How To Train A Neural Network In Python – Part III

ImageJ=1.44p unit=umIn the previous blog post, we learnt how to build a multilayer neural network in Python. What we did there falls under the category of supervised learning. In that realm, we have some training data and we have the associated labels. Now the goal is to train the neural network correctly label our training data. Once we train the model, we can use it to predict the labels of unknown datapoints. But what about unsupervised learning? In the real world, we also have to deal with a lot of unlabeled data. Can we train a neural network to recognize clusters in our data? Yes, we certainly can! Let’s go ahead and see how we can do that in Python, shall we?   Continue reading

How To Train A Neural Network In Python – Part II

1 mainIn the previous blog post, we discussed about perceptrons. We learnt how to train a perceptron in Python to achieve a simple classification task. If you need a quick refresher on perceptrons, you can check out that blog post before proceeding further. In a way, perceptron is a single layer neural network with a single neuron. In this blog post, we will learn how to develop a multilayer neural network. A multilayer neural network consists of multiple layers and each layer consists of many perceptrons, and it is much better at classifying data that a single perceptron. So how exactly does a multilayer neural network function? How do we build it in Python?   Continue reading

How To Train A Neural Network In Python – Part I

1 mainDeep learning uses neural networks to build sophisticated models. The basic building blocks of these neural networks are called “neurons”. When a neuron is trained to act like a simple classifier, we call it “perceptron”. A neural network consists of a lot of perceptrons interconnected with each other. Let’s say we have a bunch of inputs and the corresponding desired outputs. The goal of deep learning is to train this neural network so that the system outputs the right value for the given set of inputs. This process basically involves tuning each neuron in the network until it behaves a certain way. So what exactly is this perceptron? How do we train it in Python?   Continue reading

Performing Windowed Computations On Streaming Data Using Spark In Python

1 mainWe deal with real time data all the time. If you look at those analytics dashboards, you can see how they perform computations and tell us what happened in the last 60 mins or may be the last 7 hours. They are dealing with terabytes of data and yet they can process all of that in real time. These insights are extremely valuable because you can take the right actions if you know what’s happening. If you have a shopping website, you need to know what happened in the last few hours so that you can boost your sales. Are there a lot of visitors from France? Can I organize a quick French themed promotion to increase my sales during peak hours? The answers to all these lies deep within your data. Spark Streaming is amazing at these things! So how do we do windowed computations in Spark? How can we process this data in real time?   Continue reading

Analyzing Real-time Data With Spark Streaming In Python

1 mainThere is a lot of data being generated in today’s digital world, so there is a high demand for real time data analytics. This data usually comes in bits and pieces from many different sources. It can come in various forms like words, images, numbers, and so on. Twitter is a good example of words being generated in real time. We also have websites where statistics like number of visitors, page views, and so on are being generated in real time. There are so much data that it is not very useful in its raw form. We need to process it and extract insights from it so that it becomes useful. This is where Spark Streaming comes into the picture! It is exceptionally good at processing real time data and it is highly scalable. It can process enormous amounts of data in real time without skipping a beat. So how exactly does Spark do it? How do we use it?   Continue reading

Launching A Spark Standalone Cluster

1 mainIn the previous blog post, we saw how to start a Spark cluster on EC2 using the inbuilt launch scripts. This is good if you want get something up and running quickly, but it won’t allow fine-grained control over our cluster. A lot of times, we would want to customize the machines that we spin up. Let’s say that you want to use different types of machines to handle production level traffic in different regions. May be you are not on EC2 and you want to launch some machines in your cluster. How would you do it? This is the reason we have Spark Standalone mode. Using this method, we can manually launch any number of machines independently in our private cluster and make them listen to our master machine. It gives us a lot of flexibility! Let’s go ahead and see how to do it, shall we?   Continue reading

How To Launch A Spark Cluster On Amazon EC2

1 mainApache Spark is marketed as “lightning fast cluster computing” and it stands true to its word! It can do amazing things really quickly using a cluster of machines. So how do we assemble that cluster? Let’s say you are using a cloud service provider like Amazon Web Services. Do we need to spin up a bunch of instances ourselves to form a “cluster”? Well, not really! Spark can launch a cluster by itself and you can control everything from one machine. You just need to log into your main instance and Spark will automatically launch all the instances in the cluster for you. It’s beautiful! Let’s go ahead and see how to launch a cluster, shall we?   Continue reading