Deep Learning with TensorFlow

TensorFlow is an open-source machine learning framework developed by Google, widely used for building and training deep learning models. It provides a flexible ecosystem of tools, libraries, and community resources that allows researchers and developers to create machine learning pipelines and deploy models at scale.

Getting Started

Install TensorFlow
Use pip to install the latest version:
```
pip install tensorflow
```
📌 Visit our official documentation for installation guides and system requirements.
Basic Concepts
- Tensors: Multi-dimensional arrays that flow through the computational graph.
- Graphs: Visual representation of computations.
- Sessions: Execute the graph operations.
  📷 TensorFlow_Logo

Example Code

Here's a simple neural network using TensorFlow:

import tensorflow as tf

# Define model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(None, 5)),
    tf.keras.layers.Dense(1)
])

# Compile model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train model
model.fit(train_data, train_labels, epochs=10)

🧠 Deep_Learning_Architecture

Resources

📘 TensorFlow Tutorials Index for more guides
🤖 TensorFlow Playground to experiment with neural networks
📚 Hands-On Machine Learning with TensorFlow for in-depth learning

Explore the power of TensorFlow and build your first deep learning model today! 🚀