This is an introductory tutorial for using TensorFlow. It will cover: Importing required packages Creating and using Tensors Using GPU acceleration Datasets Import TensorFlow To get started, import the tensorflow module and enable eager execution. Eager execution enables a more interactive frontend to TensorFlow, the details of which we will discuss much later. import tensorflow…
Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share: code to create the model, and the trained…
After learning about basic classification at lesson 1: Basic classification. Today, we learn about text classification with Keras. This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem. We’ll use the IMDB dataset…
The first time you learn to train Neural Network, you try to train a Model for Image recognition using MNIST format. And later, you want to use your images to train your model, and how can you do it? With Python script, it’s very easy! Step 1: Create folder tree: Step 2: Create an rezise…