IoT-For-Beginners VS tensorflow

Microsoft also has similar courses on IoT, and Data Science. I found the IoT one really nice [0], and it covers a lot of ground.

[0]: https://github.com/microsoft/IoT-For-Beginners

2️⃣ IOT Check this out 👇 https://microsoft.github.io/IoT-For-Beginners

You can go for IOT. Please refer to this - https://github.com/microsoft/IoT-For-Beginners This is good to get started with. Good industrial stuff can be built using IOT. You will need a device though like Raspberry pi or Arduino

You can read this definition here, https://github.com/microsoft/IoT-For-Beginners/blob/main/1-getting-started/lessons/1-introduction-to-iot/README.md

Anyone tried this course and know if it’s any good? https://github.com/microsoft/IoT-For-Beginners

three educational courses- Web Dev, ML, and IoT for beginners. Note re using educational resources as a strategy for marketing , at least the ML course links to various Azure services. Google does this a bunch as well, with Collab notebooks often being used to demo educational materials.

Have you checked out Microsoft IoT for beginners course? It has both Python and C++ code depending on what device you use.

microsoft/IoT-For-Beginners

By Educating others:- Another way is to educate others. There are many ways to do this, Like creating tutorials on youtube, writing blogs, or making Github repositories for storing and sharing learning material like IoT Course from Microsoft (which I am learning IoT from).

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

Explore here.

Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)