TensorFlow.js - Making Predictions from 2D Data
TensorFlow.js - Training in Node.js Codelab
TensorFlow is a powerful open-source software library for machine learning and deep learning. Here are some key concepts in TensorFlow that you should know:
Model: A model is a representation of the structure and relationships of data that you want to predict. In TensorFlow, you can create and train models using various algorithms such as linear regression, decision trees, and neural networks.
Layers: Layers are the building blocks of a neural network model in TensorFlow. A layer is a collection of neurons that receive input, perform computation, and produce output. You can use TensorFlow's layers module to create different types of layers for your model, such as dense layers, convolutional layers, and recurrent layers.
Loss: Loss is a measure of how well your model is able to make predictions. In TensorFlow, you can define a loss function to evaluate the difference between the model's predictions and the actual target values. The loss function guides the model's training process by indicating where it needs to make adjustments to improve its accuracy.
Optimizer: An optimizer is an algorithm that adjusts the model's parameters to minimize the loss. TensorFlow provides several built-in optimizers, such as stochastic gradient descent (SGD), Adagrad, and Adam.
Gradient: A gradient is the derivative of the loss with respect to the model's parameters. TensorFlow uses gradients to update the model's parameters during training.
Epoch: An epoch is a complete iteration through the entire training dataset. During each epoch, the model uses the training data to make predictions and update its parameters.
Batch: A batch is a subset of the training data used during each iteration of training. The model processes the batch and updates its parameters, and the process is repeated with different batches until the entire training dataset has been processed.
These are just a few of the key concepts in TensorFlow. Understanding these concepts will help you build and train effective machine learning models with TensorFlow.