I know we're very critical on certifications on Reddit and often for very good reason. A certification doesn't prove skill and is no substitute for experience, and an awful lot of existing certifications in the data science space are money grabs that lure in people with false promises of mountains of gold waiting to be data-mined.

I've done some proctored exams in 2015 and found them nerve wracking. I can't code or answer questions when there's someone watching me and quizzing me on "WHATS THAT SOUND" every five minutes. I recall finishing an exam I paid 300 dollars for and the amount of harassment made me sick to my stomach. I did not pass the exam, and I don't even think I finished on time.

I did the TensorFlow certification exam last week and my experience is so much different. The exam is coding-based, takes five hours, is administered via a PyCharm plugin and there's no one watching you. When I finished I got the results almost instantly. Only the certified directory and the accredible link take long. I basically spent five hours coding like I would normally do on my job.

I know this sounds like a sales pitch, but I hope we move towards this model of evaluation for more "data science" certifications. IMO any certification should tests the skills required to do the job, not the ability to select the right option from a multiple-choice list.

Too me this is what stood out about the entire process. 9/10 would do again.

I've written down more experiences in this blog post for anyone interested in the exam itself, which I hope is allowed wrt the self promotion rules. It was removed on /r/datascience before for this reason.

Have played with TensorFlow for a while by reading and revising other's code. Now I want to read tensorflows tutorial carefully and learn tensorflow comprehensively. Then I find the official tensorflow's tutorial is really sucks. For one thing, there are to many see other pages, I have to click many links in order to get understand about a code in tutorial. For other thing, tensorflow's APIs are not unified. In https://www.tensorflow.org/get_started/eager, it tells me to load CSV file by tf.data.TextLineDataset, but in other page (https://www.tensorflow.org/api_docs/python/tf/TextLineReader), it tells me to load CSV file by tf.TextLineReader. Can they unify the tutorial?

Hello Everyone! I’m planning to buy the M1 Max 32 core gpu MacBook Pro for some Advance Machine Learning (using TensorFlow) like computer vision and some NLP tasks. Is it worth it? Does the TensorFlow use the M1 gpu or the neural engine to accelerate training? I can’t decide what to do? To be transparent I have all Apple devices like the M1 iPad Pro, iPhone 13 Pro, Apple Watch, etc., So I try so hard not to buy other brands with Nvidia gpu for now, because I like the tight integration of Apple eco-system and the M1 Max performance and efficiency. Also, I use Google Colab BTW. Kindly please help me to decide. Thank You All!

I have RTX 3070. I have Visual Studio installed, the appropriate driver installed along with cudnn and the appropriate CUDA toolkit. I have copied the bin, lib and include files into my C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2 directory. I have python 3.8.5 installed and Tensorflow 2.4.1.

Is there another step I am missing? I am running tf.test.is_built_with_cuda() and getting True, while tf.test.is_gpu_available(cuda_only = False) returns false.

It seems like Tensorflow 2.11 is dropping official native support for Windows. I was trying to compile TF 2.11-rc1 on Windows (as I always have been to optimize for my CPU and GPU model) but hit with an error which I have reported here (https://github.com/tensorflow/tensorflow/issues/58323). They told me to refer to the release note (https://github.com/tensorflow/tensorflow/blob/v2.11.0-rc1/RELEASE.md#major-features-and-improvements)

That release note basically said TF 2.11 Windows x86_64 CPU wheel will be maintained by Intel while TF 2.11 Windows GPU wheel will be discontinued so Windows GPU users need to use WSL2 in order to use TF2.11 on their GPUs!! Currently TF 2.11-rc1 wheels on PyPI (https://pypi.org/project/tensorflow/2.11.0rc1/#files) contains nothing, only 1.9kb in size!

That also implies users who want to compile TF GPU on windows on their owns wont be able to do so directly as TF not running CI tests on Windows GPU. Any compilation error only happens on Windows wont be caught and fixed!!

Anyone knows what is going on?? Why TF not running CI tests on Windows GPU anymore??

Hey everyone,

I recently started doing some smaller TF projects and decided that it's a good time to get some more formal training in the area and with TF.

I found a few interesting resources and was wondering if any of you have completed them and what your thought are? Are there any great resources I am missing on this list?

• https://www.coursera.org/professional-certificates/tensorflow-in-practice#courses

• https://www.coursera.org/specializations/deep-learning

• https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses

• https://www.coursera.org/specializations/ai-for-medicine

• https://www.coursera.org/specializations/tensorflow-data-and-deployment

• https://www.coursera.org/specializations/tensorflow-advanced-techniques

• https://www.coursera.org/specializations/practical-data-science

• https://www.freecodecamp.org/learn/machine-learning-with-python/

• https://www.kaggle.com/learn/time-series

• Obviously the TF Documentation

For background, I am most interested in time series analysis, medical AI, and TF on embedded devices like the Google Coral but think a combination of a broad course + one specialized on these areas would be best.

What are your thoughts?

Hey guys. I was browsing the Google developer site for guides on coding Android when I stumbled upon Tensorflow. It seems to me to be a mighty promising field with exactly what I want - niche and high paying. But is it too niche that I would not get any jobs, since I am a freelancer learning new skills through self-study?

I am worried that Tensorflow or any other new techs are only reserved for currently employed software engineers who are promised jobs already by their employers. I could enter Android dev as a self-study freelancer, but what about Tensorflow - or machine learning software dev in general?

I saw the talk on Tensorflow Probability in the latest set of Dev Summit talks on Youtube. But I was still just trying to understand what the purpose or application of Tensorflow probability was? I think even the authors of the package mentioned that the name Tensorflow Probability sounded a bit confusing.

So is the purpose of Tensorflow Probability to do Bayesian analysis in Tensorflow? Like is it meant to be a replacement for other software like STAN/BUGS/JAGS--namely other MCMC samplers. So would we use Tensorflow probability to estimate bayesian hierarchical model and get posterior distributions on the parameters? If that is the case, does Tensorflow Probability perform any better than these other STAN/BUGS/JAGS--has anyone done any benchmarks.

Or is Tensorflow Probability about getting posterior distributions from graphical models--like markov random fields?

I was hoping someone could explain the application of this new and interesting looking package.

Has anyone gotten tensorflow working with nvidia's RTX 3000 series GPUs? I'm currently working with a RTX 3070 and have tried methods such as pip installing tf-nightly-gpu, compiling from source, and using tensorflow's docker images but I can't seem get my models training using the GPU. I'm not getting any errors in the prompt and tensorflow is successfully detecting my 3070 but whenever I train my model it just uses my cpu. If you got tensorflow to work can you share how?

Update: I am using NVIDIA 455.32 version drivers, CUDA 11.1, CUDNN 8.0.4 (for CUDA 11.1), and tf-nightly-gpu.

My model is hitting a plateau at 80% and I can't figure out how to improve it. Could I just add more filters? Or is there diminishing returns for filters?

Hi all, I was wondering why Tensorflow dropped support for Windows + GPU. I just spent the last 4 hours getting it to work on WSL 2 due to CUDNN errors with Tensorflow 2.12. What is the reasoning they had of making the barrier to entry so much higher by forcing usage of WSL for 2.11+? This makes install take at least 2-3x longer due to needing to install cuda libraries manually and is very error prone to those unfamiliar with linux (in addition to causing issues when updating as I did from 2.11 to 2.12 on WSL due to new CUDNN requirements).

Hello everybody, I'm about to write a kind of weather forecasting neural network and do not really find something good about transformers... My initial thought was a classic LSTM but in the last days I heard a lot of good stuff about Transformers, especially with multiple Input variables. Does Anybody here know more about that topic and would love to explain to me why I would use LSTM, Transformers or something else?

The data input will be a list of variables like temperature, humidity, elevation, wind speed, wind direction, ...

The data output should be a 0-100 possibility of a specific event to occur.

I have some Billion labeled Data Points of from historical data.

Thx for your Help!

Training my model keeps failing, because I'm running out of GPU memory. I have 24GB available and my model is not really large. It crashes when trying to allocate 47GB.

It's a CNN with around 10M parameters, input size is (batch_size=64, 256, 128). The largest tensor within the model is (batch_size=64, 256, 128, 32) and there are 8 CNN layers.

Memory growth is activated. When I reduce the batch size, it still wants 47GB of memory, so that doesn't seem to make a difference.

Can anyone tell me what likely causes the need for so much RAM? Or what I could do to use less?

I'd like to start off by saying that I am not a novice at programming or in using Linux/windows machines.

Every time I've tried to install tensorflow, whether on a laptop, raspberry pi 3 or desktop machine, I've never been able to successfully install it. I've had the install on my desktop machine (with Debian 10) and it froze everything (overnight) to the point where I needed to hard restart it. I've had run-ins where the linux installation docs told me to install bazel without stating that tensorflow didn't support the latest version (The build told me I needed to downgrade, which then lead to the comuter hanging). I've had issues with my raspberry pi hanging and no longer responding to any ssh commands until I hard restarted it.

Over my ~6 years of programming I have never run into a library which has been harder to install. I understand that this is a massive project in the machine learning scene, written in multiple languages with thousands of contributors, but I still don't understand why it is so hard to install.