So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.

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Today Hank explores artificial intelligence, including weak AI and strong AI, and the various ways that thinkers have tried to define strong AI including the Turing Test, and John Searle’s response to the Turing Test, the Chinese Room. Hank also tries to figure out one of the more personally daunting questions yet: is his brother John a robot?

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Watch this Python tutorial to learn Python programming for machine learning and web development.
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TABLE OF CONTENT

00:00:00 Introduction
00:01:49 Installing Python 3
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django

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This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
Who is this class for: This class is for people who would like to learn more about machine learning techniques, but don’t currently have the fundamental mathematics in place to go into much detail. This course will include some exercises that require you to work with code. If you've not had much experience with code before DON'T PANIC, we will give you lots of guidance as you go.
Topic Covered:
Fuctions
Definition of a derivative
Differentiation examples & special cases
differentiate some functions
Time saving rules
Product rule
Chain rule
Variables, constants & context
Differentiate with respect to anything
Jacobians - vectors of derivatives
Jacobian applied
The Sandpit
The Sandpit -2
The Hessian
Multivariate chain rule
Neural Networks
Simple neural networks
Power series
Visualising Taylor Series
Power series derivation
Power series details
Multivariable Taylor Series
Linearisation
Multivariate Taylor
Gradient Descent
Gradient descent in a sandpit
Lagrange multipliers
Constrained optimisation
Simple linear regression
Non-linear regression
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This video is provided here for research and educational purposes in the field of Mathematics. No copyright infringement intended. If you are content owner would like to remove this video from YouTube, Please contact me through email: ict_hanif@yahoo.com
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