Category Archives: Deep Learning

Sentiment Analysis & Machine Learning Techniques

sentiment analysis machine learning

Artificial intelligence (AI) / Machine learning (ML) techniques are getting more and more popular. Many people use machine learning to analyze the sentiment of tweets, for example, to make predictions related to different business areas. In this blog post, you will learn about different machine learning / deep learning and NLP techniques which can be used for sentiment analysis. What is sentiment analysis? Sentiment analysis is about predicting the sentiment of a piece of text and then using this information to understand users’ (such as customers) opinions. . The principal objective of sentiment analysis is to classify the polarity of textual data, whether it is positive, negative, or neutral. Whether …

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Agriculture Use Cases & Machine Learning Applications

machine learning applications for agriculture use cases

Today agriculture is in a state of flux. Farmers are faced with the challenges of producing more food in face of a changing climate and population growth, while also adapting to evolving technologies that have changed agriculture forever. Machine learning has been applied to agriculture for many different use cases, from irrigation scheduling to pest management. In this post, we will explore agriculture use cases for machine learning & deep learning that can help farmers meet these challenges head-on. Different machine learning applications can be built around these agricultural use cases. It will be helpful for data scientists to get a high level idea around use cases and related machine …

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Credit Card Fraud Detection & Machine Learning

credit card fraud detection machine learning

Credit card fraud detection is a major concern for credit card companies. With credit cards so prevalent in our society, credit card companies must be able to prevent credit card fraud and protect their customers. Machine learning techniques can provide a powerful and effective way of detecting credit card fraud. In this blog post we will discuss machine learning techniques that data scientists can use to design appropriate credit card fraud detection solutions including algorithms such as Bayesian networks, support vector machines, neural networks and decision trees. What are different types of credit card fraud? The following are different types of credit card fraud: Counterfeit credit cards: Counterfeit credit cards …

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What is Machine Learning? Concepts & Examples

what is machine learning

Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover what machine learning entails, how it differs from traditional programming. What is machine learning? Simply speaking, machine learning is a technology where in machine learns to perform a prediction/estimation task based on past experience represented by historical data set.  There are three key aspects of machine learning which are following: Task: Task can be related to prediction problems  Experience: Experience represents historical dataset Performance: The goal is to perform better in the prediction task …

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NLP Pre-trained Models Explained with Examples

NLP pretrained models

The NLP (Natural Language Processing) is a branch of AI with the goal to make machines capable of understanding and producing human language. NLP has been around for decades, but it has recently seen an explosion in popularity due to pre-trained models (PTMs) which can be implemented with minimal effort and time on the side of NLP developers. This blog post will introduce you to different types of pre-trained machine learning models for NLP and discuss their usage in real-world examples. Before we get into looking at different types of pre-trained models for NLP, let’s understand the concepts related to pre-trained models for NLP. What are pre-trained models for NLP? …

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CNN Basic Architecture for Classification & Segmentation

image classification object detection image segmentation

Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include a variety of different layers with specific purposes, such as a convolutional layer, pooling layer, fully connected layers, dropout layers, etc. In this blog post, we will go over how CNNs work in detail for classification and segmentation problems. Description of basic CNN architecture for Classification The CNN architecture for classification includes convolutional layers, max-pooling layers, and fully connected layers. Convolution and max-pooling layers are used for …

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Graph Neural Networks Explained with Examples

Training a graph neural network model

Graph neural networks (GNNs) are a relatively new area in the field of deep learning. They arose from graph theory and machine learning, where the graph is a mathematical structure that models pairwise relations between objects. Graph Neural Networks are able to learn graph structures for different data sets, which means they can generalize well to new datasets – this makes them an ideal choice for many real-world problems like social network analysis or financial risk prediction. This post will cover some of the key concepts behind graph neural networks with the help of multiple examples. What are graph neural networks (GNNs)? Graphs are data structures which are used to …

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Drug Discovery & Deep Learning: A Starter Guide

generative chemistry with variational autoencoder VAE

The drug discovery process is tedious, time-consuming, and expensive. A drug company has to identify the compounds that are most likely to be successful in drug development. The drug discovery process can take up to 15 years with an average cost of $1 billion for each drug candidate that passes clinical trials. With AI and deep learning models becoming more popular in recent years, scientists have been looking at ways to use these tools in the drug discovery process. This article will explore how deep learning generative models (GANs) could be used as a starting point for data scientists to get started drug discovery AI projects! What is the drug …

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Supplier risk management & machine learning techniques

supplier risk management machine learning

Supplier risk management (SRM) is a serious issue for procurement professionals. Suppliers can be unreliable, have poor quality products, or fail to meet specifications. In this blog post we will discuss AI / machine learning algorithms / techniques that you can use to manage supplier risk and make your procurement process more efficient. What is supplier risk management? Supplier Risk Management (SRM) also known as Supplier Risk Optimization (SRO), refers to policies and technology that enables organizations to manage risks related with suppliers. This can be done by analyzing data about past purchases from the supplier, predicting future risks related with purchases from this particular company. It’s crucial for procurement …

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Key Deep Learning Techniques for Disease Diagnosis

disease diagnosis using machine learning

The disease diagnosis process has been the same for decades- a physician would analyze symptoms, perform lab tests, and refer to medical diagnostic guidelines. However, recent advances in AI/machine learning / deep learning have made it possible for computers to diagnose or detect diseases with human accuracy. This blog post will introduce some machine learning / deep learning techniques that can be used by data scientists for training models related to disease diagnosis. What are different types of diseases that can be diagnosed using AI-based techniques? The following is a list of different types of diseases that can be diagnosed using machine learning or deep learning-based techniques: Cancer prognosis and …

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How to Create & Detect Deepfakes Using Deep Learning

create and detect deepfake using deep learning

Deepfake are becoming a more common occurrence in today’s world. What is deepfake and how can you create it using deep learning? This blog post will help data scientists learn techniques for creating and detecting deepfakes, so they can stay ahead of this technology. A deepfake is a video or audio that alters reality by changing the way something appears. For example, someone could place your face onto someone else’s body in a video to make it seem like you were there when you really weren’t. There are many ways that one can detect if a photo has been manipulated with software such as Photoshop or Gimp. What is deepfake? …

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Different Activation Functions in Neural Networks

Data scientists know that activation functions are critical to understanding neural networks. It is important to use activation function in order to train the neural network. There are many activation functions available for data scientists to choose from, so it can be difficult choosing which activation function will work best for their needs. In this blog post, we look at different activation functions and provide examples of when they should be used in different types of neural networks. If you are starting on deep learning and wanted to know about different types of activation functions, you may want to bookmark this page for quicker access in future. Without further ado, …

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How do we build Deep Neural Network using Perceptron?

Single layer neural network

In this post, you will understand about how a deep neural network is built using a perceptron. This is a very important concept in relation to getting a good understanding of deep learning. You will also learn related Tensorflow / Keras code snippet. Here are the key concepts related to how deep neural network is built using one or more perceptrons: First and foremost, it is key to understand what is a Perceptron? A perceptron is the most fundamental unit which is used to build a neural network. A perceptron resembles a neuron in the human brain. In case of a neuron, multiple input signals are fed into a neuron …

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Examples of Generative Adversarial Network (GAN)

In this post, you will learn examples of generative adversarial network (GAN). The idea is to put together some of the interesting examples from across the industry to get a perspective on what problems can be solved using GAN. As a data scientist or machine learning engineer, it would be imperative upon us to understand the GAN concepts in a great manner to apply the same to solve real-world problems. This is where GAN network examples will prove to be helpful. Here are some examples of GAN network usage. Text to image translation Image editing / manipulating Creating images (2-dimensional images) Recreating images of higher resolution Creating 3-dimensional object Text to …

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Perceptron Explained using Python Example

In this post, you will learn about the concepts of Perceptron with the help of Python example. It is very important for data scientists to understand the concepts related to Perceptron as a good understanding lays the foundation of learning advanced concepts of neural networks including deep neural networks (deep learning).  In this post, the following topics are covered: What is Perceptron? Perceptron Python code example What is Perceptron? Perceptron is a machine learning algorithm which mimics how a neuron in the brain works. It is also called as single layer neural network consisting of a single neuron. The output of this neural network is decided based on the outcome of just one activation …

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50+ Machine learning & Deep learning Youtube Courses

In this post, you get an access to curated list of 50+ Youtube courses on machine learning, deep learning, NLP, optimization, computer vision, statistical learning etc. You may want to bookmark this page for quick reference and access to these courses. This page will be updated from time-to-time. Enjoy learning! Course title Course type URL MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity Deep learning https://www.youtube.com/playlist?list=PLCpMvp7ftsnIbNwRnQJbDNRqO6qiN3EyH AutoML – Automated Machine Learning AutoML https://ki-campus.org/courses/automl-luh2021 Probabilistic Machine Learning Machine learning https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd Geometric Deep Learning Geometric deep learning https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3 CS224W: Machine Learning with Graphs Machine learning  https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn MIT 6.S897 Machine Learning for Healthcare Machine learning https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Deep Learning and Combinatorial Optimization Deep …

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