2 Transfer Learning Fundamentals
⇦ Back to Transfer learning and fine-tuning
⇦ 1 Introduction to Deep Learning 3 Fine-tuning Pre-trained Models ⇨
Introduction
Deep learning is a subset of machine learning that involves training artificial neural networks to learn from data. It has revolutionized the field of artificial intelligence and has led to breakthroughs in areas such as computer vision, natural language processing, and speech recognition. In this lesson, we will explore the basics of deep learning and its applications.Neural Networks
At the heart of deep learning are artificial neural networks, which are inspired by the structure and function of the human brain. Neural networks consist of layers of interconnected nodes, or neurons, that process information. The input layer receives data, and the output layer produces a prediction or classification. In between, there can be one or more hidden layers that extract features from the input data.Training Neural Networks
To train a neural network, we need a dataset with labeled examples. The network learns by adjusting the weights of the connections between neurons to minimize the difference between its predictions and the true labels. This process is called backpropagation, and it requires a loss function that measures the error between the predicted and true labels.Convolutional Neural Networks (CNNs)
Convolutional neural networks are a type of neural network that are particularly well-suited for image recognition tasks. They use convolutional layers to extract features from the input image, followed by pooling layers to reduce the dimensionality of the features. The output of the convolutional layers is then fed into one or more fully connected layers for classification.Recurrent Neural Networks (RNNs)
Recurrent neural networks are a type of neural network that are designed to process sequential data, such as time series or natural language. They use recurrent layers that maintain a hidden state over time, allowing them to capture temporal dependencies in the data. This makes them well-suited for tasks such as speech recognition and language translation.Conclusion
Deep learning has revolutionized the field of artificial intelligence and has led to breakthroughs in a wide range of applications. Neural networks, including convolutional and recurrent neural networks, are at the heart of deep learning and are used to extract features from data and make predictions. As the field continues to evolve, we can expect to see even more exciting developments in the future.Now let's see if you've learned something...
⇦ 1 Introduction to Deep Learning 3 Fine-tuning Pre-trained Models ⇨