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