Be Prepared For An AI Takeover In 2021 By Learning These Key Terms

This article is a list of terminologies defined in a simple manner that enables easy recall.

Feel free to use this list to refresh on keywords and definitions on your way to an interview.

Or perhaps you are already a descent Data Scientist or Machine learning expert, but you would like to revisit your foundations. Or you want to pass the time.

Either way by the end of this article, you will be preapared to understand the language of our yet to be overlords.

Key Terms

1. Neuron: In the world of deep learning and machine learning, a neuron can be described as a processing unit that is one of the fundamental building blocks of a neural network.

2. Weights: These are the strength of interneuron connections. They are the critical element in the storing of knowledge within neural networks.

3. Neural Network: A collection of neurons (processing units) in a manner that enables the retention of knowledge. They can also be described as parallel distributed processors.

4. Machine learning: Is the science of the implementation of computer algorithms or instructions that are orchestrated to learn from data, where the algorithm is utilized for a task correlated with the data it learned from.

5. Deep learning: This is an area of machine learning where algorithms leverage the utilization of several layers of neural networks to extract richer features from input data. Examples of deep learning techniques are Convolution Neural Networks(CNN) and Recurrent Neural Networks(RNN)

6. Supervised learning: This is the training of a machine learning algorithm with data annotated with labels. The annotation of data is typically provided by an expert system, such as a human or external system. The task of classification is an example of a supervised learning task.

7. Unsupervised learning: Algorithms designed to tackle this type of learning have self-organizing characteristics built into them. These algorithms self organize data based on patterns detected in data without the involvement of an expert system.

8. Semi-supervised learning: Machine learning algorithms that are semi-supervised consist of both unlabeled and labelled training data. The frequency of labelled data in the distribution of the training dataset is usually on a smaller scale in comparison to unlabeled training data.

9. Reinforcement learning: This is a type of Machine learning technique that involves defined programs that are referred to as agents. These agents are placed in an environment and are governed by the notion of the increase of rewards through interactions with the environment. The agents are designed to aim to accumulate rewards where possible. There is also the form of negative rewards or penalties. The agent task is to improve its governing system to collect rewards over time and avoid penalties.

10. Batch Learning: This is a method of the presentation of training data to a machine learning algorithm’s network. Training data accumulated is fed to the machine learning algorithm all at once, and changes are made to the bias and the weights of the network being trained once all training data have been fed forward.

11. Online Learning: This method of presenting training data to the network is carried out incrementally. The training data is split into groups referred to as mini-batches, and once a mini-batch has been fed through the network, an update is made to the network’s weights and bias, then another mini-batch is then fed forward. This process is repeated until all mini-batches are passed through the network.

12. Generalization: Machine learning algorithms and models can be categorized based on the measurement of their performance on unseen data.

13. Model: This can be described as a mathematical representation of the generalized pattern observed in a dataset.

14. Instance-based learning (memory-based learning): A machine learning system can generalize based on observation of patterns within training data presented to the algorithm during the training phase. These patterns (instances) can be utilized to improve the generalization of the machine learning algorithm by calculating some similarity measurement scores based on new instances presented during testing with the instances used in training. The algorithm is said to make predictions on new instances based on previous instances of observations during training.

15. Model-based learning: An alternative method of implementing a system that can generalize as opposed to instance-based learning, is to use a generate a model based on a dataset and use the model to tackle tasks such as predictions.