Articles About Machine Learning

Developing a Python Program Using Inspection Tools

Python is an interpreting language. It means there is an interpreter to run our program, rather than compiling the code and running natively. In Python, a REPL (read-eval-print loop) can run commands line by line. Together with some inspection tools provided by Python, it helps to develop codes. In the following, you will see how to make use of the Python interpreter to inspect an object and develop a program. After finishing this tutorial, you will learn: How to work […]

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Python for Machine Learning (7-day mini-course)

Python for Machine Learning Crash Course.Learn core Python in 7 days. Python is an amazing programming language. Not only it is widely used in machine learning projects, you can also find its presence in system tools, web projects, and many others. Having good Python skills can make you work more efficiently because it is famous for its simplicity. You can try out your idea faster. You can also present your idea in a concise code in Python. As a practitioner, […]

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Using Normalization Layers to Improve Deep Learning Models

You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll explore more in this article. Also, thinking about it, in neural networks, the output of each layer serves as the inputs into the next layer, […]

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Overview of Some Deep Learning Libraries

Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. A neural network is probably a concept older than machine learning, dating back to the 1950s. Unsurprisingly, there were many libraries created for it. The following aims to give an overview of some of the famous libraries for neural networks and deep learning. After finishing this tutorial, you will learn: Some of the deep learning or neural network libraries The […]

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Using Autograd in TensorFlow to Solve a Regression Problem

We usually use TensorFlow to build a neural network. However, TensorFlow is not limited to this. Behind the scenes, TensorFlow is a tensor library with automatic differentiation capability. Hence you can easily use it to solve a numerical optimization problem with gradient descent. In this post, you will learn how TensorFlow’s automatic differentiation engine, autograd, works. After finishing this tutorial, you will learn: What is autograd in TensorFlow How to make use of autograd and an optimizer to solve an […]

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Three Ways to Build Machine Learning Models in Keras

If you’ve looked at Keras models on Github, you’ve probably noticed that there are some different ways to create models in Keras. There’s the Sequential model, which allows you to define an entire model in a single line, usually with some line breaks for readability. Then, there’s the functional interface that allows for more complicated model architectures, and there’s also the Model subclass which helps reusability. This article will explore the different ways to create models in Keras, along with […]

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How to Checkpoint Deep Learning Models in Keras

Deep learning models can take hours, days, or even weeks to train. If the run is stopped unexpectedly, you can lose a lot of work. In this post, you will discover how to checkpoint your deep learning models during training in Python using the Keras library. Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. Let’s get started. Jun/2016: First published Update Mar/2017: Updated for Keras […]

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Using Activation Functions in Neural Networks

Activation functions play an integral role in neural networks by introducing nonlinearity. This nonlinearity allows neural networks to develop complex representations and functions based on the inputs that would not be possible with a simple linear regression model. Many different nonlinear activation functions have been proposed throughout the history of neural networks. In this post, you will explore three popular ones: sigmoid, tanh, and ReLU. After reading this article, you will learn: Why nonlinearity is important in a neural network […]

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A Gentle Introduction to the tensorflow.data API

When you build and train a Keras deep learning model, you can provide the training data in several different ways. Presenting the data as a NumPy array or a TensorFlow tensor is common. Another way is to make a Python generator function and let the training loop read data from it. Yet another way of providing data is to use tf.data dataset. In this tutorial, you will see how you can use the tf.data dataset for a Keras model. After finishing […]

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Understanding the Design of a Convolutional Neural Network

Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How convolutional layers extract features from an image How different convolutional layers can stack up to build a neural network Let’s get started. Understanding the design […]

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