- November: Advancing in Code and Character
- Advanced Java Programming: Data Structures and Algorithms
- How Christians Can Maintain Integrity in High-Stakes Roles
- Building Scalable Applications with Laravel and Vue.js
- How to Overcome Burnout with Faith and Self-Care
- Securing IoT Devices: Protecting the Internet of Things
- What Does the Bible Say About Rest and Rejuvenation?
- Exploring TensorFlow: Building Deep Learning Models
Introduction
TensorFlow has become the go-to framework for deep learning. Whether you’re classifying images or building recommendation systems, understanding its workflow can empower your projects.
The Building Blocks
- Tensors: Multidimensional data arrays.
- Graphs: Define computation flow.
- Sessions (TF1) / Eager Execution (TF2): Run operations imperatively.
- Keras API: High-level interface for neural networks.
Simple Example
import tensorflow as tf
from tensorflow import keras
model = keras.Sequential([
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Training and Deployment
Use TensorBoard to visualise training, and TensorFlow Lite for mobile deployment.Understanding optimisation, overfitting, and validation improves accuracy and reliability.
Conclusion
Deep learning doesn’t have to be daunting. With frameworks like TensorFlow, powerful AI tools are within every developer’s reach.
