Deep Learning
Once you choose and fit a final deep learning model in Keras, you can use it to make predictions on new data instances. There is some confusion amongst beginners about how exactly to do this. I often see questions such as: How do I make predictions with my model in Keras? In this tutorial, you […]
After you write your code, you must run your deep learning experiments on large computers with lots of RAM, CPU, and GPU resources, often a Linux server in the cloud. Recently, I was asked the question: “How do you run your deep learning experiments?” This is a good nuts-and-bolts question that I love answering. In […]
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast […]
The Keras Python deep learning library provides tools to visualize and better understand your neural network models. In this tutorial, you will discover exactly how to summarize and visualize your deep learning models in Keras. After completing this tutorial, you will know: How to create a textual summary of your deep learning model. How to […]
Convolutional neural networks are now capable of outperforming humans on some computer vision tasks, such as classifying images. That is, given a photograph of an object, answer the question as to which of 1,000 specific objects the photograph shows. A competition-winning model for this task is the VGG model by researchers at Oxford. What is […]
The Keras Python library makes creating deep learning models fast and easy. The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. The functional API in Keras is an alternate way […]
The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if […]
Running large deep learning processes on Amazon Web Services EC2 is a cheap and effective way to learn and develop models. For just a few dollars you can get access to tens of gigabytes of RAM, tens of CPU cores, and multiple GPUs. I highly recommend it. If you are new to EC2 or the […]
Keras is a Python deep learning library that can use the efficient Theano or TensorFlow symbolic math libraries as a backend. Keras is so easy to use that you can develop your first Multilayer Perceptron, Convolutional Neural Network, or LSTM Recurrent Neural Network in minutes. You may have technical questions when you get started using […]
Stochastic gradient descent is the dominant method used to train deep learning models. There are three main variants of gradient descent and it can be confusing which one to use. In this post, you will discover the one type of gradient descent you should use in general and how to configure it. After completing this […]
