Intro to Deep Learning (ML Tech Talks)

[@TensorFlow]

Here are three helpful classes you can check out to learn more: Intro to Deep Learning from MIT → goo.gle/3sPj8To​ MIT Deep Learning and Artificial Intelligence Lectures → goo.gle/3qh7H54​ Convolutional Neural Networks for Visual Recognition from Stanford → goo.gle/3bbC34I​ And here are all the links to demos and code from the video, in the order they appeared: Face and hand tracking demos → goo.gle/2WTCwSc​ Teachable machine demo → goo.gle/3bSCzCi​ What features does a network see? → goo.gle/3e2zpA5​ DeepDream tutorials → goo.gle/3bYIBTp​ and goo.gle/384B6JC​ Hyperparameter tuning with Keras Tuner → goo.gle/2InBK7J​ Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → goo.gle/309pMY5​ Linear (and deep) regression tutorial → goo.gle/3sKxkN7​ Image classification with a CNN tutorial → goo.gle/3qdD2Wb​ Audio recognition tutorial → goo.gle/3kFpl1j​ Transfer learning tutorial → goo.gle/3bV7D60​ RNN tutorial (sentiment analysis / text classification) → goo.gle/3bVM1X7​ RNN tutorial (text generation with Shakespeare) → goo.gle/3qmnrnz​ Timeseries forecasting tutorial (weather) → goo.gle/3ecdYg9​ Sketch RNN demo (draw together with a neural network) → goo.gle/3bbHTTy​ Machine translation tutorial (English to Spanish) → goo.gle/3e7IJme​ Image captioning tutorial → goo.gle/3sKFNQz​ Autoencoders and anomaly detection tutorial → goo.gle/30aD0UA​ GANs tutorial (Pix2Pix) → goo.gle/3kI1ZrB​ A Deep Learning Approach to Antibiotic Discovery → goo.gle/3e7ivQD​ Integrated gradients tutorial → goo.gle/2PxfRtq​ and goo.gle/3sE0bmq​ TensorFlow Playground demos → goo.gle/2Px6rhB​ Introduction to gradients and automatic differentiation → goo.gle/3sFVybo​ Basic image classification tutorial → goo.gle/3c2AF3o​ Overfitting and underfitting tutorial → goo.gle/3cdA9Qv​ Keras early stopping callback → goo.gle/308XQUj​ Interactive autoencoders demo (anomaly detection) → goo.gle/3kPfW7q​ Deep Learning with Python, Second Edition → goo.gle/3qcQ5Y5​ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → goo.gle/386DKP4​ Deep Learning book → goo.gle/3c2VQmd

[@ezazakhtar6339]

Absolutely fascinating series!

[@raj-nq8ke]

great series. Hope to work with your team in near future

[@JBGordon]

It was fun recording this! I hope it's helpful to you. I know there are many intro to dl talks :) A good strategy you can use to learn a topic is to leverage talks (and books!) by different people on the same idea in parallel. Everyone covers it a bit differently. Some of their explanations will click for you, and you can merge them into your own understanding. I left links to a bunch of my favorite courses + books in the video description for you (they're really great!), so you can dive deeper.

[@JBGordon]

@Amin Thank you! Sure thing, the best place to start is with this guide (for subclassing) www.tensorflow.org/guide/keras/custom_layers_and_models + this one (for functional): www.tensorflow.org/guide/keras/functional. The 2nd editions of Hands-on ML + DL with Python both cover subclassing, too.

[@dinoscheidt]

Very very approachable. Nicely done! From now on I will forward this instead of just the tensorflow course site. We need more product engineers to take a look at AI if we want deep tech to go forward. 30k machine learning engineers vs 60Mio software engeneeir 😵

[@notgaurav]

Thanks for the recording

[@NicolasAgostini-Nico]

This was great! Thank you for this recording and links!

[@merenptah1985]

You have a great talent for teaching.

[@farhanfuadabir4565]

It is by far the best introduction to Deep Learning I had. Thanks.

[@xedski]

The best I've seen to date. Not about coding, not about matrix algebra, it's about concepts behind what is going on with tons of parameters whil eventually result in a model.

[@ayoolafakoya9841]

Fantastic Intermediate introduction to Deep Learning .

[@7Trident3]

That was a lot of ground covered in in 75 minutes! Bravo Sir, your skills as a teacher are being honed in front of our eyes!

[@paedrufernando2351]

agree

[@soukisama05]

Thank you! High level content explained in a simple and direct way. Bests!

[@prakashsuthar8036]

Thank you so much for recording excellent presentation. You have covered all key concepts exceptionally well. Please continuing sharing your knowledge.

[@donquixoterosinante7899]

I love his explaination and genuine kindness he show throughout the video

[@nasilasy]

Was going to just watch the first few minutes and come back to it later but end up watching the whole video. The best high level introduction to deep learning and it's accompaning concepts. Thanks for sharing this valuable resource.

[@borin2882]

This is the best presentation that mostly covers all the things in Machine Learning. Thank!

[@yk4993]

The sigmoid function (in the context of logistic regression) is not just interpreted as probability, it truly yields the probability, though the fact that it can be analytically derived is mostly overlooked in ML courses.

[@masudRana-br6ev]

A complete presentation indeed. The way you followed was simple and easy for everyone. Thank you very much for sharing your knowledge .... and you always had a smiling face that made the presentation more attractive to me. Again thanks a lot

[@notsure7132]

I have never seen the IT community so excited. Thank you all around the world..😁

[@uty6595]

this is unbelievably good, makes neural network interesting, even to an old man like me haha

[@imranullah3097]

I love your teching approach ☺😍.

[@kunalsoni7681]

great explanation :) I'm very happy to watch this

[@mohammedqasim7608]

Very well explained, thank you for doing this and best of all the last few mins on the reference books! super like!

[@kyleaustin2728]

THANK YOU

Great introduction to dl, thank you!

[@mikyM82]

This is gold

[@ekaterinadranitsyna5107]

Josh, thank you very much for this overview of neural nets! It's really useful to have it all in one place! I have a tangent question: is it possible to run TensorFlow on AMD GPUs on Windows? Would it help to install Linux subsystem and ROCm or it wouldn't work?

[@halilibrahimaysel6796]

Thank you so much for this amazing recording! I was just wondering if you can recommend any paper for interpretation with "Integrated Gradients". It will help me a lot!

[@RobinWootton]

Totally excellent, well explained.

[@ketsakda]

Thank you so much. Great presentation

[@fahemhamou6170]

تحياتي الخالصة شكرا جزيلا

[@SuperNelmer]

We miss you at our GDG meetups Josh! ToT

[@SuchetanSaravanan]

Amazing video!! :)

[@retagif]

Is machine learning a pre-requisit for this course? Where to start?

[@oviasifovihasan]

Can I have the PPT slides of this video, please?

[@WhenThoughtsConnect]

Ai is just, a large marble plinko game but the vector fields but are pruned based on test scores

[@mhmdshkier9722]

ثم عص

[@WhenThoughtsConnect]

theres games, then there are games of games.

[@desiremei1387]

Some one I think have USD this to hack my gmail Facebook and Twitter account

[@NikiWinProd]

tech talk sounds a lot like tiktok

[@fouratiahmad9578]

Hello, thank you for your video, it is beneficial to me. I have my private dataset, and my U-net code runs correctly (train and test) on the full dataset. I want to test the model on only 27 images. When I execute evaluate function, I meet this error : **InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: slice index 1 of dimension 2 out of bounds. [[{{node strided_slice_1}}]] [[IteratorGetNext]] [[IteratorGetNext/_4]] (1) Invalid argument: slice index 1 of dimension 2 out of bounds. [[{{node strided_slice_1}}]] [[IteratorGetNext]] 0 successful operations. 0 derived errors ignored. [Op:__inference_test_function_12429] Function call stack: test_function -> test_function** Can you please help me? thank you