Narendran Sridharan

Ranch Hand
+ Follow
since Aug 11, 2017
Merit badge: grant badges
For More
Cows and Likes
Total received
In last 30 days
Total given
Total received
Received in last 30 days
Total given
Given in last 30 days
Forums and Threads
Scavenger Hunt
expand Ranch Hand Scavenger Hunt
expand Greenhorn Scavenger Hunt

Recent posts by Narendran Sridharan

I am excited to see problems such as "water reduction", "Schrödinger equation", "Travelling Salesman Problem", "Factoring Problem" solved and stated with probability by Quantum computers with its core concepts "Superposition" and "Entanglement".

I hope we could solve any problems that takes many states and can be parallely explored by designing Quantum Circuits can be solved or some decision making for the possibility of a solution can be solved.

Penrose Graphical Notation and Richard Feynmann Notations may remain very helpful in these cases in designing these circuit, it may get simplified in future.  class of problems as it is states anything that is bounded error with Quantum Polinomial Time will be solved by Quantum Computing.
7 months ago
Information such as 10000 qubits requirement for factoring 2 prime numbers to make Quantum Advantage via parallel exploration. IoNQ and IBM research are very exciting.
7 months ago
Welcome Barry Burd. All the very best for your book promotion
7 months ago
Welcome to the Ranch Matt
1 year ago
Java community had removed few hurdle especially keeping students in mind. you can REPL (read, evaluate, print and loop) with Jshell, a good start for even kids.

Over a period of time students will be able to appreciate the underlying tools and technologies.

Getting things done quick has become today's norm, I ask chatgpt if I had to get started. I ask it to write code for me and correct code for me. It all looks awesome, staying in context over a chat on the topic, yet there are few limitations, beyond certain level you have to again face the reality and start digging into the underlying stuff.

We reach out community like stackoverflow and if we don't find any solution we started digging deeper for our unique problem.

Java had been transparent, innovative and successful in its endeavours in building a strong community, better processes for sustaining and maintaining it position in building better systems and softwares.

Datascientist prefer python, as they are best for throw away code after analysis, they don't maintain code for decades. Web Page designers prefer javascripts as they are more interested in manipulating html.

Decades of maintenance of softwares had been possible with Java. People who write python and javascript prefer java for preparing systems and softwares once they want to stabilize and maintain their code base.

Learning any new programming language will be exciting to get started. "absolute beginner guide" seems providing a very systematic approach and power boost for beginners, Matt seems to have carefully laid his course of thoughts for helping students of this age & era.

All the very best students and Matt.
1 year ago
Good to see you here. Welcome Sourabh Sharma
2 years ago
Thanks Thushan. Handwriting recognition and image to text conversion are few use cases i could quote which are similar to image classification/segmentation and combination of text and image.

My imagination on CNN is that I just recognize an object by zooming in and zooming out and try to classify or segment based on the recognized features.
For RNN, it makes a great sense to take LSTM model and assume how we read text and analyze the context of the sentence or a paragraph. we also can go forward and backward on sentences like in bi-directonal Networks. Definitely we have seqtoseq, manytoone and onetomany or many to many models.

Beyond these general example which puts up itself into the context of our human eye, reading skills and listening skills. is it possible to apply them in Strategic Games like what Open AI did? how complex such system will be and what will be general compute resource required in production systems.
I found some interesting information on few thing beyond deep NN in the preview pages of your book

Is tensor flow is for creating DNN pipelines, do tensor flow support stats based models to be combined with DNN? what are the advantages? For a newbie, how one should imbibe such facilities?
Mostly in all courses regarding deep learning and tensorflow, i commonly see the following examples,
1. Image Classification (CNN)
2. Image Segementations (CNN)
3. Text Classification (RNN)
4. Text Translation (RNN)
and mostly something to do with text, images and speech.

why do they form the foundations for anyone learning deep learning?
While it is interesting to learn about Do's with tensor flow in the book, it is also interesting to learn don't dos like Machine learning if we could do it with Scikitlearn etc.,

Can you please elaborate on it more? Is it really necessary to know what we should not be doing with tensor flow? What are the pre-dominant mistakes people do?
Welcome Thushan Ganegedara. Congratz on the book release. All the very best on the promotions.
Welcome, Marko. Congratz on your new book.
6 years ago
Welcome J Sharma & Ashish Sarin. Congratz on your new book
6 years ago
Hello Sebastian, Welcome to the Ranch! Happy to see you here with your new book. Congratz...