In 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 … Continue reading How To Train A Neural Network In Python – Part III
Category: Machine Learning
How To Train A Neural Network In Python – Part II
In 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 … Continue reading How To Train A Neural Network In Python – Part II
How To Train A Neural Network In Python – Part I
Deep 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 … Continue reading How To Train A Neural Network In Python – Part I
How To Install Caffe On Ubuntu
The concept of deep learning is becoming increasingly pervasive. It is a new area of research in machine learning that focuses on learning optimal representations of data. Now what does it mean? In the realm of classical machine learning, we have the build the features first and then the machine learning algorithm will learn how to … Continue reading How To Install Caffe On Ubuntu
How To Compute Confidence Measure For SVM Classifiers
Support Vector Machines are machine learning models that are used to classify data. Let's say you want to build a system that can automatically identify if the input image contains a given object. For ease of understanding, let's limit the discussion to three different types of objects i.e. chair, laptop, and refrigerator. To build this, … Continue reading How To Compute Confidence Measure For SVM Classifiers
Dissecting Bias vs. Variance Tradeoff In Machine Learning
I was recently working on a machine learning problem when I stumbled upon an interesting question. I wanted to build a machine learning model using a labeled dataset that can classify an unknown image. It's a classic supervised learning problem! I was not exactly sure how the model would turn out, so I had to … Continue reading Dissecting Bias vs. Variance Tradeoff In Machine Learning
Autoencoders In Machine Learning
When we talk about deep neural networks, we tend to focus on feature learning. Traditionally, in the field of machine learning, people use hand-crafted features. What this means is that we look at the data and build a feature vector which we think would be good and discriminative. Once we have that, we train a model … Continue reading Autoencoders In Machine Learning
What’s The Importance Of Hyperparameters In Machine Learning?
Machine learning is becoming increasingly relevant in all walks of science and technology. In fact, it’s an integral part of many fields like computer vision, natural language processing, robotics, e-commerce, spam filtering, and so on. The list is potential applications is pretty huge! People working on machine learning tend to build models based on training data, … Continue reading What’s The Importance Of Hyperparameters In Machine Learning?
What Is A Markov Chain?
If you have studied probability theory, then you must have heard Markov's name. When we study probability and statistics, we tend to deal with independent trials. What this means is that if you conduct an experiment a lot of times, we assume that the outcome of one trial doesn't influence the outcome of the next … Continue reading What Is A Markov Chain?
What Is Manifold Learning?
Machine learning is being used extensively in fields like computer vision, natural language processing, and data mining. In many modern applications that are being built, we usually derive a classifier or a model from an extremely large data set. The accuracy of the training algorithms is directly proportional to the amount of data we have. … Continue reading What Is Manifold Learning?
Perpetual Enigma 