Deep Learning is a subset of Machine Learning where we need a large amount of data to train powerful models capable of finding relationships in the data to solve complex problems. So, if you are looking for courses that will…
TensorFlow is one of the most powerful frameworks for Machine Learning. It was created by Google and used by companies like Airbnb, Spotify, and Google. So if you are looking for the best resources to learn TensorFlow, this article is…
Netflix is one of the most popular OTT streaming platforms. It offers a vast collection of television series and films and owns its productions known as Netflix Originals. People who are highly active in stock market investments always keep an…
In machine learning, classification means categorizing the known classes, For example, categorizing the most profitable and non-interested customers from a dataset for advertising a particular product. You must have trained a classification model with a machine learning algorithm before. Here…
A neural network is a computational architecture that connects an input layer to the output layer. Learning about Neural Networks will not only help you in solving complex Deep Learning problems but will also help you do better in an…
A neural network is a computational structure that connects an input layer to an output layer. This computational structure is used in training deep learning models that can easily outperform any classical machine learning algorithm. As a data science beginner,…
LSTM stands for Long Short Term Memory Networks. It is a type of recurrent neural network that is commonly used for regression and time series forecasting in machine learning. It can memorize data for long periods, which differentiates LSTM neural…
Deep Learning is a subset of machine learning that mimics the human brain to solve complex problems. Deep Learning is used to train computers to perform tasks where humans are good at, but computers are not. If you are learning…
Deep Learning is a subset of machine learning where we need a large amount of data to train powerful models capable of finding relationships in data to solve complex problems. While machine learning algorithms can also be used to train…
The activation function is a function used in neural networks to calculate the weighted sum of inputs and biases, which is used to determine whether a neuron should be activated or not. You must have heard a lot about activation…
AlexNet is a popular convolutional neural network architecture that won the ImageNet 2012 challenge by a large margin. It was developed by Alex Krizhevsky, Ilya Sutskever and Geoffery Hinton. It is similar to the LeNet-5 architecture but larger and deeper.…
Computer Vision is one of the fields of Artificial Intelligence that enables a computer to find meaningful information from digital images, videos and other visual sources of data. The Face ID found in iPhones is one of the best examples…
A Multilayer Perceptron or MLP is one of the simplest feed-forward neural networks. Multilayer Perceptrons are the types of neural networks which are bidirectional as they foreword propagation of the inputs and backward propagation of the weights. If you want…
Deep learning is a subset of machine learning in which we use artificial neural networks to train models to solve complex problems where we need a large amount of data compared to other machine learning algorithms. If you want to…
The LeNet-5 architecture is the most widely used architecture of convolutional neural networks. It was created by Yann LeCunn in 1998. If you have never used the LeNet-5 architecture of convolutional neural networks, then this article is for you. In…
Computer vision is one of the fields of artificial intelligence. The way machine learning is used to make systems smart enough to solve business problems by finding meaningful relationships from data, similarly, computer vision is used to make systems smart…
If you want to become an expert in machine learning, you must also learn deep learning. There are many paid and free courses on the internet that can give you a comprehensive knowledge of the concepts of deep learning. So,…
Deep learning is a subset of machine learning in which we use the power of neural network architectures that mimic the workings of a human brain while solving a problem. If you want to become a machine learning engineer, you have…
You must have used a neural network for training a model on your data. There are so many types of architectures of neural networks that you can use to train a model, but have you ever visualized the architecture of…
AutoKeras is a Keras-based machine learning framework. It was developed by DATA Lab at Texas A&M University to provide deep learning for everyone. Simply put, it is an automatic machine learning framework for deep learning. If you want to learn…
Perceptron is one of the simplest architecture of Artificial Neural Networks in Machine Learning. It was invented by Frank Rosenblatt in 1957. In this article, I will take you through an introduction to Perceptron in Machine Learning and its implementation…
A neural network is a subset of machine learning that mimics the workings of a human brain while solving a complex problem based on deep learning. Neural networks are inspired by neurons found in the human brain. In this article,…
Both PyTorch and TensorFlow have been used in industry for a long time. These two frameworks are the best in the features they provide for deep learning. So what should you learn? In this article, I’ll walk you through what…
The way birds inspired humans to create an aeroplane the same way a human’s brain inspired humans to build intelligent machines. Neural networks were inspired by biological neurons found in the brain of a human. In this article, I will…
To create a neural network, you need to decide what you want to learn. Here, I’m going to choose a fairly simple goal: to implement a three-input XOR gate. (It’s an exclusive OR gate.) The table shows the function we…
Neural networks and various other models of how the brain works have been around since people started talking about artificial intelligence. This article introduces you to the concept of neural networks and how to implement them using Python. Understanding Neural…
Neural networks and various other models of how the brain works have been around since people started talking about artificial intelligence. In this article, I’m going to introduce you to some very useful neural networks projects that you can use…
Neural networks can be a difficult concept to understand. I think it’s mainly because they can be used for so many different things like classification, identification or just regression. In this article, I will walk you through how to set…
If you think that TensorFlow and PyTorch are the only ways to Neural Networks with Python then I would tell you that you are wrong. As there is another package to build Neural Networks with Scikit-Learn and Python. In this…
Humans take no effort to distinguish a dog, cat, or flying saucer. But this process is quite difficult for a computer to emulate: it only looks easy because God designs our brains incredibly well to recognize images. One common example…
The LSTM Network model stands for Long Short Term Memory networks. These are a special kind of Neural Networks which are generally capable of understanding long term dependencies. LSTM model was generally designed to prevent the problems of long term…
Birds inspired us to fly, nature inspired us to countless inventions. It seems logical, then to look at the brain’s architecture for inspiration on how to build an Intelligent Machine. This is the logic that sparked Artificial Neural Networks (ANN).…
What is a Neural Network Neural Network is a computational algorithm that is used in creating deep learning models for predictions and classifications. It is based on self-learning and training, rather than being explicitly programmed. Neural Networks are inspired by…
Introduction to CNN Convolutional neural networks (CNN) are primarily used to classify images or identify pattern similarities between them. So a convolutional network receives a normal color image as a rectangular box whose width and height are measured by the…