Learn KERAS with Real Code Examples

MNIST handwritten digit classification

CIFAR-10 image classification

Sentiment analysis on text

Time series forecasting using LSTM

Image segmentation with U-Net

Resolve GPU/CPU backend issues

Fix shape mismatch in layers

Handle NaN loss during training

Optimize memory usage for large datasets

Ensure correct preprocessing of inputs

Unit-test custom layers

Validate model predictions on sample data

Profile training speed

Check for overfitting/underfitting

Test model saving and loading

Export as HDF5 (.h5) or SavedModel

TensorFlow Serving

TFLite for mobile/embedded devices

ONNX for cross-framework compatibility

Integrate with web APIs or cloud services

TensorFlow backend

Keras Tuner for hyperparameter optimization

TensorBoard for visualization

tf.data for dataset pipelines

Pre-trained model hub (Keras Applications)

TensorFlow for GPU/TPU acceleration

NumPy, Pandas for data processing

OpenCV for image preprocessing

Scikit-learn for evaluation and preprocessing

ONNX or TensorFlow Serving for deployment

Leverage pre-trained models

Use callbacks for automation

Profile early to identify bottlenecks

Use tf.data pipelines for efficiency

Start with small models before scaling

Build simple feedforward network

Train CNN on image dataset

Implement LSTM for sequence data

Use callbacks for early stopping

Deploy model to web or mobile