Welcome to the Keras tutorials section! Here, you'll find comprehensive guides to help you master deep learning with Keras. Whether you're a novice or looking to refine your skills, these resources are tailored for your journey.

📚 Core Concepts of Keras

Keras is an open-source neural network library that simplifies building and training deep learning models. Key features include:

  • User-Friendly API: Easy to use for rapid prototyping.
  • Modular Design: Stack layers like building blocks.
  • Support for GPUs/TPUs: Accelerate training with hardware.
Keras_Tutorials

🧠 Neural Network Basics

Start with the fundamentals:

  • Layers: Dense, Conv2D, LSTM, and more.
  • Activation Functions: ReLU, Sigmoid, Tanh.
  • Loss Functions: Mean Squared Error, Cross-Entropy.

Check out our TensorFlow tutorials for deeper integration insights.

Neural_Network_Basics

📊 Data Preprocessing

Clean and prepare your data efficiently:

  • Normalize inputs using StandardScaler.
  • Split datasets with train_test_split.
  • Augment images via ImageDataGenerator.

Explore advanced data techniques to enhance model performance.

Data_Preprocessing

🛠️ Model Building & Training

Create models step-by-step:

  1. Define architecture with Sequential or Model.
  2. Compile with optimizers like Adam.
  3. Train using model.fit() and validate with model.evaluate().

Learn more about Keras model optimization to fine-tune your workflows.

Model_Building

🚀 Practical Examples

Try these hands-on projects:

  • MNIST Classification: Dense layers for handwritten digit recognition.
  • Image Generation: GAN models with Conv2D layers.
  • Sentiment Analysis: LSTM for text processing.

For detailed code samples, visit our Keras code repository.

Practical_Examples

📌 Next Steps

Ready to level up? Explore:

Happy coding! 🌟