Training computer vision (CV) or natural language processing (NLP) models can be expensive and requires large datasets. If labeling is done manually, the process will take a longer training time and requires expensive hardware. For instance, the Generative Pre-trained Transformer 2 (GPT-2), a benchmark-setting language model created by Open AI| Machine learning nuggets
Recurrent Neural Networks (RNNs) are a class of neural networks that form associations between sequential data points. For example, the average sales made per month over a certain period. The data has a natural progression from month to month, meaning that the sales for the first month are the only| Machine learning nuggets
The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. In this article, you will discover that the Keras Functional API is used to create networks that: * Are non-linear. * Share layers. * Have| Machine learning nuggets
Building object detection and image segmentation models is slightly different from other models. Majorly because you have to use specialized models and prepare the data in a particular way. This article will examine how to perform object detection and image segmentation on a custom dataset using the TensorFlow 2 Object| Machine learning nuggets
Training models in Keras is usually done using the fit method. However, you may want more control over the training process. To do that, you'll need to create a custom training loop. This involves setting up a custom function to compute the loss and gradient. This article will walk you| Machine learning nuggets
In the artificial neural networks with TensorFlow article, we saw how to build deep learning models with TensorFlow and Keras. We covered various concepts that are foundational in training neural networks with TensorFlow. In that article, we used a Pandas DataFrame to build a classification model in Keras. This article| Machine learning nuggets
First off: If you are familiar with NumPy arrays, understanding TensorFlow Tensors will be as easy as first importing TensorFlow as below: import tensorflow as tf print(tf.__version__) # check version # 2.14.0 💡The examples in this article use TensorFlow v2.x, so concepts deprecated and or that were| Machine learning nuggets
The recent wave of generative language models is the culmination of years of research starting with the seminal "Attention is All You Need" paper. The paper introduced the Transformer architecture that would later be used as the backbone for numerous language models. These text generation language models are autoregressive, meaning| Machine learning nuggets
TensorBoard is a visualization library that enables data science practitioners to visualize various aspects of their machine learning modeling. For instance, you can use TensorBoard to: * Visualize the performance of the model. * Tuning model parameters. * Profile the executions of the program. For example, check the utilization of GPUs. * Debug machine| Machine learning nuggets
TensorFlow| Machine learning nuggets