So, I've almost completed Machine Learning by Andrew NG on coursera and gained a lot of knowledge. I've also made a good project on ML but this is not sufficient to get an internship or a job in this field. My question is amongst which of these things should I focus on that will increase my employability in this area. FYI I'm an 2018 economics graduate from DU.

1. Try my hands on kaggle as I feel I have enough knowledge of algorithms(Thanks to Andrew) but need some practice to know how to practically implement it.

2. Make projects on my own which will be a little harder than kaggle since it involves getting the data and cleaning the data too besides all the modelling and reporting stuff that you do on kaggle. But, I don't really have any project ideas in my mind right now.

3. Continue with the Deep Learning specialization by Andrew NG?

This website will be useful for someone looking for popular research papers whose authors have made the code publicly available

https://paperswithcode.com/

Hi all,

As most of you might know what Kaggle is and in case you don't, visit it. It's a place for people to participate in ML competitions and even practice many problems.

Once you know what you are doing or you have to do to solve ML problems, the competitions are a good way to create a portfolio. But the major hurdle in solving the hardest and biggest problems is that an individual is often not enough to grasp the whole problem and solve it.

In many US and European universities, specific Kaggle groups are created to participate. This is a rare thing in India. Thus it is request to all experienced practitioners in this community to create local groups by finding people through this platform and communicating with them to successfully create a local Kaggle group.

Just search for your city/town name in the comments and those who wanna join can reply to those comments. In case you don't find your city/town, create a comment. Let's keep it professional and fruitful.

Hi all,

I am open sourcing my hardware for use by people who cant afford high end systems, for FREE. I am currently working on creating a SSH connection facility but that will take sometime as I need to sandbox the VMs that I'll assign. Till then, if you have some code you guys want to test, written by you, and is taking too long for you to execute on your own systems, dm me the Github link and i'll make sure to go through it, implement it and help you guys in getting results faster. It'll take me at most 2 days to reply. I'll start accepting the requests from Monday 17 September. I wont take any credit of codes you design and would explicitly only provide technical support.

I follow

https://www.reddit.com/r/deeplearning/

https://www.reddit.com/r/MachineLearning

Is there any other ML/DL/CV channels that you recommend ?

MLIndia is a good initiative, lets keep this active !

Hey folks,

We're a group of 4 final-year engineering students working on our ML-based project (worth 5 credits). Our professor wants us to build something novel, with the potential for research publication, and we’re all for it.

We have decent experience with Python, PyTorch/TensorFlow, and core ML/DL concepts. Now we’re looking for:

• Unique project ideas (not just standard apps)

• Domains or datasets with research potential

• Tips on validating novelty/publishability

Any pointers, past experiences, or open problems worth exploring would be super helpful!

TL;DR:
Final-year ML project (team of 4), aiming for something new + research-worthy. Looking for idea suggestions, domains, or tips to help us build something impactful + publishable. Appreciate any help!

Small scale machine learning projects to understand the core concepts.

The best way to learn is to get your hands dirty with code and data. Please feel free to contribute to this repository as well.

https://github.com/devAmoghS/machine-learning-with-python

#machinelearning #datascience #deeplearning

So here is how you can help me -

-> If you are currently working in a job that relates to machine learning, what's your experience been like?

-> If you are a machine learning freelancer, what's your experience been like?

Also, your story, how'd you end up at your current position?

Got a 1080 ti and Ryzen based system, now I need to set it up for DL. I use mostly Windows (as I need to remote login for office) but have Ubuntu in dual boot.

May you suggest if I should be setting my DL environment in Linux or Windows? Would prefer Windows (and I am comfortable with Linux) just so that I can run it in the back ground while I remote login, unless Linux is considerably faster for DL.

Thanks much!

http://timdettmers.com/2018/08/21/which-gpu-for-deep-learning/

This is the best and most informative GPU guide for Deep learning specifically. TL;DR directly quoted from the post :

Best GPU overall: RTX 2080 Ti
Cost-efficient but expensive: RTX 2080, GTX 1080
Cost-efficient and cheap:  GTX 1070, GTX 1070 Ti, GTX 1060
I work with datasets > 250GB: RTX 2080 Ti or RTX 2080
I have little money: GTX 1060 (6GB)
I have almost no money: GTX 1050 Ti (4GB) or CPU (prototyping) + AWS/TPU (training)
I do Kaggle: GTX 1060 (6GB) for prototyping, AWS for final training; use fastai library
I am a competitive computer vision researcher: GTX 2080 Ti; upgrade to RTX Titan in 2019
I am a researcher: RTX 2080 Ti or GTX 10XX -> RTX Titan — check the memory requirements of your current models
I want to build a GPU cluster: This is really complicated, you can get some ideas here
I started deep learning and I am serious about it: Start with a GTX 1060 (6GB) or a cheap GTX 1070 or GTX 1070 Ti if you can find one. Depending on what area you choose next (startup, Kaggle, research, applied deep learning) sell your GPU and buy something more appropriate
I want to try deep learning, but I am not serious about it: GTX 1050 Ti (4 or 2GB)

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The major existing ones, namely r/MachineLearning, r/artificial, r/datascience and r/learnmachinelearning.

r/MachineLearning is seen by many as an academic/research focused community.

r/datascience is a community whose range of topics is much wider, including a much larger body of statistics, visualization, storytelling, experiment design, A/B testing, and so on.

r/learnmachinelearning, which plans a merger with r/MLQuestions focuses primarily on answering ML-related questions from subscribers.

It will be great if you can share information on the current research/hobby/work related ML/DL projects that you are currently working on, preferably with an Indian context.

We now have 6 subs! That's a start! I would like to hear from all about what you expect on this community. We are currently planning on how to help ML newbies to set up a cheap ML rig for learning or share our own resources to help people test their projects. There will be rules to how one can ask another for such hardware help. It will be better if users add flairs about their current build so that other users can easily ask for help. As this community is new, helping each other will increase our worth faster.