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Learning Path: TensorFlow: The Road to TensorFlow Second Edition

Video Introducing this tutorial

Chapter 1 : Mastering Python - Second Edition
The Course Overview 00:03:25
Python Basic Syntax and Block Structure 00:11:54
Built-in Data Structures and Comprehensions 00:08:55
First-Class Functions and Classes 00:05:50
Extensive Standard Library 00:05:56
New in Python 3.5 00:06:02
Downloading and Installing Python 00:05:17
Using the Command-Line and the Interactive Shell 00:04:01
Installing Packages with pip 00:03:16
Finding Packages in the Python Package Index 00:04:29
Creating an Empty Package 00:05:50
Adding Modules to the Package 00:05:31
Importing One of the Package's Modules from Another 00:05:26
Adding Static Data Files to the Package 00:02:53
PEP 8 and Writing Readable Code 00:07:51
Using Version Control 00:04:48
Using venv to Create a Stable and Isolated Work Area 00:04:41
Getting the Most Out of docstrings 1: PEP 257 and docutils 00:08:00
Getting the Most Out of docstrings 2: doctest 00:04:04
Making a Package Executable via python -m 00:05:52
Handling Command-Line Arguments with argparse 00:06:22
Interacting with the User 00:04:39
Executing Other Programs with Subprocess 00:09:10
Using Shell Scripts or Batch Files to Run Our Programs 00:03:01
Using concurrent.futures 00:13:53
Using Multiprocessing 00:11:22
Understanding Why This Isn't Like Parallel Processing 00:08:02
Using the asyncio Event Loop and Coroutine Scheduler 00:06:52
Waiting for Data to Become Available 00:03:30
Synchronizing Multiple Tasks 00:06:18
Communicating Across the Network 00:03:45
Using Function Decorators 00:06:45
Function Annotations 00:07:09
Class Decorators 00:05:53
Metaclasses 00:05:35
Context Managers 00:05:52
Descriptors 00:05:38
Understanding the Principles of Unit Testing 00:05:07
Using the unittest Package 00:07:28
Using unittest.mock 00:06:12
Using unittest's Test Discovery 00:04:30
Using Nose for Unified Test Discover and Reporting 00:03:42
What Does Reactive Programming Mean? 00:02:50
Building a Simple Reactive Programming Framework 00:07:22
Using the Reactive Extensions for Python (RxPY) 00:10:22
Microservices and the Advantages of Process Isolation 00:04:13
Building a High-Level Microservice with Flask 00:09:59
Building a Low-Level Microservice with nameko 00:06:25
Advantages and Disadvantages of Compiled Code 00:04:42
Accessing a Dynamic Library Using ctypes 00:07:59
Interfacing with C Code Using Cython 00:12:35


Chapter 2 : Deep Learning with Python
The Course Overview 00:03:52
What Is Deep Learning? 00:04:09
Open Source Libraries for Deep Learning 00:04:31
Deep Learning "Hello World!" Classifying the MNIST Data 00:07:57
Introduction to Backpropagation 00:05:24
Understanding Deep Learning with Theano 00:05:04
Optimizing a Simple Model in Pure Theano 00:07:54
Keras Behind the Scenes 00:05:24
Fully Connected or Dense Layers 00:04:46
Convolutional and Pooling Layers 00:06:40
Large Scale Datasets, ImageNet, and Very Deep Neural Networks 00:05:17
Loading Pre-trained Models with Theano 00:05:16
Reusing Pre-trained Models in New Applications 00:07:22
Theano "for" Loops – the "scan" Module 00:05:18
Recurrent Layers 00:06:28
Recurrent Versus Convolutional Layers 00:03:43
Recurrent Networks –Training a Sentiment Analysis Model for Text 00:06:50
Bonus Challenge – Automatic Image Captioning 00:04:41
Captioning TensorFlow – Google's Machine Learning Library 00:05:15


Chapter 3 : Deep Learning with TensorFlow
The Course Overview 00:03:00
Installing TensorFlow 00:05:34
Simple Computations 00:05:32
Logistic Regression Model Building 00:06:59
Logistic Regression Training 00:04:53
Basic Neural Nets 00:05:17
Single Hidden Layer Model 00:05:06
Single Hidden Layer Explained 00:04:33
Multiple Hidden Layer Model 00:05:22
Multiple Hidden Layer Results 00:04:43
Convolutional Layer Motivation 00:05:04
Convolutional Layer Application 00:06:56
Pooling Layer Motivation 00:03:59
Pooling Layer Application 00:04:18
Deep CNN 00:06:29
Deeper CNN 00:04:08
Wrapping Up Deep CNN 00:04:56
Introducing Recurrent Neural Networks 00:09:03
skflow 00:09:19
RNNs in skflow 00:04:04
Research Evaluation 00:06:56
The Future of TensorFlow 00:04:19


Chapter 4 : Machine Learning with TensorFlow
The Course Overview 00:03:48
Introducing Deep Learning 00:03:59
Installing TensorFlow on Mac OSX 00:03:51
Installation on Windows – Pre-Reqeusite Virtual Machine Setup 00:02:49
Installation on Windows/Linux 00:04:01
The Hand-Written Letters Dataset 00:03:01
Automating Data Preparation 00:03:20
Understanding Matrix Conversions 00:05:34
The Machine Learning Life Cycle 00:01:52
Reviewing Outputs and Results 00:02:51
Getting Started with TensorBoard 00:05:09
TensorBoard Events and Histograms 00:05:22
The Graph Explorer 00:05:09
Our Previous Project on TensorBoard 00:05:02
Fully Connected Neural Networks 00:04:44
Convolutional Neural Networks 00:04:59
Programming a CNN 00:05:02
Using TensorBoard on Our CNN 00:01:58
CNN Versus Fully Connected Network Performance 00:02:08

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