From 2604ecd61a9911d1d73e764046ee09c78ab5fb05 Mon Sep 17 00:00:00 2001
From: rohit <codyyrohit@gmail.com>
Date: Sat, 18 May 2024 21:51:53 +0530
Subject: [PATCH] Added TensorFlow,md File

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 # List of sections
 
-- [Section title](filename.md)
+- [TensorFlow.md](learn-python\contrib\machine-learning\tensorFlow.md)
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+# TensorFlow
+
+Developed by the Google Brain team, TensorFlow is an open-source library that provides a comprehensive ecosystem for building and deploying machine learning models. It supports deep learning and neural networks and offers tools for both beginners and experts.
+
+## Key Features
+
+- **Flexible and comprehensive ecosystem**
+- **Scalable for both production and research**
+- **Supports CPUs, GPUs, and TPUs**
+
+## Basic Example: Linear Regression
+
+Let's start with a simple linear regression example in TensorFlow.
+
+```python
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Generate synthetic data
+X = np.array([1, 2, 3, 4, 5], dtype=np.float32)
+Y = np.array([2, 4, 6, 8, 10], dtype=np.float32)
+
+# Define the model
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(units=1, input_shape=[1])
+])
+
+# Compile the model
+model.compile(optimizer='sgd', loss='mean_squared_error')
+
+# Train the model
+history = model.fit(X, Y, epochs=500)
+
+# Predict
+predictions = model.predict(X)
+
+# Plot the results
+plt.plot(X, Y, 'ro', label='Original data')
+plt.plot(X, predictions, 'b-', label='Fitted line')
+plt.legend()
+plt.show()
+```
+
+In this example:
+
+1. We define a simple dataset with a linear relationship.
+2. We build a sequential model with one dense layer (linear regression).
+3. We compile the model with stochastic gradient descent (SGD) optimizer and mean squared error loss.
+4. We train the model for 500 epochs and then plot the original data and the fitted line.
+
+## When to Use TensorFlow
+
+TensorFlow is a great choice if you:
+
+- **Need to deploy machine learning models in production:** TensorFlow’s robust deployment options, including TensorFlow Serving, TensorFlow Lite, and TensorFlow.js, make it ideal for production environments.
+- **Work on large-scale deep learning projects:** TensorFlow’s comprehensive ecosystem supports distributed training and has tools like TensorBoard for visualization.
+- **Require high performance and scalability:** TensorFlow is optimized for performance and can leverage GPUs and TPUs for accelerated computing.
+- **Want extensive support and documentation:** TensorFlow has a large community and extensive documentation, which can be very helpful for both beginners and advanced users.
+
+## Example Use Cases
+
+- Building and deploying complex neural networks for image recognition, natural language processing, or recommendation systems.
+- Developing models that need to be run on mobile or embedded devices.
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