)In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve, explain how these terms relate to AI and why it’s important to know about them.
Show Notes:
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AI Glossary
Glossary Series: Training Data, Epoch, Batch, Learning Curve
Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer
Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU
Glossary Series: Perceptron
Glossary Series: Hidden Layer, Deep Learning
Glossary Series: Loss Function, Cost Function & Gradient Descent
Glossary Series: Backpropagation, Learning Rate, Optimizer
Glossary Series: Feed-Forward Neural Network
Glossary Series: OpenAI, GPT, DALL-E, Stable Diffusion
Glossary Series: Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech Recognition
AI Glossary Series – Machine Learning, Algorithm, Model
AI Glossary Series – Model Tuning and Hyperparameter
AI Glossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance Tradeoff
Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary
Continue reading AI Today Podcast: AI Glossary Series – Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve at Cognilytica.)
