Tag Archives: 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 …
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? …
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 …
Top Deep Learning Myths You should know
This post highlights the top deep learning myths you should know. This is important to understand in order to leverage deep learning to solve complex AI problems. Many times, beginner to intermediate level machine learning enthusiasts don’t consider deep learning based on the myths discussed in this post. Without further ado, let’s look at the topmost and most common deep learning myths: Good understanding of complex mathematical concepts: Well, that is just a myth. At times, they say that one needs to have a higher degree in Mathematics & statistics. That is not true. With tools and programming languages along with libraries available today, basic mathematical concepts should be able …
When to use Deep Learning vs Machine Learning Models?
In this post, you will learn about when to go for training deep learning models from the perspective of model performance and volume of data. As a machine learning engineer or data scientist, it always bothers as to can we use deep learning models in place of traditional machine learning models trained using algorithms such as logistic regression, SVM, tree-based algorithms, etc. The objective of this post is to provide you with perspectives on when to go for traditional machine learning models vs deep learning models. The two key criteria based on which one can decide whether to go for deep learning vs traditional machine learning models are the following: …
Historical Dates & Timeline for Deep Learning
This post is a quick check on the timeline including historical dates in relation to the evolution of deep learning. Without further ado, let’s get to the important dates and what happened on those dates in relation to deep learning: Year Details/Paper Information Who’s who 1943 An artificial neuron was proposed as a computational model of the “nerve net” in the brain. Paper: “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, volume 5, 1943 Warren McCulloch, Walter Pitts Late 1950s A neural network application by reducing noise in phone lines was developed Paper: Andrew Goldstein, “Bernard Widrow oral history,” IEEE Global History Network, 1997 Bernard …
Keras CNN Image Classification Example
In this post, you will learn about how to train a Keras Convolution Neural Network (CNN) for image classification. Before going ahead and looking at the Python / Keras code examples and related concepts, you may want to check my post on Convolution Neural Network – Simply Explained in order to get a good understanding of CNN concepts. Keras CNN Image Classification Code Example First and foremost, we will need to get the image data for training the model. In this post, Keras CNN used for image classification uses the Kaggle Fashion MNIST dataset. Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a …
Convolutional Neural Network (CNN) – Simply Explained
In this post, you will learn about the basic concepts of convolutional neural network (CNN) explained with examples. As data scientists / machine learning / deep learning enthusiasts, you must get a good understanding of convolution neural network as there are many applications of CNN. Before getting into the details on CNN, let’s understand the meaning of Convolution in convolutional neural network. What’s Convolution? What’s intuition behind Convolution? Convolution represents a mathematical operation on two functions. As there can be applied different mathematical operations such as addition or multiplication on two different functions, in the similar manner, convolution operation can be applied on two different functions. Mathematically, the convolution of two different …
Keras Neural Network for Regression Problem
In this post, you will learn about how to train neural network for regression machine learning problems using Python Keras. Regression problems are those which are related to predicting numerical continuous value based on input parameters / features. You may want to check out some of the following posts in relation to how to use Keras to train neural network for classification problems: Keras – How to train neural network to solve multi-class classification Keras – How to use learning curve to select most optimal neural network configuration for training classification model In this post, the following topics are covered: Design Keras neural network architecture for regression Keras neural network …
Keras – Categorical Cross Entropy Loss Function
In this post, you will learn about when to use categorical cross entropy loss function when training neural network using Python Keras. Generally speaking, the loss function is used to compute the quantity that the the model should seek to minimize during training. For regression models, the commonly used loss function used is mean squared error function while for classification models predicting the probability, the loss function most commonly used is cross entropy. In this post, you will learn about different types of cross entropy loss function which is used to train the Keras neural network model. Cross entropy loss function is an optimization function which is used in case …
I found it very helpful. However the differences are not too understandable for me