I know I am starting this blog from day 17 and not from the day 1 cuz hey its better late than never 😁😁

Why?

Before going into my first blog, I want to give you some context. I am currently learning deep learning and simultaneously I am also doing this "180 days of code" challenge, where I am actually learning C++ from scratch and trying to solve DSA problems. Along with this I am also learning some math mostly, deep learning or finance-related. So every day I have three responsibilities:

• Daily task 1: Deep learning theory

• Daily task 2: 180 days of code (i.e C++ or DSA related)

• Daily task 3: Math topic

I will try to write this blog every day (if not every day, most days), so that I can keep myself accountable and to journey my progress. In between I will try to write some technical blogs on deep learning, math, etc. So subscribe for updates 😁

Daily task 1: Deep learning

I could not do anything from this task today, as I was busy with some other tasks. I do not have any progress to report so I will just give you guys some resources:

• Michigan university computer vision course

  This is similar to cs231n as both courses are taught by the same instructor.

  I try to just go through the course materials because whatever is taught in the video lecture, you could find everything in the course materials (also saves time).

Daily task 2: 180 days of code

Today I actually solved my first leetcode problem and in total solved 7 leetcode problems like palindrome, leap year problem, and bitwise complement of a given number.

All of these were fairly doable, but only the bitwise complement problem is unique, where i used the Xor operator (^). Here is the code for the bitwise complement :

class Solution {
public:
    int bitwiseComplement(int n) {

        if(n==0)
        return 1;

        int ans = 0, rem, mul = 1;

        while(n)
        {
            rem = n % 2;
            rem = rem ^ 1;   // XOR operation

            n = n/2;

            ans = ans + rem*mul;
            mul = mul*2;

        }

        return ans;

    }
};

Daily task 3: Math

I am currently learning probability and statistics. of course, this is used in deep learning and machine learning, but also used in quant finance, etc where learning this from a sufficiently pure mathematics perspective is useful. I am using this free online book/ website called " Introduction to probability, statistics and Random Processes", the link is given here:

Introduction to probability, statistics and random processes

I completed upto sections 1.3.4 Discrete models. I could have completed the whole first chapter but got lazy and was not able to do. For people who already are ok with mathematics can complete this easily. Some of the important points that I learned from these topics:

• Probability measure is always assigned to a set (event) and not to individual outcomes. so if we write P(1), it actually means P({1}).

• 3 axioms of probability, where the third one states for disjoint events, the probability of their union is just the summation of probability of individual events.

• Inclusion-Exclusion principle: P(A∪B)=P(A)+P(B)−P(A∩B). Can be extende for any "n"

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So this is how my day went and along with daily progress, I want to have weekly targets too

Week 1:

• Complete prob as much as possible, maybe upto chp-4 in the book.

• Learn pytorch. basically, I am bad with OOP in Python, so its taking time.

• Learn upto lecture 7 from the Deep learning playlist.

So lets see how much will i progress by the end of the week. I will try to write one blog just about these concepts, because there is a need for the math related blogs for deep learning, etc. Everyone are posting practical (code-related) stuff and i feel math is the missing link. so I will post blogs on the math that I learnt.

So subscibe !!!!!!!!!!