GeeksforGeeks

Last Updated: 09 February 2025

The tf.keras.layers.LSTM layer is a built-in TensorFlow layer designed to handle sequential data efficiently. It is widely used for applications like:Text GenerationMachin...read more

Tensorflow

Deep Learning

AI-ML-DS

AI-ML-DS With Python

tf.keras.layers.GRU in TensorFlow

Last Updated: 09 February 2025

TensorFlow provides an easy-to-use implementation of GRU through tf.keras.layers.GRU, making it ideal for sequence-based tasks such as speech recognition, machine translat...read more

tf.keras.layers.Dropout in TensorFlow

Last Updated: 09 February 2025

In deep learning, overfitting is a common challenge where a model learns patterns that work well on training data but fails to generalize to unseen data. One effective tec...read more

Loss Function in TensorFlow

Last Updated: 12 February 2025

Loss function compute errors between the predicted output and actual output. The optimizer then updates the model parameters based on the loss value to improve accuracy.M...read more

model.evaluate() in TensorFlow

Last Updated: 10 February 2025

The model.evaluate() function in TensorFlow is used to evaluate a trained model on a given dataset. It returns the loss value and any additional metrics specified during m...read more

model.fit() in TensorFlow

Last Updated: 12 February 2025

In TensorFlow,model.fit() function is used to train a machine learning model for a fixed number of epochs (iterations over the entire dataset). During training, the model ...read more

Activation Function in TensorFlow

Last Updated: 12 February 2025

Activation functions add non-linearity to deep learning models and allow them to learn complex patterns. TensorFlow’s tf.keras.activations module provides a variety of act...read more

Evaluation Metrics in TensorFlow

Last Updated: 12 February 2025

Evaluation metrics accesses the performance of machine learning models. In TensorFlow, these metrics help quantify how well the model is performing during training and aft...read more

TF-IDF Representations in TensorFlow

Last Updated: 12 February 2025

Text data is one of the most common forms of unstructured data, and converting it into a numerical representation is essential for machine learning models. Term Frequency-...read more

Bag-of-Words Representations in TensorFlow

Last Updated: 12 February 2025

Bag-of-Words (BoW) converts text into numerical vectors based on word occurrences, ignoring grammar and word order. The model represents text as a collection (bag) of word...read more

Attention Layers in TensorFlow

Last Updated: 12 February 2025

Attention Mechanism allows models to focus on specific parts of input data, enabling more effective processing and prediction. In this article, we'll explore what attentio...read more

Recurrent Layers in TensorFlow

Last Updated: 12 February 2025

Recurrent layers are used in Recurrent Neural Networks (RNNs), which are designed to handle sequential data. Unlike traditional feedforward networks, recurrent layers main...read more

Convolutional Layers in TensorFlow

Last Updated: 12 February 2025

Convolutional layers are the foundation of Convolutional Neural Networks (CNNs), which excel at processing spatial data such as images, time-series data, and volumetric da...read more

Image Classification using ResNet

Last Updated: 13 February 2025

This article will walk you through the steps to implement it for image classification using Python and TensorFlow/Keras. Image classification classifies an image into one ...read more

Scientific Computing with Python

Last Updated: 25 February 2025

Scientific computing refers to the use of computational techniques and tools to solve scientific and engineering problems. Python has become one of the most popular langua...read more