모두를 위한 딥러닝 - PyTorch
Offered By: YouTube
Course Description
Overview
Syllabus
Docker Instruction.
[PyTorch] Lab-01-1 Tensor Manipulation 1.
[PyTorch] Lab-01-2 Tensor Manipulation 2.
[PyTorch] Lab-02 Linear regression.
[PyTorch] Lab-03 Deeper Look at GD.
[PyTorch] Lab-04-1 Multivariable Linear regression.
[PyTorch] Lab-04-2 Loading Data.
[PyTorch] Lab-05 Logistic Regression.
[PyTorch] Lab-06 Softmax Classification.
[PyTorch] Lab-07-1 Tips.
[PyTorch] Lab-07-2 MNIST Introduction.
[PyTorch] Lab-08-1 Perceptron.
[PyTorch] Lab-08-2 Multi Layer Perceptron.
[PyTorch] Lab-09-1 ReLU.
[PyTorch] Lab-09-2 Weight initialization.
[PyTorch] Lab-09-3 Dropout.
[PyTorch] Lab-09-4 Batch Normalization.
[PyTorch] Lab-10-0 Convolution Neural Networkintro.
[PyTorch] Lab-10-1 Convolution.
[PyTorch] Lab-10-2 mnist cnn.
[PyTorch] Lab-10-3 visdom.
[PyTorch] Lab-10-4-1 ImageFolder1.
[PyTorch] Lab-10-4-2 ImageFolder2.
[PyTorch] Lab-10-5 Advance CNN(VGG).
[PyTorch] Lab-10-6-1 Advanced CNN(RESNET)1.
[PyTorch] Lab-10-6-2 Advanced CNN(RESNET)2.
[PyTorch] Lab-10-7 Next step of CNN.
[PyTorch] Lab-11-0 RNN intro.
[PyTorch] Lab-11-1 RNN basics.
[PyTorch] Lab-11-2 RNN hihello and charseq.
[PyTorch] Lab-11-3 RNN Long sequence.
[PyTorch] Lab-11-4 RNN timeseries.
[PyTorch] Lab-11-5 RNN seq2seq.
[PyTorch] Lab-11-6 RNN PackedSequence.
Taught by
Deep Learning Zero To All
Related Courses
Practical Predictive Analytics: Models and MethodsUniversity of Washington via Coursera Deep Learning Fundamentals with Keras
IBM via edX Introduction to Machine Learning
Duke University via Coursera Intro to Deep Learning with PyTorch
Facebook via Udacity Introduction to Machine Learning for Coders!
fast.ai via Independent