| Artificial Intelligence (AI) |
The simulation of human intelligence processes by machines, especially computer systems.
|
Link to Colab (TBD) |
General
|
| Machine Learning (ML) |
A subset of AI that enables systems to learn from data and improve from experience without
being explicitly programmed. |
Link to Colab (TBD) |
GeneralCore
|
| Deep Learning (DL) |
A subset of ML based on artificial neural networks with multiple layers (deep neural
networks). |
Link to Colab (TBD) |
MLNeural Networks
|
| Neural Network |
A series of algorithms that endeavor to recognize underlying relationships in a set of data
through a process that mimics the way the human brain operates. |
Link to Colab (TBD) |
DLCore
|
| Supervised Learning |
A type of ML where the model is trained on labeled data. |
Link to Colab (TBD) |
MLTraining
|
| Unsupervised Learning |
A type of ML where the model is trained on unlabeled data and must find patterns on its own.
|
Link to Colab (TBD) |
MLTraining
|
| Reinforcement Learning (RL) |
A type of ML where an agent learns to make decisions by performing actions and receiving
rewards or penalties. |
Link to Colab (TBD) |
MLTraining
|
| Natural Language Processing (NLP) |
A field of AI focused on the interaction between computers and humans through natural
language. |
Link to Colab (TBD) |
AILanguage
|
| Computer Vision (CV) |
A field of AI that enables computers to derive meaningful information from digital images,
videos and other visual inputs. |
Link to Colab (TBD) |
AIVision
|
| Large Language Model (LLM) |
A deep learning algorithm that can recognize, summarize, translate, predict and generate
text and other content based on knowledge gained from massive datasets. |
Link to Colab (TBD) |
NLPGenerative AI
|
| Generative AI |
A type of AI technology that can produce various types of content, including text, imagery,
audio and synthetic data. |
Link to Colab (TBD) |
AICreation
|
| Transformer |
A deep learning model that adopts the mechanism of self-attention, differentially weighting
the significance of each part of the input data. |
Link to Colab (TBD) |
DLNLP
|
| Overfitting |
A modeling error that occurs when a function is too closely fit to a limited set of data
points, performing well on training data but properly on new data. |
Link to Colab (TBD) |
MLError
|
| Underfitting |
A modeling error that occurs when a model cannot adequately capture the underlying structure
of the data. |
Link to Colab (TBD) |
MLError
|
| Bias |
The simplifying assumptions made by a model to make the target function easier to learn.
High bias can cause underfitting. |
Link to Colab (TBD) |
MLError
|
| Variance |
The amount that the estimate of the target function will change if different training data
was used. High variance can cause overfitting. |
Link to Colab (TBD) |
MLError
|
| Activation Function |
A function that defines the output of a node given an input or set of inputs, introducing
non-linearity to the network. |
Link to Colab (TBD) |
DLMath
|
| Backpropagation |
An algorithm used for supervised learning of artificial neural networks using gradient
descent. |
Link to Colab (TBD) |
DLTraining
|
| Epoch |
One complete pass of the training dataset through the algorithm. |
Link to Colab (TBD) |
MLTraining
|
| Loss Function |
A method of evaluating how well your algorithm models your dataset. |
Link to Colab (TBD) |
MLMath
|