How to create custom training loops in Keras Paid Members Public
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
How to train deep learning models on Apple Silicon GPU Paid Members Public
Training deep learning models on GPUs is faster than training on the CPU, especially when training large image or language models. You can train deep learning models with GPUs on Apple Silicon devices. To train deep learning models with GPU on Apple Silicon devices, you need to install the TensorFlow
How to build CNN in TensorFlow(examples, code, and notebooks) Paid Members Public
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
Pandas tutorial (A complete guide with examples and notebook) Paid Members Public
Pandas is an open-source Python library that provides a rich collection of data analysis tools for working with datasets. It borrows most of its functionality from the NumPy library. Therefore, we advise that you go through our NumPy tutorial first. As we dive into familiarizing ourselves with Pandas, it is
How to build artificial neural networks with Keras and TensorFlow Paid Members Public
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
How to use TensorBoard in Flax Paid Members Public
Tracking machine learning experiments makes understanding and visualizing the model's performance easy. It also makes it possible to spot any problems in the network. For example, you can quickly spot overfitting by looking at the training and validation charts. You can plot these charts using your favorite charts package, such
NumPy tutorial(Everything you need to know about NumPy with examples and video tutorials) Paid Members Public
You might have just finished a chunk of Python tutorials or have been using Python for other projects and now decided to venture into machine learning and data science. Well, you just pointed yourself to the right path. However, to venture into machine learning and data science, you will need
How to load datasets in JAX with TensorFlow Paid Members Public
JAX doesn't ship with data loading utilities. This keeps JAX focused on providing a fast tool for building and training machine learning models. Loading data in JAX is done using either TensorFlow or PyTorch. In the Image classification with JAX & Flax tutorial, we saw how to load image data with
