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zakruti.com » IT - Software » freeCodeCamp.org
Deep Learning with PyTorch Live Course - Image Classification with CNNs (Part 4 of 6)

Deep Learning with PyTorch Live Course - Image Classification with CNNs (Part 4 of 6)

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Rating: 4.5; Vote: 2
This video covers the basics of convolutions, the end-to-end process of training a convolutional neural network on a GPU for classifying images of everyday objects. Resources: - Image Classification with CNNs: https://jovian.ml/aakashns/05-cifar10-cnn - Discussion forum: https://jovian.ml/forum/t/lecture-4-image-classification-with-convolutional-neural-networks/1545 - Data science competition: https://www.kaggle.com/c/jovian-pytorch-z2g - Competition starter notebook: https://jovian.ml/aakashns/zerogans-protein-competition - Course project: https://jovian.ml/aakashns/03-cifar10-feedforward Topics covered: -- Working with the 3-channel RGB images from the CIFAR10 dataset -- Introduction to Convolutions, kernels & features maps -- Underfitting, overfitting and techniques to improve model performance -- Building & training a convolutional neural network on a GPU This course is taught by Aakash N S, Founder & CEO of Jovian.ml - a platform for sharing, showcasing and collaborating on data science projects online
Date: 2022-03-14

Comments and reviews: 9


At 1:31:37 and also earlier, Aakash says that we can pick the max. probability. Actually, the output from the model is not really probability. It is a floating point number (negative and positive) and it is rather the softmax layer (embedded into the cross-entropy loss function) that turns these positive and negative numbers into a probability (+ve) value that sums to 1. But picking the max. of such a output is still fine, since that is just the number that gives the max. probability when passed through the cross-entropy.
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Good day sir, I got to know about this program during the third week, i tried to register via the link provided but i later realized the registration has been closed. I just want to know if people in my category will have opportunity to participate in Kaggle competition scheduled at the end of this program.
I have watched all the previous videos and did all the previous given assignment but i did not submit since am not eligible.
Let me know your thought sir.
Thanks in advance.

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Is there such thing as junior machine learning engineer? Is it possible to get your very first job as programmer straight into ML? And that on the base of being self taught, studying for about 6 months, about 2 hours per day. Also moving from totally different field and starting your studies from the basics of Python.
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This has been one of the best videos so far. Aakash did a great job in explaining the convolution and overfitting problems. Really enjoying the series so far! I like how we came here gradually applying different techniques hands on and understanding the limits of each one!
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your dataloader normalize the image in the range(0,1) so when you plot them why are they giving the real image . I mean to say why the image didn't get distorted after changing the pixel values.
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How to make two models and predict the outcome.....like in training dataset contain two type data normal and differents......so while predicting how to known it's normal or different
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what good is a free course if they dont teach basic math operation , nothing major was covered , but its better than nothing so use it to fullest and learn urself
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hey, could you do more of these in class kaggle pytorch competitions. I just got to know about it so late and there is just one day to go to compete :(
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One thing, I want to clarify 1:47:33 that these are not proteins but cell organelles and some are composed of proteins and other macromolecules
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