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
@ezazakhtar63393 жыл бұрын
Absolutely fascinating series!
@raj-nq8ke3 жыл бұрын
great series. Hope to work with your team in near future
@JBGordon3 жыл бұрын
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.
@JBGordon3 жыл бұрын
@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.
@dinoscheidt3 жыл бұрын
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 😵
@notgaurav3 жыл бұрын
Thanks for the recording
@NicolasAgostini-Nico3 жыл бұрын
This was great! Thank you for this recording and links!
@merenptah19853 жыл бұрын
You have a great talent for teaching.
@farhanfuadabir45653 жыл бұрын
It is by far the best introduction to Deep Learning I had. Thanks.
@xedski3 жыл бұрын
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.
@ayoolafakoya98413 жыл бұрын
Fantastic Intermediate introduction to Deep Learning .
@7Trident33 жыл бұрын
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!
@paedrufernando23512 жыл бұрын
agree
@soukisama053 жыл бұрын
Thank you! High level content explained in a simple and direct way. Bests!
@prakashsuthar80363 жыл бұрын
Thank you so much for recording excellent presentation. You have covered all key concepts exceptionally well. Please continuing sharing your knowledge.
@donquixoterosinante78993 жыл бұрын
I love his explaination and genuine kindness he show throughout the video
@nasilasy3 жыл бұрын
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.
@borin28823 жыл бұрын
This is the best presentation that mostly covers all the things in Machine Learning. Thank!
@yk49933 жыл бұрын
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-br6ev2 жыл бұрын
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
@notsure71323 жыл бұрын
I have never seen the IT community so excited. Thank you all around the world..😁
@uty65953 жыл бұрын
this is unbelievably good, makes neural network interesting, even to an old man like me haha
@imranullah30973 жыл бұрын
I love your teching approach ☺😍.
@kunalsoni76813 жыл бұрын
great explanation :) I'm very happy to watch this
@mohammedqasim76083 жыл бұрын
Very well explained, thank you for doing this and best of all the last few mins on the reference books! super like!
@kyleaustin27283 жыл бұрын
THANK YOU
3 жыл бұрын
Great introduction to dl, thank you!
@mikyM823 жыл бұрын
This is gold
@ekaterinadranitsyna51073 жыл бұрын
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?
@halilibrahimaysel67963 жыл бұрын
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!
@RobinWootton2 жыл бұрын
Totally excellent, well explained.
@ketsakda2 жыл бұрын
Thank you so much. Great presentation
@fahemhamou61702 жыл бұрын
تحياتي الخالصة شكرا جزيلا
@SuperNelmer3 жыл бұрын
We miss you at our GDG meetups Josh! ToT
@SuchetanSaravanan3 жыл бұрын
Amazing video!! :)
@retagif3 жыл бұрын
Is machine learning a pre-requisit for this course? Where to start?
@oviasifovihasan3 жыл бұрын
Can I have the PPT slides of this video, please?
@WhenThoughtsConnect3 жыл бұрын
Ai is just, a large marble plinko game but the vector fields but are pruned based on test scores
@mhmdshkier97222 жыл бұрын
ثم عص
@WhenThoughtsConnect3 жыл бұрын
theres games, then there are games of games.
@desiremei13873 жыл бұрын
Some one I think have USD this to hack my gmail Facebook and Twitter account
@NikiWinProd3 жыл бұрын
tech talk sounds a lot like tiktok
@fouratiahmad9578 Жыл бұрын
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