In the previous tutorial we covered the TensorFlow APIs for automatic differentiation, a basic building block for machine learning. In this tutorial we will use the TensorFlow primitives introduced in the prior tutorials to do some simple machine learning. TensorFlow also includes a higher-level neural networks API (tf.keras) which provides useful abstractions to reduce boilerplate.…
In the previous tutorial we introduced Tensors and operations on them. In this tutorial we will cover automatic differentiation, a key technique for optimizing machine learning models. Setup import tensorflow as tf tf.enable_eager_execution() tfe = tf.contrib.eager # Shorthand for some symbols Derivatives of a function TensorFlow provides APIs for automatic differentiation – computing the derivative…
In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). This notebook builds a model to predict the median price…
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…