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Learning Path : Diving into Deep Learning

Video Description

A comprehensive introduction to Deep Learning with Python

In Detail

Get ready to train your machine learning algorithms. Starting off with core Python coverage and swiftly moving to practical deep learning content, this Learning Path will have you crunching stats and quoting facts in no time at all.

Prerequisites: An introductory knowledge of Python

Resources: Code downloads and errata:

  • Beginning Python

  • Mastering Python

  • Deep Learning with Python

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

  • Beginning Python (4h 20m)

  • Mastering Python (2h 35m)

  • Deep Learning with Python (1h 45m)

  • Photo Credit: ©iStockphoto.com/Maxiphoto

    Table of Contents

    1. Chapter 1 : Beginning Python
      1. The Course Overview and Installing Python 00:06:15
      2. Setting Up a Programming Environment 00:06:46
      3. Variables 00:05:29
      4. Introduction to Types 00:06:04
      5. Basic Operators 00:05:53
      6. Introduction to Strings 00:06:25
      7. String Functions 00:06:42
      8. Advanced String Manipulation 00:05:23
      9. String Formatting 00:08:18
      10. User Input 00:05:18
      11. Introduction to Lists 00:06:24
      12. List Methods 00:04:51
      13. Advanced List Methods 00:05:41
      14. Built-in List Functions 00:04:25
      15. 2D Arrays and Array References 00:07:45
      16. List Slicing 00:05:46
      17. Control Flow 00:05:46
      18. Comparison Operators 00:04:44
      19. Else and Elif 00:06:44
      20. and, or, and not 00:06:09
      21. Conditional Examples 00:05:22
      22. Mini Program 00:07:45
      23. For Loop 00:07:25
      24. While Loop 00:06:44
      25. Iterables 00:04:52
      26. Loops and Conditionals 00:05:00
      27. Prime Number Checker 00:06:55
      28. Function Basics 00:05:29
      29. Parameters and Arguments 00:06:57
      30. Return versus Void Functions 00:03:34
      31. Working with Examples 00:08:30
      32. Advanced Examples 00:06:46
      33. Recursion 00:04:26
      34. Recursion Examples 00:09:11
      35. Import, as, and from 00:04:43
      36. Python API and Modules 00:06:48
      37. Creating Modules 00:04:59
      38. Modules and Testing 00:05:28
      39. Installing PIL/Pillow 00:06:29
      40. Basics of Using PIL/Pillow 00:06:25
      41. Picture Manipulations 00:06:30
      42. Custom Picture Manipulation 00:06:20
      43. Wrapping Up 00:03:03
    2. Chapter 2 : 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
    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