Deep Learning

What is deep learning?

Deep learning is an AI function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It is an essential element of data science, including statistics and predictive modeling. It is highly beneficial to data scientists to collect, analyze, and interpret large amounts of data; deep learning makes this process faster and easier.

Which methods are used to create strong deep learning models?

• Learning rate decay: The learning rate is a hyperparameter, a factor that defines the system or set conditions for its operation before the learning process. It controls how much change the model experiences in response to the estimated error every time the model weights are altered. Too high learning rates may result in unstable training processes or learning a suboptimal set of weights. Learning rates that are too small may produce a lengthy training process that can get stuck.
• Transfer learning:This process involves perfecting a previously trained model; it requires an interface to the internals of a preexisting network. First, users feed the existing network new data containing previously unknown classifications. Once adjustments are made to the network, new tasks can be performed with more specific categorizing abilities. This method has the advantage of requiring much less data than others, thus reducing computation time to minutes or hours.
• Training from scratch: This method requires a developer to collect an extensive, labeled data set and configure a network architecture to learn the features and model. This technique is beneficial for new applications and applications with many output categories. However, it is a less common approach, as it requires excessive amounts of data, causing training to take days or weeks.
• Dropout: This method attempts to solve the problem of overfitting in networks with large amounts of parameters by randomly dropping units and their connections from the neural network during training. It has been proven that the dropout method can improve the performance of neural networks on supervised learning tasks in areas such as speech recognition, document classification, and computational biology.

What are the limitations of deep learning?

• The deep learning models are they learn through observations and know what is in the data in which they are trained.
• Bias in predictions is a big issue for deep learning models.
• Graphics Processing Units (GPUs) used to improve efficiency and decrease time consumption are expensive and use vast amounts of energy.
• Deep learning requires large amounts of data. Furthermore, the more powerful and accurate models will need more parameters, which, in turn, require more data.
• Once trained, deep learning models become inflexible and cannot handle multitasking. They can deliver efficient and accurate solutions but only to one specific problem. Even solving a similar problem would require retraining the system.