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Python Tutorials
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Table Of Contents
Python Tutorials
keyboard_arrow_down
Getting Started
keyboard_arrow_down
Crash Course
keyboard_arrow_down
Manipulate data with
ndarray
Create a neural network
Automatic differentiation with
autograd
Train the neural network
Predict with a pre-trained model
Use GPUs
Moving to MXNet from Other Frameworks
keyboard_arrow_down
PyTorch vs Apache MXNet
Gluon: from experiment to deployment
Logistic regression explained
MNIST
Packages
keyboard_arrow_down
Automatic Differentiation
Gluon
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Blocks
keyboard_arrow_down
Custom Layers
Customer Layers (Beginners)
Hybridize
Initialization
Parameter and Block Naming
Layers and Blocks
Parameter Management
Saving and Loading Gluon Models
Activation Blocks
Image Tutorials
keyboard_arrow_down
Image Augmentation
Handwritten Digit Recognition
Using pre-trained models in MXNet
Losses
keyboard_arrow_down
Custom Loss Blocks
Kullback-Leibler (KL) Divergence
Loss functions
Text Tutorials
keyboard_arrow_down
Google Neural Machine Translation
Machine Translation with Transformer
Training
keyboard_arrow_down
MXNet Gluon Fit API
Trainer
Learning Rates
keyboard_arrow_down
Learning Rate Finder
Learning Rate Schedules
Advanced Learning Rate Schedules
Normalization Blocks
KVStore
keyboard_arrow_down
Distributed Key-Value Store
NDArray
keyboard_arrow_down
An Intro: Manipulate Data the MXNet Way with NDArray
NDArray Operations
NDArray Contexts
Gotchas using NumPy in Apache MXNet
Tutorials
keyboard_arrow_down
CSRNDArray - NDArray in Compressed Sparse Row Storage Format
RowSparseNDArray - NDArray for Sparse Gradient Updates
Train a Linear Regression Model with Sparse Symbols
Sparse NDArrays with Gluon
ONNX
keyboard_arrow_down
Fine-tuning an ONNX model
Running inference on MXNet/Gluon from an ONNX model
Importing an ONNX model into MXNet
Export ONNX Models
Optimizers
Visualization
keyboard_arrow_down
Visualize networks
Performance
keyboard_arrow_down
Compression
keyboard_arrow_down
Deploy with int-8
Float16
Gradient Compression
GluonCV with Quantized Models
Accelerated Backend Tools
keyboard_arrow_down
Intel MKL-DNN
keyboard_arrow_down
Quantize with MKL-DNN backend
Install MXNet with MKL-DNN
TensorRT
keyboard_arrow_down
Optimized GPU Inference
Use TVM
Profiling MXNet Models
Using AMP: Automatic Mixed Precision
Deployment
keyboard_arrow_down
Export
keyboard_arrow_down
Exporting to ONNX format
Export Gluon CV Models
Save / Load Parameters
Inference
keyboard_arrow_down
Deploy into C++
Deploy into a Java or Scala Environment
Real-time Object Detection with MXNet On The Raspberry Pi
Run on AWS
keyboard_arrow_down
Run on an EC2 Instance
Run on Amazon SageMaker
MXNet on the Cloud
Extend
keyboard_arrow_down
Custom Layers
Custom Numpy Operators
New Operator Creation
New Operator in MXNet Backend
Python API
keyboard_arrow_down
mxnet.ndarray
keyboard_arrow_down
ndarray
ndarray.contrib
ndarray.image
ndarray.linalg
ndarray.op
ndarray.random
ndarray.register
ndarray.sparse
ndarray.utils
mxnet.gluon
keyboard_arrow_down
gluon.Block
gluon.HybridBlock
gluon.SymbolBlock
gluon.Constant
gluon.Parameter
gluon.ParameterDict
gluon.Trainer
gluon.contrib
gluon.data
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data.vision
keyboard_arrow_down
vision.datasets
vision.transforms
gluon.loss
gluon.model_zoo.vision
gluon.nn
gluon.rnn
gluon.utils
mxnet.autograd
mxnet.initializer
mxnet.optimizer
mxnet.lr_scheduler
mxnet.metric
mxnet.kvstore
mxnet.symbol
keyboard_arrow_down
symbol
symbol.contrib
symbol.image
symbol.linalg
symbol.op
symbol.random
symbol.register
symbol.sparse
mxnet.module
mxnet.contrib
keyboard_arrow_down
contrib.autograd
contrib.io
contrib.ndarray
contrib.onnx
contrib.quantization
contrib.symbol
contrib.tensorboard
contrib.tensorrt
contrib.text
mxnet
keyboard_arrow_down
mxnet.attribute
mxnet.base
mxnet.callback
mxnet.context
mxnet.engine
mxnet.executor
mxnet.executor_manager
mxnet.image
mxnet.io
mxnet.kvstore_server
mxnet.libinfo
mxnet.log
mxnet.model
mxnet.monitor
mxnet.name
mxnet.notebook
mxnet.operator
mxnet.profiler
mxnet.random
mxnet.recordio
mxnet.registry
mxnet.rtc
mxnet.test_utils
mxnet.torch
mxnet.util
mxnet.visualization
Table Of Contents
Python Tutorials
keyboard_arrow_down
Getting Started
keyboard_arrow_down
Crash Course
keyboard_arrow_down
Manipulate data with
ndarray
Create a neural network
Automatic differentiation with
autograd
Train the neural network
Predict with a pre-trained model
Use GPUs
Moving to MXNet from Other Frameworks
keyboard_arrow_down
PyTorch vs Apache MXNet
Gluon: from experiment to deployment
Logistic regression explained
MNIST
Packages
keyboard_arrow_down
Automatic Differentiation
Gluon
keyboard_arrow_down
Blocks
keyboard_arrow_down
Custom Layers
Customer Layers (Beginners)
Hybridize
Initialization
Parameter and Block Naming
Layers and Blocks
Parameter Management
Saving and Loading Gluon Models
Activation Blocks
Image Tutorials
keyboard_arrow_down
Image Augmentation
Handwritten Digit Recognition
Using pre-trained models in MXNet
Losses
keyboard_arrow_down
Custom Loss Blocks
Kullback-Leibler (KL) Divergence
Loss functions
Text Tutorials
keyboard_arrow_down
Google Neural Machine Translation
Machine Translation with Transformer
Training
keyboard_arrow_down
MXNet Gluon Fit API
Trainer
Learning Rates
keyboard_arrow_down
Learning Rate Finder
Learning Rate Schedules
Advanced Learning Rate Schedules
Normalization Blocks
KVStore
keyboard_arrow_down
Distributed Key-Value Store
NDArray
keyboard_arrow_down
An Intro: Manipulate Data the MXNet Way with NDArray
NDArray Operations
NDArray Contexts
Gotchas using NumPy in Apache MXNet
Tutorials
keyboard_arrow_down
CSRNDArray - NDArray in Compressed Sparse Row Storage Format
RowSparseNDArray - NDArray for Sparse Gradient Updates
Train a Linear Regression Model with Sparse Symbols
Sparse NDArrays with Gluon
ONNX
keyboard_arrow_down
Fine-tuning an ONNX model
Running inference on MXNet/Gluon from an ONNX model
Importing an ONNX model into MXNet
Export ONNX Models
Optimizers
Visualization
keyboard_arrow_down
Visualize networks
Performance
keyboard_arrow_down
Compression
keyboard_arrow_down
Deploy with int-8
Float16
Gradient Compression
GluonCV with Quantized Models
Accelerated Backend Tools
keyboard_arrow_down
Intel MKL-DNN
keyboard_arrow_down
Quantize with MKL-DNN backend
Install MXNet with MKL-DNN
TensorRT
keyboard_arrow_down
Optimized GPU Inference
Use TVM
Profiling MXNet Models
Using AMP: Automatic Mixed Precision
Deployment
keyboard_arrow_down
Export
keyboard_arrow_down
Exporting to ONNX format
Export Gluon CV Models
Save / Load Parameters
Inference
keyboard_arrow_down
Deploy into C++
Deploy into a Java or Scala Environment
Real-time Object Detection with MXNet On The Raspberry Pi
Run on AWS
keyboard_arrow_down
Run on an EC2 Instance
Run on Amazon SageMaker
MXNet on the Cloud
Extend
keyboard_arrow_down
Custom Layers
Custom Numpy Operators
New Operator Creation
New Operator in MXNet Backend
Python API
keyboard_arrow_down
mxnet.ndarray
keyboard_arrow_down
ndarray
ndarray.contrib
ndarray.image
ndarray.linalg
ndarray.op
ndarray.random
ndarray.register
ndarray.sparse
ndarray.utils
mxnet.gluon
keyboard_arrow_down
gluon.Block
gluon.HybridBlock
gluon.SymbolBlock
gluon.Constant
gluon.Parameter
gluon.ParameterDict
gluon.Trainer
gluon.contrib
gluon.data
keyboard_arrow_down
data.vision
keyboard_arrow_down
vision.datasets
vision.transforms
gluon.loss
gluon.model_zoo.vision
gluon.nn
gluon.rnn
gluon.utils
mxnet.autograd
mxnet.initializer
mxnet.optimizer
mxnet.lr_scheduler
mxnet.metric
mxnet.kvstore
mxnet.symbol
keyboard_arrow_down
symbol
symbol.contrib
symbol.image
symbol.linalg
symbol.op
symbol.random
symbol.register
symbol.sparse
mxnet.module
mxnet.contrib
keyboard_arrow_down
contrib.autograd
contrib.io
contrib.ndarray
contrib.onnx
contrib.quantization
contrib.symbol
contrib.tensorboard
contrib.tensorrt
contrib.text
mxnet
keyboard_arrow_down
mxnet.attribute
mxnet.base
mxnet.callback
mxnet.context
mxnet.engine
mxnet.executor
mxnet.executor_manager
mxnet.image
mxnet.io
mxnet.kvstore_server
mxnet.libinfo
mxnet.log
mxnet.model
mxnet.monitor
mxnet.name
mxnet.notebook
mxnet.operator
mxnet.profiler
mxnet.random
mxnet.recordio
mxnet.registry
mxnet.rtc
mxnet.test_utils
mxnet.torch
mxnet.util
mxnet.visualization
Blocks
¶
Custom Layers
Customer Layers (Beginners)
Hybridize
Initialization
Parameter and Block Naming
Layers and Blocks
Parameter Management
Saving and Loading Gluon Models
Activation Blocks
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