English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 85 lectures (14h 27m) | 4.13 GB
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers
Common scenario: You try to get into machine learning and data science, but there’s SO MUCH MATH.
Either you never studied this math, or you studied it so long ago you’ve forgotten it all.
What do you do?
Well my friends, that is why I created this course.
Calculus is one of the most important math prerequisites for machine learning. It’s required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.
If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.
Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.
Luckily, I’ve refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.
This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn’t normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don’t worry, you don’t need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.
What you’ll learn
- Limits, limit definition of derivative, derivatives from first principles
- Derivative rules (chain rule, product rule, quotient rule, implicit differentiation)
- Integration, area under curve, fundamental theorem of calculus
- Vector calculus, partial derivatives, gradient, Jacobian, Hessian, steepest ascent
- Optimize (maximize or minimize) a function
- l’Hopital’s Rule
- Newton’s Method
Table of Contents
Introduction and Outline
1 Introduction
2 Outline
3 How to Succeed in this Course
4 Where to Get the Code
Review
5 Functions Review
6 Functions Review in Python
Limits
7 What Are Limits
8 Precise Definition of Limit (Optional)
9 Limit Laws
10 Infinities and Asymptotes
11 Indeterminate Forms
12 Limits in Python
13 Limits with Plotting in Python
14 Limits Section Summary
Derivatives From First Principles
15 Slopes, Tangent Lines, and Derivatives
16 More On Tangent Lines, Derivative Checking
17 Exercise Quadratic
18 Exercise Cubic
19 Exercise Reciprocal
20 Exercise Root
21 Alternate Notations & Higher Order Derivatives
22 Derivative Checking in Python
23 Derivatives Section Summary
Derivative Rules
24 Power Rule
25 Constant Multiple, Addition, Subtraction Rules
26 Exponent Rule
27 Exponent Rule (continued)
28 Chain Rule
29 Exercises Chain Rule
30 Product and Quotient Rules
31 Exercises Product and Quotient Rules
32 Implicit Differentiation
33 Logarithm Rule
34 Implicit Differentiation Applications
35 Logarithmic Differentiation
36 Exercise Derivatives of Hyperbolic Functions
37 Exercise Sum of Polynomials
38 Exercise Gaussian Variance
39 Exercise Entropy
40 Trigonometric Functions (Optional)
41 Inverse Trigonometric Functions (Optional)
42 Derivative Rules Section Summary
Applications of Differentiation
43 Finding the Minimum Maximum
44 Minimum Maximum Clarifications and Examples
45 Second Derivative Test
46 Exercise Minimums and Maximums
47 Exercise Entropy
48 Exercise Gaussian 1
49 Exercise Gaussian 2
50 l’Hopital’s Rule
51 Newton’s Method
52 Newton’s Method in Python
53 Applications Section Summary
Integration (Calculus 2)
54 Integrals Section Introduction
55 Area Under Curve
56 Fundamental Theorem of Calculus (pt 1)
57 Fundamental Theorem of Calculus (pt 2)
58 Definite and Indefinite Integrals
59 Exercises Definite Integrals
60 Exercises Indefinite Integrals
61 Exercises Improper Integrals
62 Numerical Integration in Python
63 Integration Section Summary
Vector Calculus in Multiple Dimensions (Calculus 3)
64 Functions of Multiple Variables
65 Partial Differentiation
66 The Gradient
67 The Jacobian and Hessian
68 Differentials and Chain Rule in Multiple Dimensions
69 Why is the Gradient the Direction of Steepest Ascent
70 Steepest Ascent in Python
71 Optimization and Lagrange Multipliers (pt 1)
72 Optimization and Lagrange Multipliers (pt 2)
73 Vector Calculus Section Summary
Setting Up Your Environment (AppendixFAQ by Student Request)
74 Pre-Installation Check
75 Anaconda Environment Setup
76 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
77 Where To Get the Code Troubleshooting
78 How to use Github & Extra Coding Tips (Optional)
Effective Learning Strategies (AppendixFAQ by Student Request)
79 Math Order for Machine Learning & Data Science
80 Can YouTube Teach Me Calculus (Optional)
81 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
82 What order should I take your courses in (part 1)
83 What order should I take your courses in (part 2)
Appendix FAQ Finale
84 What is the Appendix
85 BONUS
Resolve the captcha to access the links!