Sequence Prediction with GRU Model in PyTorch

     Gated Recurrent Unit (GRU) is a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data efficiently. It is an extension of traditional RNNs and shares similarities with LSTM (Long Short-Term Memory) networks.

    In this tutorial, we'll briefly learn about GRU model and how to implement sequential data prediction with GRU in PyTorch covering the following topics:
  1. Introduction to GRU
  2. Data preparing
  3. Model definition and training
  4. Prediction
  5. Conclusion

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Sequence Prediction with LSTM model in PyTorch

     Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to overcome the limitations of traditional RNNs in capturing long-range dependencies in sequential data. 

    In this tutorial, we'll briefly learn about LSTM and how to implement an LSTM model with sequential data in PyTorch covering the following topics:
  1. Introduction to LSTM
  2. Data preparing
  3. Model definition and training
  4. Prediction
  5. Conclusion

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Introduction to Recurrent Neural Networks (RNNs) with PyTorch

    Recurrent Neural Network (RNN) is a type of neural network architecture designed for sequence modeling and processing tasks. Unlike feedforward neural networks, which process each input independently, RNNs have connections that allow them to combine information about previous inputs into their current computations. 

    In this tutorial, we'll briefly learn about RNNs and how to implement a simple RNN model with sequential data in PyTorch covering the following topics:

  1. Introduction to RNNs
  2. Data preparing
  3. Model definition and training
  4. Prediction
  5. Conclusion

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MNIST Image Classification with PyTorch

    In this tutorial, we'll learn how to build a convolutional neural network (CNN) using PyTorch to classify handwritten digits from the MNIST dataset. The MNIST dataset consists of 28x28 pixel grayscale images of handwritten digits (0-9), and the task is to correctly identify which digit is represented in each image. The tutorial covers:

  1. Preparing data
  2. Model definition
  3. Model training
  4. Model evaluation
  5. Prediction
  6. Conclusion

Understanding PyTorch Autograd

    Autograd is a key component for implementing  automatic differentiation. It allows us to compute gradients automatically for tensor operations, which is crucial for training neural networks efficiently using techniques like backpropagation. This tutorial will provide an overview of PyTorch Autograd, covering the following topics:

  1. Introduction to Autograd
  2. Autograd in model training
  3. Conclusion 

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Data Loading in PyTorch with DataLoader

    In PyTorch, a DataLoader is a tool that efficiently manages and loads data during the training or evaluation of machine learning models. It acts as a bridge between datasets and models, facilitating seamless data handling throughout the process. In this tutorial, we'll explore how to utilize PyTorch's DataLoader with synthetic and classical MNIST datasets, covering the following topics:

  1. Understanding DataLoader
  2. Usage with simple data
  3. Usage with MNIST Dataset
  4. Conclusion 

     Let's get started.