5 Must-Know Machine Learning Terms
There are so many terms in Machine Learning that may confuse a Beginner and demotivate from learning further. Here are 5 must-know jargons of Machine Learning.
AutoML
An AutoML "solution" could include the tasks of data preprocessing, feature engineering, algorithm selection, algorithm architecture search, and hyperparameter tuning, or some subset or variation of these distinct tasks. Thus, automated machine learning can now be thought of as anything from solely performing a single task, such as automated feature engineering, all the way through to a fully-automated pipeline, from data preprocessing, to feature engineering, to algorithm selection, and so on.
BERT
BERT stands for Bidirectional Encoder Representations from Transformers and is a pretraining technique for natural language processing. What sets BERT apart from other language representations is the application of bidirectional training to the existing Transformer attention model. BERT pre-trains deep bidirectional representations of unlabeled text data on both left and right contexts, resulting in a language model that can be fine-tuned with only a single added layer. BERT has achieved a state of the art performance on a number of NLP tasks, including question answering and inference. Both BERT and the Transformer were developed by Google.
MLOps & AIOps
This term MLOps represents the latest best practices for developing and deploying machine learning models through effective collaborations with data scientists and IT professionals. Working within a well-defined development lifecycle should be quite welcoming for many data scientists, as formal and self-guided educational tracks tend to focus on the fundaments of AI and ML, with less familiarity provided for the requirements of production deployments.
Broadening this scope of leveraging artificial intelligence into the operations of an organization is AIOps that pulls in any and all machine learning technologies to extract meaningful insights from IT systems. This approach combines the intelligence of humans with that of AI algorithms to enhance IT teams in making better and faster decisions, responding to incidents in real-time, and developing optimized applications to facilitate more effective or automated business processes. With Gartner’s prediction that only 30% of large enterprise CIOs will be exclusively using AIOps to improve operations by 2023, there will be much more to be seen from the evolution of AIOps across IT organizations.
Deepfakes
Deep Fakes are fake images, video, or audio that have been created using advanced Deep Learning and Generative Adversarial Networks(GANs) technology. This technology is so advanced that the results are very realistic and are very hard to identify as fake.
Deepfakes first became prominent in the context of porn, with popular celebrities faces superimposed on top of adult videos, but recently the technology has progressed with apps like FakeApp and more recent open-source alternatives like FaceSwap and DeepFaceLab.
Graph Neural Networks
Data Scientists are swimming in data. Piles of data. Some data can be raw and unorganized as it streams in through a firehose. Other data can be neat and orderly (or curated to be so), formatted within manageable dimensions.
What about data that is more interrelated? Data can be connected to one other through dependent relationships. Interactions between users might impact purchasing decisions on e-commerce platforms. Chemical interactions for drug discovery are mapped out through complex interconnections of reactions. Social networks are formed and devolve through ever-changing, irregular, and unordered relationships.
These sorts of data relationships can be modeled as graphs with data points represented as nodes and relationships encoded through interconnecting links. Traditional machine learning approaches, including deep learning, need to be generalized further to compute within a non-Euclidean, graph-based space.
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