Have you ever wondered why deep learning is often preferred over traditional machine learning?
Do you want to learn the key difference between these techniques that gives deep learning an edge over machine learning?
Do you want to train your own deep learning without the need to code?
If you answered yes to any of these questions, you are reading the right article.
Traditional machine learning techniques often rely on feature engineering, which is the process of manually extracting relevant features from raw data to be used as inputs for a model. This can include techniques such as using Gabor filters to detect texture in images. Gabor can generate an infinite number of features, but the key is finding the correct parameters for the kernel to extract the appropriate features. These parameters include wavelength (lambda), orientation (theta), phase offset (phi), the standard deviation of the Gaussian envelope (sigma), and spatial aspect ratio (gamma). As shown in Figure 1, horizontal bars can be extracted by using a kernel with a theta value of pi/2, and vertical bars can be extracted using a kernel with a theta value of pi. However, in real-life images, there is added complexity that makes it difficult to determine which parameters will work effectively.
Even experienced engineers have difficulty determining the correct parameters for a specific problem. As a result, it is common to generate a large number of features by varying all parameters and allowing the machine learning algorithm to determine which ones are the most important. Although this method is effective, it is not an efficient way to tackle challenges using machine learning, especially when there is a large amount of training data available.
Deep learning, on the other hand, is a form of machine learning that uses neural networks to automatically learn features from raw data. The layers of a neural network can be thought of as a hierarchy of features, where each layer learns increasingly complex features. See Figure 2.
For example, in the VGG16 model that has been trained on the ImageNet dataset, the early layers learn basic features such as edges and textures, while the later layers learn more complex features such as object parts and entire objects. In Figure 3, a variety of features are displayed that have been extracted from the same image as seen in Figure 1, utilizing the second convolutional block from the VGG16 network that has been pre-trained on the Imagenet dataset. No feature engineering was needed as the model had already been trained on a vast number of images. Deep learning allows for both learning the features and using them. These features can be utilized as input for traditional machine learning techniques such as Random Forest or further fine-tuned for this specific application through additional deep learning training.
To put it simply, deep learning is favored over traditional machine learning due to its superior ability to perform feature learning eliminating the need for human-designed feature engineering. Furthermore, deep learning can deal with complex relationships between features and the target variable, making it especially effective when there is a large amount of training data.
Due to its advantages in feature learning and handling complex relationships, deep learning is rapidly gaining popularity in the field of scientific image analysis. With the development of products like APEER, even those with no coding skills can train custom deep-learning models with a relatively small number of training images. APEER makes the process of deep learning training simple and accessible, enabling more people to leverage the powerful capabilities of deep learning for scientific image analysis.
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