semvec/pypi-packages huggingface dataset:
network-yuanzhe 1.0.2
Build A Neural Network from Scratch
License: BSD License

Build A Neural Network From Scratch

The author builds a neural network structure from scratch. The neural network modules include:

1. Multiple Hidden Layers
2. Kaiming Initialization
3. Weight Decay
4. Batch Normalization
5. Dropout
6. Label Smoothing
7. ReLU Activation Function
8. Tanh Activation Function
9. GELU Activation Function
10. Softmax and Cross-entropy Loss
11. Momentum in SGD
12. Adam Optimizer
13. Mini-batch Training
neural-network-yuanzhe-jia 1.0.4
Build A Neural Network from Scratch
License: BSD License

Build A Neural Network From Scratch

The author builds a neural network structure from scratch. The neural network modules include:

1. Multiple Hidden Layers
2. Kaiming Initialization
3. Weight Decay
4. Batch Normalization
5. Dropout
6. Label Smoothing
7. ReLU Activation Function
8. Tanh Activation Function
9. GELU Activation Function
10. Softmax and Cross-entropy Loss
11. Momentum in SGD
12. Adam Optimizer
13. Mini-batch Training
neural-network-yuanzhe 1.0.3
Build A Neural Network from Scratch
License: BSD License

# Build A Neural Network From Scratch

### Introduction

- What is a neural network?
- What modules are included in a neural network?
- What can neural networks be used for?

These questions may have plagued those interested in neural networks. 
In this study, the author builds a neural network structure from scratch, and applies it to accomplish a multi-class classification task. 
The neural network modules include: 

1. Multiple Hidden Layers
2. Kaiming Initialization
3. Weight Decay
4. Batch Normalization
5. Dropout
6. Label Smoothing
7. ReLU Activation Function
8. Tanh Activation Function
9. GELU Activation Function
10. Softmax and Cross-entropy Loss
11. Momentum in SGD
12. Adam Optimizer
13. Mini-batch Training


### How to use

Step 1. 
Install [package](https://pypi.org/project/neural-network-yuanzhe/).
```
pip install neural-network-yuanzhe
```

Step 2. 
Import functions from the package.
```
from neural-network-yuanzhe import dense, batch_norm, dropout, model #neural network architecture
from neural-network-yuanzhe import relu, gelu, tanh #activation functions
from neural-network-yuanzhe import softmax, softmax_cross_entropy_loss, softmax_cross_entropy_derivatives #loss functions
from neural-network-yuanzhe import categorical_accuracy #evaluation metrics
```

Step 3. 
See an image classification example in the `experiements.ipynb` file to learn how to build a classifier based on the self-built neural network structure. 
The prediction loss, prediction accuracy and running time are adopted as the evaluation methods. 
Hyper-parameter tuning, comparative analysis and ablation studies are also included.