Check out my video on the Titanic dataset - I think you'll like it https://youtu.be/SffkBc1MQ40
Loved your video. Buy kindle edition to reduce some cost of ownership
Can I read the second edition without reading the first edition?a
is still recommended for 2022 ?
What book would you recommend that has every example in C++ instead of python? Also, what book for just the high level theory, without using actual coding, that could then be adapted to any computer language? In particular, I want to be able to write neural networks in C++, because then I can essentially create C++ bindings that I can use in any language - for example, creating binding languages in Xamarin, WASM, etc ... unfortunately, python isn't portable enough, yet I can even use C++ libraries in embedded devices that use the Zepher RTOS, such as Nordic's nRF5 SDK etc. Great review, btw!!!
I confirm everything you said.
I think 90% of people who wish to learn anything significant, or anything even moderately complex, should learn it top down. Abstraction->details
I just made to thru chapter 3 motivated learner as you described. I went thru a few crash courses on python but am loving it!
Read ,, An Introduction to statistical Learning" from Hastie, T. et al (2021) 2. Edition
Hi Giles, I have been watching your channel for quite a while now. I have started reading this book in order to learn more about ML. I am now starting with chapter 3. But, I have a question and it is that "How do I use this book in the best way?". Just reading is not enough (after reading 2 chapters and not writing the code), so, I think I should start writing alongside the chapters but that will take a long time to finish each chapter. So, I was wondering who has read the book before, he might be able to tell what is the best way to go about learning ML through this book?
Thanks for posting such good reviews and resources about ML and python!
Hope you will see this comment soon! @Python Programmer
I want a bit of advice. I was planning to get started with ISLR 2. But, this book is also great. Which one will you recommend to get started with?
Note: I'm really motivated enough to learn machine learning.
Some Criticisms of the bookالانتقادات
please make a video when it's 3rd edition comes out
Can anyone pls give me the link of the pdf version of latest edition of this book.
how about python machine learning by sebastian raschka?
As a beginner I found this book very difficult to understand...
Hello my friend, how's it going? I'm enjoying a lot with your channel, I started to learn Python and I'm working as a Networking Enginner and Python has been useful for me to solve my problems. By the way tell me the name of that book TCP/IP in your background
sir, i know the python and i also done two courses of ML from Andrew Ng so, can i buy this book or i have to learn more . plz guide
I bought this book and boy is it heavy !!!
What is the cost of this book...
Did I miss any? What are your favourite data science and machine learning books?
Is this book for beginners?
Awesome and thanks for the video
Remake the video with new list of book?
Hi, can you recommend a deep learning book for beginners?
Daniel you are awesome and very generous in sharing the knowledge...the love the way you explain the things...great..
Great list. Thanks Daniel !!
Download Deep Learning with Python 2nd Edition PDF by Francois Chollet : https://www.lunaticai.com/2021/07/deep-learning-with-python-second-edition-pdf.html
please can you put your email here
How do you feel about using a kindle to read some of these books considering they can be graphic and color and text intensive?
no wonder why his voice sounds familiar. I am doing his course on Udemy and its worth it. So good explanation.
Greatly appreciate you sharing your reading list, so to speak...
Thank you so much for that
Gift me that data analysis book
7:13 HAHAHAHAHHA 🤣🤣🤣🤣🤣🤣🤣
Introduction to machine learning with python? Is it good book?
Hey Daniel, if you don’t mind, can I send an email and ask you for some information about a career in DS and ml and how the job situation in Australia is
Reading is learning. You are correct about that.
are freecodecamp certifications worth it, for the new python courses that are out there?
I have "Python for Data Analysis" and "Hands-on Machine Learning with Scikit-Learn and TensorFlow". Other book I recommend also "Python Data Science Hanbook" the author is Jake VanderPlas.
Thank you very much for introducing VARIDESK.COM. Your desk looks really nice.
Which programming language should we know to read this books
Can you suggest me a beginner-level deep learning book?
Thanks you bud!
pretty good recommendations!! Would definitely start reading those :)
what are your reviews on Hands On Machine Learning & Scikit Learn?
Do you require Real analysis to be a machine learning engineer?
I have read the mml book.......what a great book it is.......i llove maths
I really like "Practical Machine Learning in R" by Fred Nwanganga and Mike Chapple. It doesn't overwhelm you with math or ask you to program anything from scratch. It only covers a few statistical techniques so an expert would not be impressed. But I think it is just right for somebody who is struggling and needs a better understanding of data analysis using machine learning.
Good informative video! Happy holidays
You know evolution with Continuous Gray Code Optimization works very well with neural networks. You can track down the pdf on the internet. You only need to pass around short sparse lists of mutations, costs and accept or reject messages between GPUs which could be scattered around the globe Each GPU has the full neural model and part of the training data which could be private and local. Every GPU gets the same sparse list of mutations, returns the cost for its part of the training set, the costs are summed and and the same accept or reject mutations message is sent to each GPU. That is far more decoupled than distributed BP.
You know ReLU is a switch. f(x)=x is connect, f(x)=0 is disconnect.
A ReLU net is a switched composition of dot products. When all the switch states become known the net collapses to a simple matrix upon which you can apply various metrics if you are curious. For example the variance equation for linear combinations of random variables applies to the various dot product outputs and you can quantify noise sensitivity.
More important it tells you you are allowed to put together dot products and switching in a very wide range of ways, way beyond the historical constraints and dogma.
You know you can swap around what is adjustable in neural nets. You can have fixed dot products (enacted with fast transforms like FFT or WHT) and adjustable (parametric) activation functions like fi(x)=ai.x x=0, i=0 to m. You don't want the frequency response of the first transform, you are only using the tranforms as fixed systems of dot products. To prevent that you can apply a random (or sub-random) fixed pattern of sign flips to the input data. You can also use a final transform as a sort of readout layer. Such a net then is: sign flips, transform, activation functions, transform, activation functions.......transform. Very fast and far fewer parameter than a conventional net.
The fast Walsh Hadamard transform is ideal for that and with a specialized chip could have extreme performance compared to a conventional net.
great recommendations and videos
Some really basic foundation things have not been researched properly. The scientific method has been absent in ML.
The variance equation for linear combinations of random variables applies to the dot product. To use the dot product as an associative memory the equation tells you to evenly spread out input data over all the dimensions of the dot product. To have high storage capacity you need some non-linear behavior. Thus: random projection, non-linear behavior, dot product.
There are quite a few other thing I know of where it is obvious that things have been set in stone far too soon. There are far more options for constructing and training neural networks than even 'advanced' experts know about. And that is their own fault due to poor scientific method and assuming about the basic math.
Which playlist to start with for computer vision on your channel?
What are your Thoughts of Machine Learning - A Probabilistic Perspective ; by Kelvin Murphy? Also I want to get started with some books on ML, I have Lin. Algebra, Single & Multivariate Calc along with differential equations knowledge on an Undergrad Engineering Program level. I'm afraid I dont have much knowledge on Probability and Stats. I want to know what books to read and in what sequence. Including books to fill gaps in math knowledge, Stats and Prob basics to Advanced and ML basics to Advanced in a pure Mathematical view. It would be really helpful if you reply. Thanks.
Can you provide pdf
thank you. hands-on ml is very good as well. i currently read deep learning with python and it is very good too. i read ai a modern approach till 300th page and i didn't get it. it is too advanced.
thankyou so much for all the work you do, you save our lives, your videos are my guidlines towards ML, i have one question about (vgg feature extraction,XGBoost classification model, I use breast cancer images, when fit the model, the layers are incompatible, itried to solve this by using suggestions from the internet, but was not solved so far, could you please help me to resolve this issue, I really hope that you will see my comment.
Thanks for your advice here - very much appreciated. Totally agree with you on Deep Learning for Computer Vision -- the lack of a chapter on semantic segmentation is a significant shortcoming of this text.
Thank you for sharing this.
Any recommendations on Reinforcement learning?
Can you provide the e-book of the basic ML book
Happy Christmas 🎄 your videos have got me to the end of my MSc this year ! Thank you for all the work you do :)
Thanks a lot sir, your channel is priceless and thanks for all the educational content!
Sreeni sir , will you suggest me best opensource tool for annotating data for computer vision/ Segmentation problems
Thank you sir .. happy Christmas
Thank you so much sir for the work and videos you've been putting up.
I will really like to speak with you 1:1
After read all those books, now I am very confident to talk to anyone who do not work on ml/dl, they all belive I am real ml/dl expert! thanks for sharing
sir plz can you make a video on corona virus detection using deep learning approach using restricted boltzmann machine . there is no video of image classification using deep learning by restricted boltzmann machine.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow This book is best.
Thank you so much. Your videos are more easy to follow for me.
Thanks very much for your recommendation
Thank you, my teacher.
Thank you, Mr. Sreeni. I wish you a happy Christmas