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A Bhat edited this page May 31, 2020
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- Training | Inference
- Model : Parameter | Layer | Accuracy
- Neural network : Neuron | Perceptron | Weight | Bias | Activation function | Depth
- Dataset : Feature | Label
- Data formats : FP16 | FP32 | bfloat16 | INT8
- Networks: CNN | RNN
- Learning types: Supervised | Unsupervised | Federated | Reinforcement
- Supervised Learning : Classification | Regression
- Unsupervised Learning : Clustering
- Example : Labeled example | Unlabeled example
- Loss : Loss function
- Passes : Backpropagation | Forward pass | Backward pass
- Gradient Descent algorithm: SGD | Batch | Mini-batch
- Batch : Mini-Batch | Batch Size | Iteration
- Hyperparameter : Learning rate | Epoch
- Overfitting | Underfitting
- Regularization : Dropout
- Quantization
- Image classification: AlexNet | VGG | ResNet | Inception | EfficientNet
- NLP: BERT
- MLPerf : MLPerf Inference | MLPerf Training
- Data sets : MNIST | ImageNet | COCO | PASCAL VOC | CIFAR
- Frameworks: TensorFlow | PyTorch | MXNET | Keras | ONNX
- Inference engines: ArmNN | OpenVINO | TensorRT | TensorFlow Lite | Core ML
- Libraries: cuDNN | MKL-DNN | Arm Compute Library
- BLAS libraries: Eigen | OpenBLAS | MKL | BLIS | cuBLAS
- Compiler tech: MLIR | TensorFlow XLA | TVM