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Mastering Calculus – Mathematics for Deep learning / Machine learning / Data Science / Data Analysis / AI – Hands On
Do you want to be better data Scientist ?
Are you looking for way to stand out in the crowd?
Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills ?
If the Answer is Yes.
Then, this course is for you.
Calculus for Deep learning
“Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python ”
With this course,
You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning, Deep Learning , Artificial intelligence, Data Science Application.
Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background.
Whether you are building Self driving cars, or building the recommendation engine for Netflix, or trying to fit the practice data for a function
Your data,
Will have some type of labelled input and , some type of labelled output.
A typical goal would always be fit these data to the function by adjusting the parameters.
Hence in our course,
We start from understanding the basics of functions which you might have touched upon in highschool.
And then,
In further sections, we move along and apply the basics and learn some of the important concepts related to approximation which is the core for any Machine learning, Deep Learning , Artificial intelligence, Data Science model
And, in the last two sections of this course,
We make use of all our learning from previous sections, and train our Neural networks and understand how we apply in Linear Regression models by writing the code from scratch.
We are sure that you will be amazed how well you can perform in your work once you have the intuition of calculus.
This course is carefully designed by experts with student’s feedback so that you can have the premium learning experience.
Join now to build confidence in Mathematics part of Machine learning, Deep Learning , Artificial intelligence, Data Science and stay ahead in your career.
What you’ll learn
- Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning
- The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner
- How to take their Data Science / Machine Learning / Deep learning career to the next level
- Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career
- Implement Machine Learning / Deep learning Algorithms better
- Learn core concept to Implement in Machine Learning / Deep learning
Table of Contents
Introduction
1 Understanding the Function
2 Calculus Basics
3 Finding a Derivative
4 Derivatives using Delta Method
5 Product Rule for Differentiation
6 Chain Rule
7 Applying all the basics
8 End of Section 1
9 Exercise – 2
10 Exercise – 3
11 Exercise – 4
12 Exercise 1 – Finding the Derivative
Multi Variate Calculus
13 Multi Variate Calculus
14 Differentiate With respect to anything
15 Jacobians
16 Hessian
17 Exercise – 5
18 Exercise – 6
19 Exercise – 7
20 Exercise – 8
Chain Rule on Multi-Variate Functions
21 Chain Rule on Multi Variate
22 Chain Rule on Multi Variate – more functions
Taylor Series of Approximations
23 Taylor Series of Approximation
24 Concept of Approximation
25 Taylor Series – Intuition
26 Taylor Series Detailed
27 Taylor Series Derivation
28 Taylor Series Derivation Part 2
29 Taylor Series – More
Neural Networks
30 Neural Networks – Intro
31 Bias in Neural Networks
32 Neural Networks Part 2
33 Calculus in Action – Neural Networks
34 Intuition of Sigmoid Function
35 Manual Fitting of Data
36 Loss Function
37 How to Update Parameters
38 Compute Partial Derivative
39 Exercise to compute Partial derivative of parameter – bias
40 Program overview
41 Program in Python
Optimization Methods – Newton Raphson & Gradient Descent
42 Newton Raphson Method
43 Newton Raphson Method in Python
44 Gradient Descent
Linear Regression
45 Linear Regression
46 Linear Regression in Python
47 Evaluation of Model – RMSE and R2 Score
48 Implementation using Scikit Library
Calculus for Deep Learning
49 Calculus in Deep Neural Networks
50 Calculus Update – Sigmoid Neuron
51 Fit & Accuracy
52 Deep Neural Network Update Parameters
53 Deep Neural Network
54 Perform Fit Deep Neural Networks
55 Jupyter Notebook of the Section
Working with Tensorflow
56 Install Tensorflow
57 Tensor Object – Constant
58 Tensor Object – Variables
59 Tensor Object – Shape,Rank & Type Casting
60 Mathematical Operation & Broadcasting
61 Matmul – Transpose – Reshaping
62 Concat – Stack – Slice – Reduce
63 Jupyter Notebook of the Section
Finding the Derivative using Tensorflow – AutoGrad
64 Finding the Derivative Mathematically
65 Intro to Gradients
66 AutoGrad Part 1
67 AutoGrad Part 2
68 Download the Jupyter Notebook of the Section
Linear Regression with Deep learning
69 Linear Regression from Scratch
70 Linear Regression Fit on Data
71 Download the Jupyter Notebook of the Section
Linear Regression using Keras
72 Linear Regression Using Keras API
73 Load Data in Batches
74 Download the Jupyter Notebook of the Section
Deep learning Tasks
75 Multi Class Classification – Creating the Model
76 Multi Class Classification – Perform Fit
77 Regression
78 Regression Part 2
79 Download the Jupyter Notebook of the Section
Solution for Exercise
80 Exercise 1 – Solution
81 Exercise 2 – Solution
82 Exercise 3 – Solution
83 Exercise 4 – Solution
84 Exercise 5 – Solution
85 Exercise 6 – Solution
86 Exercise 7 – Solution
87 Exercise 8 – Solution
Python for Data Science – Refresh the Basics
88 Source code download
89 Installing & Using Jupyter Notebook
90 Google Colab
91 Basic Data Types
92 Python Basics – Containers in Python
93 Control Statements Python if..else
94 Control Statements While & For
95 Functions & Classes in Python
Python for Data Science
96 Source code download
97 Python Numpy Basics
98 Python Numpy Basics Contd
99 Python Numpy
100 Pandas in Python – Pandas Series
101 Pandas DataFrame
102 Pandas – Dealing with Missing Values
103 Matplotlib
104 Matplotlib – Density and Contour Plot
105 Bonus Lecture Never Stop Learning
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