How to Choose the Right Activation Function Selecting the appropriate activation function is crucial for the performance of neural networks

Extracting Insights from Cities with Graph Neural Networks for INFORMS 2023 by Amadou Ba et al

Convolutional neural networks make ReLU activation function so popular. It doesn't saturate for positive inputs but it still tends to saturate for negative inputs. Herein, a small modification would be done and the function will produce a constant times input value for negative inputs

In this post, Karan describes the technicalities of why neural networks do not learn continually, briefly discusses how the brain is thought to succeed at learning task after task, and finally highlights some exciting work in the machine learning community that builds on fundamental principles of neural computation to alleviate catastrophic forgetting

Former Arm VP Noel Hurley joins Literal Labs to drive energy-efficient AI with Tsetlin Machines, offering a faster, lower-power alternative to neural networks

From the very simple Feed Forward Neural Networks to the majestic transformers: everything you need to know

Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way

Discover how neural networks approximate any function, why it matters, and the surprising limits of their 'universality' in real-world AI

Using Apple’s new BNNS framework to make a basic neural network

Deep Neural Networks # We can construct feedforward neural networks $f(x):\mathbb{R}^d\rightarrow\mathbb{R}^K$ with more hidden layers as follows. Denote the input of size $M_0=d$ to the neural net by $$z^{(0)}=\begin{pmatrix}x_1\\\vdots\\x_d\end{pmatrix}\in\mathbb{R}^{d}.$$ We compute $M_1$ activations making up the first hidden layer by $$z^{(1)}=\sigma(a^{(1)}),\quad a^{(1)}=W^{(1)}z^{(0)}+b^{(1)}$$ where $W^{(1)}\in\mathbb{R}^{M_1\times M_0}$ is a matrix of weights and $b^{(1)}\in\mathbb{R}^{M_1}$ is a