Develop a handwritten digit recognition system in PyTorch. Then, use data preprocessing skills to load data appropriately for use in models. Develop a neural network using PyTorch and write a training loop that trains the model with the loaded data. Lastly, apply advanced training techniques to improve accuracy on the test set.

1. Deep Learning

   Explain the difference between artificial intelligence, machine learning, and deep learning. Recognize the power of deep learning by reviewing popular examples of deep learning applications

2. Minimizing the Error Function with Gradient Descent

   Use PyTorch to preprocess data and use maximum likelihood, cross-entropy, and probability to measure model performance. Apply gradient descent to minimize error. Implement a backpropagation algorithm and identify key components of perceptrons.

3. Introduction to Neural Networks

   Explain essential concepts in neural networks and design neural network architectures. Distinguish between problems based on the objective of the model. Implement appropriate architectures for model objectives

4. Training Neural Networks

   Define a loss function and optimization method to train a neural network. Distinguish between overfitting and underfitting, and identify the causes of each. Optimize the training process using early stopping, regularication, dropout, learning rate decay, and momentum. Distinguish between batch and stochastic gradient descent and build a neural network with PyTorch and run data through it. Test and validate a trained network to ensure it generalizes.

Combine CNN and RNN knowledge to build a deep learning model that produces captions given an input image. Image captioning requires that learners create a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language. In this project, you will implement these cutting-edge deep learning architectures.

1. Advanced CNN Architectures

   Describe advances in CNN architectures. Understand region-based CNN’s, Fast R-CNN, and Faster-R-CNN, which allow for fast, localized object recognition in images.

2. YOLO

   Understand grid, sliding windows, and bounding boxes in object detection. Implement YOLO, a real-time object detection algorithm.

3. RNN’s

   Understand how recurrent neural networks learn from ordered sequences of data. Identify RNN applications in deep learning. Understand how feedforward and backpropagation through time work as well as RNN unfolded model.

4. Long Short-Term Memory Networks (LSTMs)

   Explore how memory can be incorporated into a deep learning model and implement long short-term memory networks.

5. Hyperparameters

   Refresh important hyperparameters such as learning rate, epochs, and layer and understand hyperparameters in RNN.

6. Optional: Attention Mechanisms

   Understand how attention allows models to focus on a specific piece of input data. Describe where attention is useful in natural language and computer vision applications. Describe attention and its encoder and decoder that empower applications like text translation. Understand basic attention methods like additive attention, Bahdanau and Luong attention

7. Image Captioning

   Understand image captioning and tokenize captions and words. Combine CNNs and RNNs to build a complex captioning model.

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