tensorflow-neural-networks

Build and train neural networks using TensorFlow's Keras API by defining a sequential or functional model, compiling it with an optimizer and loss function, then calling fit() on your training data. Keras handles layer stacking, forward passes, and backpropagation automatically across CPU and GPU.

Yes. TensorFlow's Keras API includes Conv2D layers for building CNNs optimized for image tasks. You can stack convolutional and pooling layers, compile, and train on image data to classify or detect objects.

Preprocess data using TensorFlow's built-in utilities: normalize pixel values, reshape inputs, and encode labels before passing to your model. The Skill covers data preprocessing integrated into the training pipeline for efficient model fitting.

Extend Keras by subclassing Layer to define custom layers with forward logic, then write custom training loops using GradientTape for fine-grained control. This approach supports multi-output networks and specialized architectures beyond sequential models.

No. TensorFlow runs on both CPU and GPU environments. GPU accelerates training significantly, but you can develop and train models on CPU; the Skill supports both deployment contexts seamlessly.

Serialize trained models using TensorFlow's serialization utilities to save weights and architecture, then reload for inference or further training. The Skill covers model serialization as part of the training and deployment workflow.