Wrap-up

1. Wrap-up

Congratulations on completing the course!

2. What you learned

In Chapter 1, we discussed object-oriented programming and how it's used to construct PyTorch Datasets and models. You also learned about different optimizers, and how to combat the problems of vanishing and exploding gradients using weight initialization, activation functions, and batch normalization. In Chapter 2, you learned to handle images in PyTorch to train and evaluate image classifying convolutional neural networks. You also augmented the image data to improve the classification results. In Chapter 3, you tackled sequential data. You learned how to process it and how to construct a PyTorch dataset from it. You also got familiar with popular recurrent architectures, including LSTM and GRU models, which you have trained and evaluated. Finally, in Chapter 4, you learned to build models with multiple inputs and multiple outputs, and apply loss weighting to multi-output models to put more focus on one of the tasks.

3. What's next?

You can now build a wide range of deep learning models to solve various problems. But the journey doesn't end here! Here are a couple of next steps you could take on your journey. First, the transformers model. This type of architecture was first developed for natural language processing but today it finds applications in other areas such as computer vision. Transformers also stand behind large language models like ChatGPT. Second, self-supervised learning. It's a training method in which the model creates labels from unlabeled data. It's increasingly popular in many domains. You can learn more about various data science topics through other DataCamp courses.

4. Congratulations and good luck!

Once again, congratulations! I hope the knowledge and skills you've gained will help you build a variety of robust neural networks in PyTorch!

This exercise is part of the course

Intermediate Deep Learning with PyTorch

IntermediateSkill Level

4.8+

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Learn how to train neural networks in a robust way. In this chapter, you will use object-oriented programming to define PyTorch datasets and models and refresh your knowledge of training and evaluating neural networks. You will also get familiar with different optimizers and, finally, get to grips with various techniques that help mitigate the problems of unstable gradients so ubiquitous in neural nets training.

Exercise 1: PyTorch and object-oriented programming Exercise 2: PyTorch Dataset Exercise 3: PyTorch DataLoader Exercise 4: PyTorch Model Exercise 5: Optimizers, training, and evaluation Exercise 6: Training loop Exercise 7: Optimizers Exercise 8: Model evaluation Exercise 9: Vanishing and exploding gradients Exercise 10: Initialization and activation Exercise 11: Activations: ReLU vs. ELU Exercise 12: Batch Normalization

Train neural networks to solve image classification tasks. In this chapter, you will learn how to handle image data in PyTorch and get to grips with convolutional neural networks (CNNs). You will practice training and evaluating an image classifier while learning about how to improve the model performance with data augmentation.

Exercise 1: Handling images with PyTorch Exercise 2: Image dataset Exercise 3: Data augmentation Exercise 4: Data augmentation in PyTorch Exercise 5: Convolutional Neural Networks Exercise 6: The convolutional layer Exercise 7: Building convolutional networks Exercise 8: Training image classifiers Exercise 9: Choosing augmentations Exercise 10: Dataset with augmentations Exercise 11: Image classifier training loop Exercise 12: Evaluating image classifiers Exercise 13: Multi-class model evaluation Exercise 14: Analyzing metrics per class

Build and train recurrent neural networks (RNNs) for processing sequential data such as time series, text, or audio. You will learn about the two most popular recurrent architectures, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as how to prepare sequential data for model training. You will practice your skills by training and evaluating a recurrent model for predicting electricity consumption.

Exercise 1: Handling sequences with PyTorch Exercise 2: Generating sequences Exercise 3: Sequential Dataset Exercise 4: Recurrent Neural Networks Exercise 5: Sequential architectures Exercise 6: Building a forecasting RNN Exercise 7: LSTM and GRU cells Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: LSTM network Exercise 10: GRU network Exercise 11: Training and evaluating RNNs Exercise 12: RNN training loop Exercise 13: Evaluating forecasting models

Build multi-input and multi-output models, demonstrating how they can handle tasks requiring more than one input or generating multiple outputs. You will explore how to design and train these models using PyTorch and delve into the crucial topic of loss weighting in multi-output models. This involves understanding how to balance the importance of different tasks when training a model to perform multiple tasks simultaneously.

Exercise 1: Multi-input models Exercise 2: Two-input dataset Exercise 3: Two-input model Exercise 4: Training two-input model Exercise 5: Multi-output models Exercise 6: Two-output Dataset and DataLoader Exercise 7: Two-output model architecture Exercise 8: Training multi-output models Exercise 9: Evaluation of multi-output models and loss weighting Exercise 10: Multi-output model evaluation Exercise 11: Loss weighting Exercise 12: Wrap-up

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