Tag Archives: Deep Learning
Deep Neural Network Examples from Real-life
The deep neural network (DNN) is an artificial neural network, which has a number of hidden layers and nodes. Deep NN is composed of many interconnected and non-linear processing units that work in parallel to process information more quickly than the traditional neural networks. Deep learning algorithms are used for classification, regression analysis, prediction and other types of tasks. In this blog post, we will present deep neural network examples from the real-world/real-life. Before jumping into examples, you may want to check out some of my following posts on deep neural network: Deep Learning Explained Simply in Layman Terms Neural network explained with perceptron example Perceptron explained with Python example …
Different Types of CNN Architectures Explained: Examples
The CNN architectures are the most popular deep learning framework. CNNs are used for a variety of applications, ranging from computer vision to natural language processing. In this blog post, we will discuss each type of CNN architecture in detail and provide examples of how these models work. Different types of CNN Architectures The following is a list of different types of CNN architectures: LeNet: LeNet is the first CNN architecture. It was developed in 1998 by Yann LeCun, Corinna Cortes, and Christopher Burges for handwritten digit recognition problems. The model has five convolution layers followed by two fully connected layers. LeNet was the beginning of CNNs in deep learning …
Real-World Applications of Convolutional Neural Networks
Convolutional neural networks (CNNs) are a type of deep learning algorithm that has been used in a variety of real-world applications. CNNs can be trained to classify images, detect objects in an image, and even predict the next word in a sentence with incredible accuracy. CNNs can also be applied to more complex tasks such as natural language processing (NLP). CNNs are very good at solving classification problems because they’re able to identify patterns within data sets. This blog post will explore some CNN applications and discuss how CNN models can be used to solve real-world problems. Before getting into the details of CNN applications, let’s quickly understand what are …
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 …
NLP Pre-trained Models Explained with Examples
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? …
CNN Basic Architecture for Classification & 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 …
Graph Neural Networks Explained with Examples
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 …
Drug Discovery & Deep Learning: A Starter Guide
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 …
Key Deep Learning Techniques for Disease Diagnosis
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 …
How to Create & Detect Deepfakes 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? …
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, …
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 …
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 …
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 …
Top 10+ Youtube AI / Machine Learning Courses
In this post, you get access to top Youtube free AI/machine learning courses. The courses are suitable for data scientists at all levels and cover the following areas of machine learning: Machine learning Deep learning Natural language processing (NLP) Reinforcement learning Here are the details of the free machine learning / deep learning Youtube courses. S.No Title Description Type 1 CS229: Machine Learning (Stanford) Machine learning lectures by Andrew NG; In case you are a beginner, these lectures are highly recommended Machine learning 2 Applied machine learning (Cornell Tech CS 5787) Covers all of the most important ML algorithms and how to apply them in practice. Includes 3 full lectures …
Deep Learning – Top 5 Online Jupyter Notebooks Servers
In this post, you will get information regarding the online Jupyter notebooks platform (GPU-based) which you can use to get started with both, machine learning and deep learning. The list consists of both freely available and paid options of online Jupyter notebook available with GPUs. When starting with GPUs, it is recommended to use rented options available online rather than buying your own GPU servers. There are online GPU Linux servers available (free and paid options) that can be used to train deep learning & machine learning models. I will be writing about it in my next post. Here is the list of Jupyter notebook platforms that could be used …
