Wow I've been with you from very early days of your channel, it's amazing to see how much you have grown and how well you have marketed yourself. Well done, thank you for all the content and the videos this is amazing.

@mrdbourke

2 жыл бұрын

@DerSuperDoiiink

2 жыл бұрын

@danielbourke and @ahmadbazzi are really my best channels

I already used gradio thanks to you. Very nice option to test our models. Keep it coming Daniel!

What a great source of useful information! This is so rare at KZread that I feel one like isn't enough :) Please continue the great job you're doing!

This is my favourite resource for what to read/learn next, thanks Daniel!

Your an absolute inspiration for me! I’ve been streaming 100 days of ML to record my progress :)

@mrdbourke

2 жыл бұрын

All the best Harshith! Keep learning my friend

As always… remarkable content 👍🏻 congratulations sr

@mrdbourke

2 жыл бұрын

Thank you Vlademir!

I'm back for my monthly dance ritual...oh and ML Monthly too :D

@mrdbourke

2 жыл бұрын

🕺+☕️

I still remember starting your Machine Learning & Data Science bootcamp on Udemy when it was first released. You're one of my biggest role models when it comes to data science.

Hi Daniel! Thanks for sharing all this stuff, Skytowner is gonna be my good friend in daily-basis tasks and experiments.

@mrdbourke

2 жыл бұрын

Thank you! Enjoy!

Hi, daniel, nice to see this, it is very useful, interesting and inspiring, as always. Keep going on, bro.

It's been so many days hell yeah!!

Great, thanks for this. Hugging face is incredible. What took hours of code, can be done in a few lines.

Its that time of the month again !!

@mrdbourke

2 жыл бұрын

Welcome back Gaurav!

Here I was procrastinating working on my portfolio website to show my work and you just told about Gradio...so yep, got the inspiration, thanks😂

@mrdbourke

2 жыл бұрын

Hahaha perfect timing! Enjoy my friend!

Yes Please do some Pytorch related material!

100K soon !!!!!! :)

@mrdbourke

2 жыл бұрын

soon!!!! I need to do another livestream QA

When starting in mechanical engineering, you have a pretty well-defined learning path: Physics (mechanics, specifically) > Statics > Dynamics > Strength of Materials > Machine Design Physics > Statics > Fluid Mechanics > Thermodynamics > Heat and Mass Transfer Physics > Statics > Fundamental Circuits > Dynamics > Instrumentation (sensors) > Vibrations > Control Theory Each path above could get you into a job, but you need all paths to get any of 80% of entry mechanical engineering jobs. But they're all essentially textbooks and small projects. What is the equivalent pathing for SWE and ML (via textbooks and small projects/exercises)? Is it something like: Python Basics > Intro to Operating Systems > Basic Linux > Git + Github > Statistics > Databases > TensorFlow? I am having a hard time figuring out what books to read, what order, when to attempt projects, what the learning+workflow is like, etc.

@billykotsos4642

2 жыл бұрын

You don’t need to know anything about operating systems to be an ML engineer

@connorskudlarek8598

2 жыл бұрын

@@billykotsos4642 that's good to know, but also not really helpful, haha. My question was more about "what is the path," rather than "what in this fake path isn't part of the path?" Unless you mean the path I laid out is all correct except OS?

@mrdbourke

2 жыл бұрын

You're path looks good to me. ML Engineering is quite a broad field. ML is mostly an infrastructure problem. The math behind the scenes is 1st/2nd year calculus & linear algebra so most of the effort gets dedicated to moving, shifting and modelling data around the place. I'd look here for more: fullstackdeeplearning.com/ As for projects etc, I'm an advocate for starting as soon as possible: www.mrdbourke.com/how-can-a-beginner-data-scientist-like-me-gain-experience/

Love the dance

@mrdbourke

2 жыл бұрын

🕺 thank you!

Your a complete inspiration for me !💯💯 I have a doubt, that "Is 'AI and datascience 'a field worth doing Btech in" ?? (Answer if anyone knows)

@mrdbourke

2 жыл бұрын

Thank you legend! AI and data science can be useful for almost any field, however, as for speaking for Btech, I’ve never been in that field so I’m not the best to answer

please make a tutorial on pytorch!

hey daniel if possible can you please publish a book on machine learning like tensorflow for deep learning. Cause i have completed the machine learning course,tensorflow couse on udemy when i got stuck it is taking time to explore the notebooks. So if u make a book on machine learning like tensorflow book it will be very helpful.

Go ahead for making pytorch tutorials.

Help me Daniel, I am absolutely lost! I don't know what should I do. I know the basics of ML and also just started with Andrew Ng's deep learning specialization in Deep Learning. I need a proper roadmap to go ahead. The biggest problem is I don't stick to something for enough time. Please help.

@mrdbourke

2 жыл бұрын

read this article: www.mrdbourke.com/how-can-a-beginner-data-scientist-like-me-gain-experience/

Daniel we need to be good at math as Ml developer please answer for my question

@byrospyro4432

2 жыл бұрын

Start with probability ( Conditional Basic Marginal etc …) Mathematical Series and Convergence , Numerical methods for Analysis Matrix and Linear Algebra Bayesian Statistics Vectors ( Most Important) Calculus Markov Process and Chains Basics of Optimization ( Linear/ Quadratic) Advanced Matrix Algebras and Calculus ( Gradient , Divergence , Curls etc) This much mathematics will enable the understanding behind the core ideas of ML and probabilistic algorithms,

@mageshyt2550

2 жыл бұрын

@@byrospyro4432 thank you so much 😁

@byrospyro4432

2 жыл бұрын

@@mageshyt2550 No problem, when you are done with that you can also do this to master it: Stochastic Models and Time Series Analysis Differential Equations Dynamic Programming and Optimization Techniques Fourier's and Wavelengths Random Fields Basic Knowledge of PDEs Techniques to solve PDEs using Monte-Carlo , Polynomial Expansions. These mathematical techniques will help you visualize the model’s working and how to model and process raw data to create unique models whose functionality can be tuned. Parameters can be optimized for the problems and fine tuned with these techniques. learning all this will give you a masters degree/phd of knowledge in just math in ml/dl datascience. :P then after that you will probably wana learn like statistics of higher dimensions like PCA and stuff like that.

@mrdbourke

2 жыл бұрын

You can be good at ML with code-only but if you want to really dive in, you’ll want to learn the math. Don’t be scared of math. It’s the language of nature. Schools give it a bad rap but it’s amazing.

@mageshyt2550

2 жыл бұрын

@@mrdbourke thank u so much Daniel .I bought your ml course on udemy .your teaching was amazing 😁

Hotdog not hotdog