anything and everything from machine learning, data sceince, projects & progress, SolvePao , research findings ,system design and maybe food :P ? .....
Getting Started with #100daysofML Day 1 , in journey of learning ML/DL one encounter with images sooner or later and learning to play around with them in python is necessary , for the series of post i wil be using using a mix of google colab , Jupyter Notebook on Local Instance Github gists for code snippets and NBviewer for viewing the notebooks saved in my github repo for online view , will be skipping the basics majority of times as there are plenty of better explnation online about them and will be focsing more on the "Geeting Hands Dirty " and implementation side
Day 1 is not machine learning and more of warm up in python that will help us later
Prerequisites will be Python , and Running code in Jupyter Notebook, although one can easily setup and run these codes locally on your notebook but my prefernce will be to get more comfortable with Google Colab or any other cloud instance so that as one proceed you can run more GPU and CPU intensive tasks on powerful server provided you for free in learning stage rather than setting you Machine on Fire [unless you have a really good spec machine]
As the world Undergoes the Covid-19 lockdown everything is going at slow pace be at Academics , or Internship procedure in University , while my college figure out the plans on how to carry out the exams and continue with the remaining semester while the company I am going to join as Interm reschedule the timeline in match with the college calender , its been over the month i came back home in end of march and with the few lectures going on microsoft teams there was plenty of time to get on things i didnt get time for from cooking , to hardware teardown & repair , plenty of sleep , clean formating and upgrading Ubuntu 18 to 20 LTS while seting up windows 10 in dual boot this time .
To Help with students stuck at home due to the pandemic many educational websites have provided with free access to student on their otherwise paid courses which helped me keep up with learning pace and accelerated my journey to collect the infinity stones i need for snapping my project into existence
Made this Image a year back for a ppt , when Endgame had released
So I signed up on Plurasight (for Web Develeopment and C++ 14,17 course), Progate (to get stared with JS beginner), Codeacademy (complete JS and Node.js) , and my college Coursera acces for Python and ML) , with Leetcode 30 days challenge throught april , built my Notes repo Notex using github pages , CI/CD build and Mkdocs to collect all those precious code in a searchable portal
With no major submission in University until June for now and to actually get hands dirty with things as I learn them will start with #100DaysofMachineLearning , #100DaysofCode , with a daily progress update and notes on my learning and ProjectX update Starting May-1 lets see how long the Streak continues :)
Utilising the Enterprise Coursera Premium Subscripition provided by the University and to brush up my Machine learning and other skills required as i build my project X for now will use Couersera content , to understand the dynamics of AI teams in organisations and to gain a broader view of the AI in Industry beyond the DIY and small learning projects we do as students I completed this excellent course taught by Andrew Ng and with the tips and tricks picked from Siraj Raval , like watching the videos at 2x and greater now i can easily watch videos at 2.20x and even greater sometimes and completed the entire course in less than week
Things taught in course were :
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
As i went through the course i made my E-notes [Screenshot of the slides] so that in case i need to look it up later or use same example to explain the concept to someone else can easily look back to
Next up Python Course + Python for Webscraping and Visualisation for the Project Requirements , then later ML and DL ones , shifted my harshityadav95.blogspot.com to blog.harshityadav.in , will post updates here but will keep complete techincal ones on mediun [but wont post it behind the metered Medium wall,technical posts and learing should be free to all]
" Failure is an option here if you are not failing,you are not Innovating enough"- Elon Musk
Step 1 : Learning to Learn Faster
10 Ways to Learn Anything Faster
Takeaway :
1) Have a will power and mental confidence that you can learn it.
2) Create your own curriculum (using reference of some University and other College Course), then write your own path you will be following to achieve it
3) 2 hours a day is Minimum , and make Learning Goals which requires Implementation
4) Instead of following the Quotes and Motivation always know why you are learning this and your end goal
5) Learn to meditate and Meditate at least 5-10 min in the Morning
6) Do Cardiovascular exercise (something like ecstatic dance) in morning and after learning something new for better memory retention
7) Use the Pomodore technique
8) Take small breaks after a session
9) Use Multiple split windows and Tabs as you learn and watch Videos
10) If you don't understand anything find ELI5 (explain like i am 5 year Old on Internet)
11) Use Chrome Extension call Spreed for speed reading text in the browser
12) Watch the Videos at 2X ,(it will be harder at first and in some videos but your brain muscles will get adapted to it over time)
13 Make some Handwritten Notes even when you are typing or when your area of work doesn't require writing with hand .
14) Learn by doing , Trying
15) When Trying , the Application doesn't need to be a defined problem set and you can use you own case
16) Use you Knowledge as a tool and educate others (Teaching others) or teach yourself by speaking out loud.
17) Find a Study Group or a study buddy or make some self study deck and slides
18) Eat well , you are what you eat
19) Sleep Well ,Use Blue light filter before going to sleep
" There is a Smaller Number of People those who can do Math , there is even a smaller number who can explain it , but those who do both they become Billionaire "
Learning Artificial Intelligence and Machine Learning has been on my To-do-List since end of 2017 and I started it a little back then playing around demo projects and following up step by step DIY posts on Machine Learning for Beginner but that time Block chain got my most of the spare time ,since then I didn't give time to advance skill-set on it so fast forward to few weeks backs when I was looking around for how to begin with Machine Learning and among all the online courses to follow up and came across the Udacity - Secure and Private Ai Challenge applied for it and this came in Mail Today 😀🌟
the course dives fast into deep learning and with less prior knowledge and hands with machine learning there will be a lot of knowledge gap for me to fill in so will be making a doc trail of my progress from scratch in the Wiki Section of the this Github Repo , Really excited about it and lets see what area of ML draws me the most .
