freeCodeCamp.org has made the Practical Deep Learning for Coders is a course from fast.ai available .

This course was created to make deep learning accessible to as many people as possible. The only prerequisite for this course is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course.

This course was developed by Jeremy Howard and Sylvain Gugger. Jeremy has been using and teaching machine learning for around 30 years. He is the former president of Kaggle, the world’s largest machine learning community. Sylvain Gugger is a researcher who has written 10 math textbooks.

In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:40 Obtain the Data
  • 01:30 Organize the Data
  • 09:42 Process the Data
  • 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND

Learn all about Data Structures in this lecture-style course from freeCodeCamp.org

You will learn what Data Structures are, how we measure a Data Structures efficiency, and then hop into talking about 12 of the most common Data Structures which will come up throughout your Computer Science journey.

Course Contents

  • (00:00) Introduction
  • (01:06) Timestamps
  • (01:23) Script and Visuals
  • (01:34) References + Research
  • (01:56) Questions
  • (02:12) Shameless Plug
  • (02:51) What are Data Structures?
  • (04:36) Series Overview
  • (06:55) Measuring Efficiency with BigO Notation
  • (09:45) Time Complexity Equations
  • (11:13) The Meaning of BigO
  • (12:42) Why BigO?
  • (13:18) Quick Recap
  • (14:27) Types of Time Complexity Equations
  • (19:42) Final Note on Time Complexity Equations
  • (20:21) The Array
  • (20:58) Array Basics
  • (22:09) Array Names
  • (22:59) Parallel Arrays
  • (23:59) Array Types
  • (24:30) Array Size
  • (25:45) Creating Arrays
  • (26:11) Populate-First Arrays
  • (28:09) Populate-Later Arrays
  • (30:22) Numerical Indexes
  • (31:57) Replacing information in an Array
  • (32:42) 2-Dimensional Arrays
  • (35:01) Arrays as a Data Structure
  • (42:21) Pros and Cons
  • (43:33) The ArrayList
  • (44:42) Structure of the ArrayList
  • (45:19) Initializing an ArrayList
  • (47:34) ArrayList Functionality
  • (49:30) ArrayList Methods
  • (50:26) Add Method
  • (53:57) Remove Method
  • (55:33) Get Method
  • (55:59) Set Method
  • (56:57) Clear Method
  • (57:30) toArray Method
  • (59:00) ArrayList as a Data Structure
  • (1:03:12) Comparing and Contrasting with Arrays
  • (1:05:02) The Stack
  • (1:05:06) The Different types of Data Structures
  • (1:05:51) Random Access Data Structures
  • (1:06:10) Sequential Access Data Structures
  • (1:07:36) Stack Basics
  • (1:09:01) Common Stack Methods
  • (1:09:45) Push Method
  • (1:10:32) Pop Method
  • (1:11:46) Peek Method
  • (1:12:27) Contains Method
  • (1:13:23) Time Complexity Equations
  • (1:15:28) Uses for Stacks
  • (1:18:01) The Queue
  • (1:18:51) Queue Basics
  • (1:20:44) Common Queue Methods
  • (1:21:13) Enqueue Method
  • (1:22:20) Dequeue Method
  • (1:23:08) Peek Method
  • (1:24:15) Contains Method
  • (1:25:05) Time Complexity Equations
  • (1:27:05) Common Queue Uses
  • (1:28:16) The Linked List
  • (1:31:37) LinkedList Visualization
  • (1:33:55) Adding and Removing Information
  • (1:41:28) Time Complexity Equations
  • (1:44:26) Uses for LinkedLists
  • (1:47:19) The Doubly-LinkedList
  • (1:48:44) Visualization
  • (1:50:56) Adding and Removing Information
  • (1:58:30) Time Complexity Equations
  • (1:59:06) Uses of a Doubly-LinkedList
  • (2:00:21) The Dictionary
  • (2:01:15) Dictionary Basics
  • (2:02:00) Indexing Dictionaries
  • (2:02:40) Dictionary Properties
  • (2:05:53) Hash Table Mini-Lesson
  • (2:13:26) Time Complexity Equations
  • (2:16:39) Trees
  • (2:16:55) Introduction to Hierarchical Data
  • (2:18:54) Formal Background on the Tree
  • (2:20:03) Tree Terminology and Visualization
  • (2:25:08) Different types of Trees
  • (2:28:07) Uses for the Tree
  • (2:29:00) Tries
  • (2:29:50) Trie Basics
  • (2:30:41) Trie Visualization
  • (2:34:33) Flagging
  • (2:35:15) Uses for Tries
  • (2:38:25) Heaps
  • (2:38:51) Heap Basics
  • (2:39:19) Min-Heaps
  • (2:40:07) Max-Heaps
  • (2:40:59) Building Heaps
  • (2:44:20) Deleting from Heaps
  • (2:46:00) Heap Implementations
  • (2:48:15) Graphs
  • (2:49:25) Graph Basics
  • (2:52:04) Directed vs. Undirected Graphs
  • (2:53:45) Cyclic vs. Acyclic Graphs
  • (2:55:04) Weighted Graphs
  • (2:55:46) Types of Graphs
  • (2:58:20) Conclusion
  • (2:58:43) Shameless Plug

Murtaza’s Workshop – Robotics and AI posted this video to explains how to perform Facial recognition with high accuracy.

We will first briefly go through the theory and learn the basic implementation. Then we will create an Attendance project that will use webcam to detect faces and record the attendance live in an excel sheet.

Link to the Article:
https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78

deeplizard  introduces MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:17 Intro to MobileNets
  • 02:56 Accessing MobileNet with Keras
  • 07:25 Getting Predictions from MobileNet
  • 13:32 Collective Intelligence and the DEEPLIZARD HIVEMIND