Simple Linear Regression - Tensorflow Typing CST Test
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Simple Linear Regression — Tensorflow Code
A minimal TensorFlow example showing linear regression training on sample data.
import tensorflow as tf
import numpy as np
# Sample data
x_train = np.array([1, 2, 3, 4], dtype=float)
y_train = np.array([2, 4, 6, 8], dtype=float)
# Define a simple linear model
model = tf.keras.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
# Train the model
model.fit(x_train, y_train, epochs=500)
# Predict
y_pred = model.predict([10.0])
print("Prediction for 10:", y_pred)Tensorflow Language Guide
TensorFlow is an open-source, end-to-end platform for machine learning developed by Google. It provides comprehensive tools, libraries, and community resources for building and deploying ML models across different environments.
Primary Use Cases
- ▸Deep learning for image, video, and speech recognition
- ▸Natural language processing and translation
- ▸Reinforcement learning for AI agents
- ▸Time series forecasting and predictive analytics
- ▸Deployment of AI models on cloud, mobile, and embedded devices
Notable Features
- ▸Flexible computation graphs for ML models
- ▸Support for CPU, GPU, and TPU acceleration
- ▸TensorFlow Extended (TFX) for production pipelines
- ▸TensorFlow Lite for mobile and embedded deployment
- ▸TensorFlow.js for running ML in the browser
Origin & Creator
TensorFlow was created by the Google Brain team and released in 2015 to provide a flexible, scalable platform for machine learning.
Industrial Note
TensorFlow is widely adopted in industry and academia for scalable ML solutions, serving AI applications in computer vision, NLP, recommendation systems, and robotics.