Neural Networks on Batı Şengül
http://www.batisengul.co.uk/tags/neural-networks/
Recent content in Neural Networks on Batı ŞengülHugo -- gohugo.iobatisengul@gmail.combatisengul@gmail.comWed, 20 Nov 2019 00:00:00 +0000Wasserstein variational autoencoders
http://www.batisengul.co.uk/post/2019-11-20-wasserstein-vae/
Wed, 20 Nov 2019 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/2019-11-20-wasserstein-vae/Variational auto-encoders (VAEs) are a latent space model. The idea is you have some latent space variable $z \in \mathbb{R}^{k}$ which describes your original variables $x\in\mathbb{R}^d$ in higher dimensional space by a latent model $p(x|z)$. Let’s assume that this distribution is given by a neural network with some parameters $\theta$ so that we assume $$ x | z, \theta \sim N(g_\theta(z), 1). $$ Of course in reality, we don’t know $(z, \theta)$, we would like to infer these from the data.FizzBuzz with neural networks and NALU
http://www.batisengul.co.uk/post/2019-03-16-fizzbuzz-with-neural-networks-and-nalu/
Sat, 16 Mar 2019 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/2019-03-16-fizzbuzz-with-neural-networks-and-nalu/FizzBuzz is one of the most well-known interview questions. The problem is stated as: > Write the numbers from 0 to n replacing any number divisible by 3 with Fizz, divisible by 5 by Buzz and divisible by both 3 and 5 by FizzBuzz. The example program should output 1, 2, Fizz, 3, Buzz, Fizz, 7, 8, Fizz, Buzz.
A while back, there was this infamous post where the author claimed to solve this problem in an interview using tensorflow.From Zero To State Of The Art NLP Part II - Transformers
http://www.batisengul.co.uk/post/2019-03-12-from-zero-to-state-of-the-art-nlp-part-two/
Tue, 12 Mar 2019 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/2019-03-12-from-zero-to-state-of-the-art-nlp-part-two/<div class="jupyter-cell markdown">
<p>Welcome to part two of the two part series on a crash course into state of the art natural language processing. This part is going to go through the transformer architecture from <a href="https://arxiv.org/abs/1706.03762">Attention Is All You Need</a>. If you haven’t done so already, read the <a href="http://www.batisengul.co.uk/post/2019-03-06-from-zero-to-state-of-the-art-nlp-part-one/">first part</a> which introduces attention mechanisms. This post is all about transformers and assumes you know attention mechanisms.</p>From Zero To State Of The Art NLP Part I - Attention mechanism
http://www.batisengul.co.uk/post/2019-03-06-from-zero-to-state-of-the-art-nlp-part-one/
Wed, 06 Mar 2019 00:00:00 +0000batisengul@gmail.comhttp://www.batisengul.co.uk/post/2019-03-06-from-zero-to-state-of-the-art-nlp-part-one/There has been some really amazing advances in natural language processing (NLP) in the last couple of years. Back in November 2018, Google released https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html, which is based on attention mechanisms in Attention Is All You Need. In this two part series, I will assume you know nothing about NLP, have some understanding about neural networks, and take you from the start to end of understanding how transformers work.
Natural language processing is the art of using machine learning techniques in processing language.