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Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
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Comparison of ML Frameworks
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Containers
Convergence
Convolutional Neural Network (CNN)
Data Science vs Machine Learning vs Deep Learning
Datasets and Machine Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Generative Adversarial Network (GAN)
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Model Drift & Decay
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Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
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Supervised, Unsupervised, & Reinforcement Learning
Synthetic Data
Tensor Processing Unit (TPU)
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MIT Schwarzman College of Computing
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The team at MIT publishes research papers about the management of Artificial Intelligence.
April 9, 2024
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