Exploring the Security Vulnerabilities of Neural Networks
Neural networks have remarkable disruptive potential, but this makes their vulnerabilities more concerning. As helpful as these machine learning models can be, they could cause considerable damage if hackers infiltrate them. It’s important to recognize these risks to develop and implement neural networks safely. As neural... Read more
Deep Neural Networks Could Be Key to Ancient Text Restoration and Attribution, Research Shows
Uncovering the truths of ancient history can be complicated. Researchers must study texts inscribed in stone and clay, a process called epigraphy, but these inscriptions can be ineligible after centuries of damage. Recent research suggests that deep neural networks (DNNs) could help with ancient text restoration.... Read more
Repo of the Week: Instant Neural Graphics Primitives
A team of researchers from NVIDIA including Thomas Muller, Alex Evans, Christoph Schied, and Alexander Keller,  demonstrated a new method that should enable the efficient use of artificial neural networks for rendering computer graphics.  Rendering is a notoriously slow process so this is a significant development... Read more
Neural Network Optimization
This article is the third in a series of articles aimed at demystifying neural networks and outlining how to design and implement them. In this article, I will discuss the following concepts related to the optimization of neural networks: Challenges with optimization Momentum Adaptive Learning Rates... Read more
Intermediate Topics in Neural Networks
This article is the second in a series of articles aimed at demystifying the theory behind neural networks and how to design and implement them for solving practical problems. In this article, I will cover the design and optimization aspects of neural networks in detail. The... Read more
Keras Metrics: Everything You Need To Know
Keras metrics are functions that are used to evaluate the performance of your deep learning model. Choosing a good metric for your problem is usually a difficult task. you need to understand which metrics are already available in Keras and tf.keras and how to use them, in many... Read more
Simple Guide to Hyperparameter Tuning in Neural Networks
A step-by-step Jupyter notebook walkthrough on hyperparameter optimization. This is the fourth article in my series on fully connected (vanilla) neural networks. In this article, we will be optimizing a neural network and performing hyperparameter tuning in order to obtain a high-performing model on the Beale function—one... Read more
Deep Q-Learning Algorithm in Reinforcement Learning
In this article, we will discuss Q-learning in conjunction with neural networks (NNs). This combination has the name deep Q-network (DQN). This article is an excerpt from the book Deep Reinforcement Learning Hands-on, Second Edition by Max Lapan. This book provides you with an introduction to the... Read more
Building a Convolutional Neural Network: Male vs Female
In this blog, we are going to classify images using Convolutional Neural Network (CNN), and for deployment, you can use Colab, Kaggle, or even use your local machine since the dataset size is not very large. At the end of this, you will be able to... Read more
Adversarial Attacks on Deep Neural Networks
Our deep neural networks are powerful machines, but what we don’t understand can hurt us. As sophisticated as they are, they’re highly vulnerable to small attacks that can radically change their outputs. As we go deeper into the capabilities of our networks, we must examine how... Read more