When preparing for Machine Learning interviews, it’s crucial to have a good understanding of performance evaluation metrics, as they are key to assessing and improving model performance. So, if you are looking for some challenging interview questions related to performance…
Deep Learning is a subset of Machine Learning that leverages deep neural networks to learn and represent complex patterns and features from data. If you are looking for a job specifically focused on Machine Learning, you should have at least…
Neural network architectures refer to the structural and organizational designs of artificial neural networks (ANNs). These architectures determine how the network is organized, including the number of layers, the number of neurons in each layer, the connections between neurons, and…
Neural Networks are a type of computational model inspired by the structure and functioning of a human brain. They are composed of interconnected nodes called neurons, organized into layers. Each neuron receives inputs, processes them, and produces an output that…
Next Word Prediction means predicting the most likely word or phrase that will come next in a sentence or text. It is like having an inbuilt feature on an application that suggests the next word as you type or speak.…
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…
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…
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 use of deep neural architectures instead of the classic machine learning algorithms to train bigger models that can mimic the human brain while solving complex problems that involve heavy computing is known as deep learning. In this article, I…
When we use deep neural architecture instead of classic machine learning algorithms, it’s nothing but deep learning. In this article, I’m going to introduce you to some of the best deep learning projects with Python that you should try as…
Many machine learning algorithms can be used to solve complex problems that require a large amount of data with a large number of features, but deep learning can outperform all algorithms. So to understand where we can use deep learning…
Humans have an inherent ability to transfer knowledge between tasks. What we acquire as knowledge while learning a task, we use it in the same way to solve related tasks. The more the tasks are linked, the easier it is…
Deep learning is a subset of Artificial Intelligence, which is an area that relies on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, these models work with artificial neural networks, designed to…
In this article, I will take you through a brief explanation of Image Segmentation in Deep Learning. I will only explain the concept behind the image segmentation here in this article. If you want to go through the practical part…
Being a practitioner in Machine Learning, you must have gone through an image classification, where the goal is to assign a label or a class to the input image. Now, suppose you want to get where the object is present…
Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. In most of the functions in Machine Learning, the data that you work with is barely in a format for…
TensorBoard is a great interactive visualization tool that you can use to view the learning curves during training, compare learning curves between multiple runs, visualize the computation graphs, analyze training statistics, view images generated by your model, visualize complex multidimensional…
Most of the keyboards in smartphones give next word prediction features; google also uses next word prediction based on our browsing history. So a preloaded data is also stored in the keyboard function of our smartphones to predict the next…
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation. Data Augmentation is one of the…
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).…
There are two main approaches to training models across multiple devices; model parallelism, where the model is split across the devices, and data parallelism, where the model is replicated across every device, and each replica is trained on a subset…
In this article, I will take you through a very powerful algorithm in Machine Learning, which is the Grid Search Algorithm. It is mostly used in hyperparameters tuning and models selection in Machine Learning. Here I will teach you how…
In this article, we will look at how to speed up your models by using a GPU. We will also see how to split the computations across multiple devices, including the CPU and numerous GPU devices. Thanks to GPUs, instead…
PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides…
If you want to deploy your TensorFlow model to a mobile or embedded device, a large model may take too long to download and use too much RAM and CPU, all of which will make your app unresponsive, heat the…
According to the report of Centers of Disease Control and Prevention about one in seven adults in the United States have Diabetes. But by next few years this rate can move higher. With this in mind today, In this article,…
TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it for anything else that requires heavy calculations). The Google Brain team developed it, and it powers many…