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A Bhat edited this page May 30, 2020
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- Training | Inference
- Supervised Learning : Classification | Regression
- Unsupervised Learning : Clustering
- Federated learning | Reinforcement learning
- Image classification | Object detection
- Model | Parameter | Prediction
- Dataset | Feature | Label | Example | Labeled example | Unlabeled example
- Neural network : Neuron | Perceptron | Weight | Bias | Activation function | Layer | Depth
- Batch | Iteration | Loss | Loss function | Backpropagation | Forward pass
- Batch | Mini-Batch | Batch Size | Iteration
- Hyperparameter : Learning rate | Epoch
- Overfitting | Underfitting
- Regularization | Dropout
- Quantization
- Networks: CNN | RNN
- Data formats : FP16 | FP32 | bfloat16 | INT8
- Image classification: AlexNet | VGG | ResNet | Inception | MobileNets | DenseNet | ResNeXt | 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