Duration: Hours

PyTorch is an open-source Python library for deep learning developed and maintained by Facebook,became a popular framework among developers and researchers. It provides only limited visualization.

Training Mode: Online

Description

1. Tensor and Datasets

a). Overview of Tensors

b). Tensors 1D

c). Two-Dimensional Tensors

d). Differentiation in PyTorch

e). Simple Dataset

f). Dataset

g). Derivatives in PyTorch

2. Linear Regression

a). Linear Regression Prediction

b). Linear Regression Training

c). Loss

d). Gradient Descent

e). Linear Regression PyToch

f). PyTorch Linear Regression Training Slope and Bias

3. Linear Regression PyTorch Way

a). Stochastic Gradient Descent

b). Mini-Batch Gradient Descent

c). Optimization in PyTorch

4. Multiple Input Output Linear Regression

a). Multiple Linear Regression Prediction

b). Multiple Linear Regression Training

c). Linear Regression Multiple Outputs

d). Multiple Output Linear Regression

5. Logistic Regression for Classification

a). Linear Classifiers

b). Logistic Regression: Prediction

c). Bernoulli Distribution and Maximum Likelihood Estimation

d). Logistic Regression Cross Entropy Loss

6. Softmax Rergresstion

a). Softmax

b). Softmax Function:Using Lines to Classify Data

c). Softmax PyTorch

7. Shallow Neural Networks

a). What’s a Neural Network

b). More Hidden Neurons

c). Neural Networks with Multiple Dimensional Input

d). Multi-Class Neural Networks

e). Back propagation

f). Activation Functions

8. Deep Networks

a). Deep Neural Networks

b). Deeper Neural Networks : nn.ModuleList()

c). Dropout

d). Neural Network initialization Weights

e). Gradient Descent with Momentum

f). Batch Normalization

9. Convolutional Neural Network

a). Convolution

b). Activation Functions and Max Polling

c). Multiple Input and Output Channels

d). Convolutional Neural Network

e). Convolutional Neural Network

f). GPU in PyTorch

g). TORCH-VISION MODELS

 

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