Real-world image classification using convolutional neural networks | Machine Learning Foundations

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Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow.

In Episode 5, Google Developers can learn how to use convolutional neural networks to classify complex features and build machine learning models with Tensorflow. This tutorial explores real world image classification, with a hands-on example to tackle a more challenging computer vision problem, classifying images of horses and humans!

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Great videos Mr Laurence. As the other user commented your videos are much more interesting and easy to understand than other paid courses. I like the way you explained each and every detail. The pace of the videos is also good. More importantly one should feel to do the exercises, not to "give up" as things get complicated. Thanks a lot for the wonderful videos. Keep uploading more.

madhusudaneyunni

These lessons from Tensorflow really help me a lot.
I was able to create my own bank note image classifier.

August

Thank you Mr Moroney. I am following you here as well as at Harvard.TinyML course.

ArtExplained-

who would have thought that the model that I have been so eager to understand, is simply elegant lines of code

seanrosario

W.r.t previous assignment's solution & in general too: For those getting ValueError when reshaping the images, simply do training_images.reshape(-1, 28, 28, 1)
Here, -1 means it will determine the 4th parameter automatically based on the image size divided by 28x28x1, provided that result is a natural number & not any floating value, else you would need to tweak the other dimensions accordingly.

inocentboy

Wow! These lessons are so cool and usefull! Thanks from Russia!

artemborzenko

This playlist is too good, it's really helpful and I learn a lot of things from these videos.

umarfarooqmrkhan

Is there a validation set for happy-or-sad.zip? I couldn't find it. How do I validate ?

tazoo

What kind of filter we are using in Conv2D function where we are only defining number of filters. Is there any default filter used while using Conv2D function.

Thank You in Advance!😊

sushantshelar

It is a great course, can you plz make a video on incremental learning(i.e. feeding the trained model with another dataset so that it can identify both the new as well as the old one

kushchoudhary

These videos are very good I had tweaked the last layer to 2 cells no wonder why my loss was showing NaN

digvijayyadav

Wow, I have learned a ton from these videos. Thanks Laurence

Shaan

I want to know about Radial Basis function neural network, multilayer perceptron,
sequence to sequence model.. How many of them will you teach?

brajendrasharma

Pretty unlikely that I get a response here, but I'll give it a try: What can I do if I have 17000 unlabelled images in one directory/folder and don't want to preselect them?

GamingShiiep

It was really good video. What if we increase the learning rate ??

snehashukla

Thank you very much for these excellent videos. I am a little bit confused about steps_per_epoch and validation_steps and their relations with epochs, batch_size and iterations. DO you have some videos that explain these parameters?

mesrepas

Great class. You are a wonderful teacher. Thanks a lot!!

zapphodbeeblebrox

Hi, I want to ask if I test the code on my own server, what will be the code to upload images to classify on the Jupyter notebook?

chitrungbuitran

Hello Laurence Sir,
QUESTION:: After performed the training of CNN for detection of a certain object, wherein the CNN architecture the learned features from training dataset stored?

TejasPhase

Why didn't you rescale the uploaded image to colab by 1/255, as you did for train and validation images? Is not it necessary to scale the image while making a prediction?

DiptaparnaBiswas

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