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Archive | Deep Learning with PyTorch


Training Logistic Regression with Cross-Entropy Loss in PyTorch

In the previous session of our PyTorch series, we demonstrated how badly initialized weights can impact the accuracy of a classification model when mean square error (MSE) loss is used. We noticed that the model didn’t converge during training and its accuracy was also significantly reduced. In the following, you will see what happens if […]

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Text Generation with LSTM in PyTorch

Recurrent neural network can be used for time series prediction. In which, a regression neural network is created. It can also be used as generative model, which usually is a classification neural network model. A generative model is to learn certain pattern from data, such that when it is presented with some prompt, it can […]

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Photo by <a href="https://unsplash.com/photos/la0WP7U3-AM">Johnny Wong</a>. Some rights reserved.

Handwritten Digit Recognition with LeNet5 Model in PyTorch

A popular demonstration of the capability of deep learning techniques is object recognition in image data. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. In this post, you will discover how to develop a deep learning model to achieve near state-of-the-art performance on […]

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Photo by <a href="https://unsplash.com/photos/10py7Mvmf1g">Ken Cheung</a>. Some rights reserved.

Visualizing a PyTorch Model

PyTorch is a deep learning library. You can build very sophisticated deep learning models with PyTorch. However, there are times you want to have a graphical representation of your model architecture. In this post, you will learn: How to save your PyTorch model in an exchange format How to use Netron to create a graphical […]

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