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
Building artificial neural networks with TensorFlow and Keras requires understanding some key concepts. After learning these concepts, you'll install TensorFlow and start designing neural networks. This article will cover the concepts you need to comprehend to build neural networks in TensorFlow and Keras. Without further ado, let's get the ball| Machine learning nuggets
JAX is a high performance library that offers accelerated computing through XLA and Just In Time Compilation. It also has handy features that enable you to write one codebase that can be applied to batches of data and run on CPU, GPU, or TPU. However, one of its biggest selling| 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
Training computer vision models with little data can lead to poor model performance. This problem can be solved by generating new data samples from the existing images. For example, you can create new images by flipping and rotating the existing ones. Generating new image samples from existing ones is known| Machine learning nuggets
Training computer vision models requires a lot of time because of the size of the models and image data. Therefore, training these models can take prolonged periods of time, especially when training on a single GPU. You can reduce the training time by distributing the training across several GPUs. This| 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