en/resources/deep-learning/keras-neural-networks

Keras Neural Networks: A Comprehensive Guide

🧠 Introduction to Keras
Keras is an open-source deep learning framework that simplifies building and training neural networks. It runs on top of TensorFlow, Theano, or CNTK, making it highly flexible for various machine learning tasks.

🔧 Key Features

• User-Friendly API: Easy to prototype and experiment with models
• Modular Architecture: Stack layers like building blocks
• Prebuilt Layers: Dense, Conv2D, LSTM, and more
• Support for Both CNNs and RNNs: Handles image and sequence data

📊 Getting Started

1. Install Keras: pip install keras
2. Import libraries:

   from keras.models import Sequential  
   from keras.layers import Dense  
   

3. Build a simple network:

   model = Sequential()  
   model.add(Dense(32, activation='relu', input_dim=100))  
   model.add(Dense(1, activation='softmax'))  
   model.compile(optimizer='adam', loss='binary_crossentropy')  
   

📚 Resources

• Understanding Neural Networks with Keras
• Keras Documentation
• Deep Learning with Python (Book)

💡 Tips for Effective Model Training

• Use model.summary() to visualize the network structure
• Experiment with different activation functions (ReLU, Tanh, etc.)
• Regularly save checkpoints with model.save()

📌 Next Steps
Explore Keras tutorials to dive deeper into practical implementations!