TensorFlow Keras Tutorials 🎯

Welcome to the TensorFlow Keras tutorials! Keras is a high-level API for building and training deep learning models, and it integrates seamlessly with TensorFlow. Here's a quick guide to get you started:

What is Keras? 🧠

Keras simplifies the process of creating neural networks by providing a user-friendly interface. It supports both convolutional networks and recurrent networks, and it can run on top of TensorFlow, CNTK, or Theano.

Ease of Use: Keras abstracts complex operations into simple, modular layers.
Flexibility: You can customize models with custom layers and loss functions.
Speed: Built-in tools for rapid prototyping and experimentation.

Getting Started with TensorFlow & Keras 📚

Install TensorFlow
```
pip install tensorflow
```

Import Keras

import tensorflow as tf
from tensorflow.keras import layers, models

Build a Simple Model

model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(32,)),
    layers.Dense(10, activation='softmax')
])

Key Features of Keras 🌟

Modular Design: Layers are building blocks that can be stacked.
Pre-built Layers: Includes activation functions, optimizers, and loss functions.
Scalability: Works with both small and large datasets.

Explore More 🚀

For a deeper dive into Keras concepts, check out our Keras Introduction Tutorial. You can also experiment with different models using the TensorFlow Playground tool.

Visual Aids 📷

Happy learning! 🌱