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Learn By Example: PyTorch
You, This Course and Us
You, This Course and Us (2:46)
Course Materials
Introduction To PyTorch And Neural Networks
Overview (2:23)
Neurons And Neural Networks (8:35)
Introducing PyTorch (6:43)
Installation And Setup (1:41)
The Computation Graph (4:06)
Gradient Descent (4:37)
Forward And Backward Passes (1:59)
PyTorch Tensors
PyTorch Tensors (2:57)
PyTorch Tensors Implementation - I (5:56)
PyTorch Tensors Implementation - II (4:13)
PyTorch Tensors Implementation - III (10:15)
Gradient Descent And Autograd
Gradients, A Vector Of Partial Derivatives (5:50)
Autograd (4:43)
Reverse Mode Auto Differentiation (9:51)
Linear Regression Using Autograd (7:00)
Regression and Classification
Regression To Predict Air Quality (7:13)
Regression To Predict Air Quality - continued (6:37)
Optimizers (2:34)
Neural Networks For Classification (4:45)
Classification To Categorize Salary Categories (5:57)
Classification To Categorize Salary Categories - continued (7:39)
Convolutional Neural Networks In PyTorch
Viewing An Image (2:11)
Convolution (6:47)
Pooling (2:35)
CNN Architectures (2:25)
Batch Normalization (3:55)
Neural Networks To Classify House Numbers (4:44)
Neural Networks To Classify House Numbers - continued (7:24)
Recurrent Neural Networks In PyTorch
Recurrent Neurons (4:59)
Layers In An RNN (3:01)
Long/Short Term Memory (2:08)
Language Prediction Using RNNs (5:06)
Recurrent Neural Networks To Predict Languages Associated With Names (11:52)
Confusion Matrix (2:22)
Confusion Matrix For Classification (2:54)
Transfer Learning And Pre-trained Models
Transfer Learning (5:20)
Resnet-18 Model To Classify Fruits (6:45)
Resnet-18 Model To Classify Fruits - continued (9:18)
Summary (2:16)
Resnet-18 Model To Classify Fruits - continued
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