What is Machine Learning (ML)?
Machine learning is a branch of artificial intelligence that gives computers the ability to learn without being explicitly programmed. Machine learning algorithms are used for prediction, classification, and analysis. The automated accounting platform of Portali utilizes this type of technology.
Machine learning algorithms can be broadly classified into two categories: supervised and unsupervised. Supervised machine learning requires labeled training data, while unsupervised machine learning does not require labeled training data.
What is Deep Learning (DL)?
Deep learning is a subset of machine learning that uses neural networks to train and improve itself. In other words, it's a type of AI that allows computers to learn from data without being explicitly programmed.
The first step in deep learning is for the computer to be trained on a large dataset (for example thousands or millions) with labeled examples - that is, images tagged with their corresponding labels (such as "cat" or "dog"). The next step involves feeding new data into the model so it can make predictions about what kind of object should be labeled with each image. This process continues until accuracy reaches an acceptable threshold level (e.g., 90% accuracy).
The two technologies are similar in the way that they both use machine learning algorithms to solve problems. However, they differ in their approach to AI. Deep learning is based on neural networks and uses multiple layers of processing units to learn from data and make predictions about future outcomes. Machine learning uses statistical models for prediction tasks like classification or regression analysis but does not require any specific structure or topology for its models. It also doesn't require you to understand how your model works; instead, it can be trained using large amounts of labeled data so that it can learn from experience without being explicitly programmed with rules or constraints.
Deep Learning is best suited for problems where there's a lot of unstructured data available but little guidance as far as what kind of information should be extracted from those images - such as recognizing objects within an image. Machine Learning requires less training data than Deep Learning because its algorithms are more flexible when making predictions based on sparse datasets.