducing Recurrent Neural Networks
09:03
96 - skflow
09:20
97 - RNNs in skflow
04:05
98 - Research Evaluation
06:56
99 - The Future of TensorFlow
04:19

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