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Learning Path: The Road to Tensorflow

Video Description

Discover deep learning with Python and TensorFlow

In Detail

It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. If you have no prior exposure to one of the most important trends impacting how we do data science in the next few years, this path will help you get up to speed. It specifically focuses on getting you up and running with TensorFlow, after up-and-running coverage of Python and Deep Learning in Python with Theano.

Prerequisites: A firm understanding of Python and the Python ecosystem

Resources: Code downloads and errata:

  • Mastering Python

  • Data Mining with Python

  • Deep Learning with Python

  • Deep Learning with TensorFlow

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Mastering Python (2h 35m)

  • Data Mining with Python (2h 3m)

  • Deep Learning with Python (1h 45m)

  • Deep Learning with TensorFlow (2h 0m)

  • Photo Credit: ©iStockphoto.com/blackdovfx

    Table of Contents

    1. Chapter 1 : Mastering Python
      1. The Course Overview 00:03:42
      2. Downloading and Installing Python 00:03:03
      3. Using the Command Line and the Interactive Shell 00:03:30
      4. Installing Packages with pip 00:02:55
      5. Finding Packages in the Python Package Index 00:03:09
      6. Creating an Empty Package 00:04:10
      7. Adding Modules to the Package 00:04:37
      8. Importing One of the Package's Modules from Another 00:05:02
      9. Adding Data Files to the Package 00:02:33
      10. PEP 8 and Writing Readable Code 00:06:20
      11. Using Version Control 00:06:01
      12. Using venv to Create a Stable and Isolated Work Area 00:03:26
      13. Getting the Most Out of docstrings Part 1 – PEP 257 and Sphinx 00:06:24
      14. Getting the Most Out of docstrings Part 2 – doctest 00:02:52
      15. Making a Package Executable via python – m 00:04:15
      16. Handling Command-line Arguments with argparse 00:05:21
      17. Text-mode Interactivity 00:03:38
      18. Executing Other Programs 00:05:04
      19. Using Shell Scripts or Batch Files to Launch Programs 00:01:54
      20. Using concurrent.futures 00:09:57
      21. Using Multiprocessing 00:08:38
      22. Understanding Why Asynchronous I/O Isn't Like Parallel Processing 00:05:51
      23. Using the asyncio Event Loop and Coroutine Scheduler 00:05:26
      24. Futures 00:06:04
      25. Making Asynchronous Tasks Interoperate 00:05:51
      26. Communicating across the Network 00:03:16
      27. Using Function Decorators 00:05:02
      28. Using Function Annotations 00:04:55
      29. Using Class Decorators 00:04:28
      30. Using Metaclasses 00:04:56
      31. Using Context Managers 00:04:42
      32. Using Context Managers 00:05:37
      33. Understanding the Principles of Unit Testing 00:03:32
      34. Using unittest 00:05:37
      35. Using unittest.mock 00:05:39
      36. Using unittest's Test Discovery 00:03:56
      37. Using Nose for Unified Test Discovery and Reporting 00:03:59
    2. Chapter 2 : Data Mining with Python
      1. The Course Overview 00:03:55
      2. A Brief Introduction to Data Mining 00:04:37
      3. Data Mining Basic Concepts and Applications 00:07:06
      4. Why Python? 00:03:31
      5. Basics of Python 00:05:55
      6. Installing IPython 00:02:10
      7. Installing the Numpy Library 00:04:33
      8. Installing the pandas Library 00:05:32
      9. Installing Matplotlib 00:02:42
      10. Installing scikit-learn 00:02:37
      11. Data Cleaning 00:05:31
      12. Data Preprocessing Techniques 00:05:08
      13. Linear Regression Basic Model Approach 00:08:24
      14. Evaluating Regression Models 00:05:31
      15. Basic Regression Model Implementation to Predict House Prices 00:09:20
      16. Regression Model Implementation to Predict Television Show Viewers 00:09:46
      17. Logistic Regression 00:04:02
      18. K – Nearest Neighbors Classifier 00:05:51
      19. Support Vector Machine 00:05:42
      20. Logistic Regression Model Implementation 00:10:45
      21. K – Nearest Neighbor Classifier Implementation 00:10:44
    3. Chapter 3 : Deep Learning with Python
      1. The Course Overview 00:03:52
      2. What Is Deep Learning? 00:04:09
      3. Open Source Libraries for Deep Learning 00:04:31
      4. Deep Learning "Hello World!" Classifying the MNIST Data 00:07:57
      5. Introduction to Backpropagation 00:05:24
      6. Understanding Deep Learning with Theano 00:05:04
      7. Optimizing a Simple Model in Pure Theano 00:07:54
      8. Keras Behind the Scenes 00:05:24
      9. Fully Connected or Dense Layers 00:04:46
      10. Convolutional and Pooling Layers 00:06:40
      11. Large Scale Datasets, ImageNet, and Very Deep Neural Networks 00:05:17
      12. Loading Pre-trained Models with Theano 00:05:16
      13. Reusing Pre-trained Models in New Applications 00:07:22
      14. Theano "for" Loops – the "scan" Module 00:05:18
      15. Recurrent Layers 00:06:28
      16. Recurrent Versus Convolutional Layers 00:03:43
      17. Recurrent Networks –Training a Sentiment Analysis Model for Text 00:06:50
      18. Bonus Challenge – Automatic Image Captioning 00:04:41
      19. Captioning TensorFlow – Google's Machine Learning Library 00:05:15
    4. Chapter 4 : Deep Learning with TensorFlow
      1. The Course Overview 00:03:00
      2. Installing TensorFlow 00:05:34
      3. Simple Computations 00:05:32
      4. Logistic Regression Model Building 00:06:59
      5. Logistic Regression Training 00:04:53
      6. Basic Neural Nets 00:05:17
      7. Single Hidden Layer Model 00:05:06
      8. Single Hidden Layer Explained 00:04:33
      9. Multiple Hidden Layer Model 00:05:22
      10. Multiple Hidden Layer Results 00:04:43
      11. Convolutional Layer Motivation 00:05:04
      12. Convolutional Layer Application 00:06:56
      13. Pooling Layer Motivation 00:03:59
      14. Pooling Layer Application 00:04:18
      15. Deep CNN 00:06:29
      16. Deeper CNN 00:04:08
      17. Wrapping Up Deep CNN 00:04:56
      18. Introducing Recurrent Neural Networks 00:09:03
      19. skflow 00:09:19
      20. RNNs in skflow 00:04:04
      21. Research Evaluation 00:06:56
      22. The Future of TensorFlow 00:04:19