# A new type of deep neural network that has no layers Hello Algorithm readers, If there’s one thing you learn from spending a week with AI researchers, it’s how much uncertainty exists in the field. We still don’t really know how neural networks work, how to improve their accuracy (besides just feeding them more data), or how to fix their biases. But bit by bit, people are working together to find answers to these questions. It’s both terrifying and exciting to observe the frontlines. At NeurIPS

This video covers the architecture of a Convolutional Neural Network, focusing on the concept of "filters

Deep neural networks (DNNs) have been shown to tolerate "brain damage": cumulative changes to the network's parameters (e.g., pruning, numerical perturbations) typically result in a graceful degradation of classification accuracy. However, the limits of this natural resilience are not well understood in the presence of small adversarial changes to the DNN parameters' underlying memory representation, such as bit-flips that may be induced by hardware fault attacks. We study the effects of bitwise corruptions

Demystifying Tensor Calculus A Deep Dive into Neural Network Gradient Computation. Demystifying Tensor Calculus A Deep Dive into Neural Network Gradient

Using the weights of trained Neural Network (NN) models as data modality has recently gained traction as a research field - dubbed Weight Space Learning (WSL). Multiple recent works propose WSL

(2018) Prasetya, Tran. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). In theory, a neural network can be trained to act as an artificial specification for a program by showing it samples of the programs executi

In this Q&A with Giuseppe Bonaccorso, author of 'Mastering Machine Learning Algorithms,' read about his take on common pitfalls in artificial neural network modeling, best practices and toolkit recommendations

# Pokemon generated by neural network I’ve been playing around with char-rnn, an open-source torch add-on for character-based neural networks by Andrej Karpathy , using it to generate everything from cookbook recipes to superhero names to a Lovecraft/cookbook mashup . I decided to train the neural network to randomly generate Pokemon names and abilities based on this list as a training set - and found that it was good at generating Pokemon. Annoyingly good at it - by the time it had gone through the train

In this chapter, you will learn how to use TensorFlow 2.0 for building and training a simple neural network along with the best practices

Long Short-Term Memory Networks With Python Discover how to bring Long Short-Term Memory recurrent neural networks to your sequence prediction problems. Everything You Need To Know about LSTMs With Python Foundation topics like RNNs, BPTT and data preparation. Details on the 4 types of sequence prediction models. Discover 6 different LSTM architectures with worked examples of each. Advanced topics like model tuning, making predictions and updating models. Check Out What Customers Are Saying I really like th