Learn TENSORFLOW with Real Code Examples

MNIST handwritten digit classification

CIFAR-10 image classification

Sentiment analysis on text data

Time series forecasting using LSTM

Image segmentation with U-Net

Resolve GPU/CPU device issues

Fix shape mismatches in layers

Handle NaN loss during training

Optimize memory usage for large datasets

Ensure correct preprocessing of inputs

Unit-test custom layers and models

Validate predictions on sample data

Profile training performance

Check for overfitting/underfitting

Test model saving and loading mechanisms

SavedModel or HDF5 (.h5) format

TensorFlow Serving for APIs

TensorFlow Lite for mobile or embedded devices

TensorFlow.js for web deployment

ONNX for cross-framework compatibility

TensorBoard for visualization

TensorFlow Hub for pre-trained models

TFX for production ML pipelines

TensorFlow Lite for mobile/embedded deployment

TensorFlow.js for web-based ML

Keras for high-level model APIs

NumPy, Pandas for data processing

OpenCV for image preprocessing

Scikit-learn for evaluation and preprocessing

ONNX or TensorFlow Serving for deployment

Use pre-trained models when possible

Leverage callbacks for automation

Profile early to identify bottlenecks

Use tf.data pipelines for efficiency

Start with small models before scaling

Train a simple neural network

Implement CNN for image classification

Build LSTM for time series data

Use callbacks and early stopping

Deploy a trained model to a web or mobile app