Intermediate Deep Learning with PyTorch

Via DataCamp

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Overview

This intermediate-level course builds on the basics of deep learning and focuses on building more complex models using Py Torch. Learners will explore neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and implement them using Py Torch’s flexible APIs. Topics include optimization, transfer learning, and model evaluation techniques. Students will also work on hands-on projects, including building and training models on real-world datasets. This course is perfect for individuals looking to deepen their understanding...

Syllabus

Training Robust Neural Networks

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 mitigate the problems of unstable gradients so ubiquitous in neural nets training.

Images & Convolutional Neural Networks

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.

Sequences & Recurrent Neural Networks

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.

Multi-Input & Multi-Output Architectures

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.