Coding agents are revolutionizing software engineering by enhancing productivity, but they require careful context management and do not replace the need for experienced developers.
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The text discusses the complexity of item crafting in the game Path of Exile and explores the question of whether an algorithm can be used to learn the optimal sequence of actions to craft a target item. It explains the item craft...
0Adopting Nix
2023-01-07 •
The text describes the author's experience with adopting Nix for the build process and CI pipeline of projects. It explains what Nix is, the problems it solves, and the challenges faced. The author discusses how Nix helps with rep...
The text explains how to implement probability monads in Haskell using less than 100 lines of code. It discusses the concept of probabilistic functional programming and how to express probability distributions as monads. The post ...
The post reviews ideas in deep learning that have stood the test of time, covering various fields such as computer vision, natural language, speech, and reinforcement learning. It discusses the historical context, links to papers ...
0Building AI Trading Systems
2020-06-26 •
The author discusses their experience building AI trading systems, including the failures and successes of their systems. They argue that it is possible to beat the market and discuss the importance of relative performance in trad...
0Replication Issues in AI research
2020-06-17 •
The text discusses the replication issues in AI research, particularly in the field of deep learning. It highlights the challenges in reproducing and replicating results, including issues with software frameworks, subtle implement...
0Learning to Trade with Reinforcement Learning
2018-02-10 •
The post discusses the potential of using reinforcement learning to train agents to trade in the financial and cryptocurrency markets. It explains the basics of market microstructure, data analysis, trading strategy metrics, super...
0AI and Deep Learning in 2017 – A Year in Review
2017-12-31 •
The text is a summary of the amazing things that happened in AI and Deep Learning in 2017. It covers topics such as Reinforcement Learning, Evolution Algorithms, WaveNets, CNNs, Attention Mechanisms, Deep Learning frameworks, Lear...
0Learning Reinforcement Learning
2016-10-02 •
The text discusses the importance of studying reinforcement learning and the resources available for learning it. It also provides a Github repository with code and exercises for implementing standard reinforcement algorithms usin...
The post is a practical guide to using Recurrent Neural Networks (RNNs) in Tensorflow. It covers best practices for working with RNNs in Tensorflow, especially the functionality that isn’t well documented on the official site. It ...
The post is about implementing a retrieval-based bot using the Ubuntu Dialog Corpus. It discusses the limitations of generative models and the advantages of retrieval-based models. It also explains the process of building a Dual E...
0Deep Learning for Chatbots, Part 1 – Introduction
2016-04-06 •
The text is an introduction to deep learning techniques used to build conversational agents, focusing on retrieval-based vs. generative models, long vs. short conversations, open vs. closed domain, and common challenges. It also d...
0Attention and Memory in Deep Learning and NLP
2016-01-03 •
The text discusses the concept of attention mechanisms in neural networks, particularly in the context of deep learning and natural language processing. It explains the problem that attention solves, how it works, and its applicat...
The post is about implementing a Convolutional Neural Network (CNN) for text classification in TensorFlow. It discusses the model presented in Kim Yoon’s paper and the dataset used for the implementation. The post also covers the ...
0Understanding Convolutional Neural Networks for NLP
2015-11-07 •
The text explains how Convolutional Neural Networks (CNNs) are used in Natural Language Processing (NLP). It describes the intuition behind CNNs, their application to NLP, and the hyperparameters involved in building a CNN archite...
This is part 4 of the Recurrent Neural Network Tutorial, focusing on LSTM and GRU networks. The post explains the equations and intuition behind LSTM and GRU networks, compares the two, and provides implementation details. It also...
This is the third part of the Recurrent Neural Network Tutorial, focusing on Backpropagation Through Time (BPTT) and the vanishing gradient problem. The tutorial explains the BPTT algorithm and the vanishing gradient problem, whic...
This is the second part of the Recurrent Neural Network Tutorial. The author explains how to implement a full Recurrent Neural Network from scratch using Python and optimize the implementation using Theano. The goal is to build a ...
The text is an introduction to Recurrent Neural Networks (RNNs) and their applications in natural language processing (NLP). It explains the concept of RNNs, their architecture, and their applications in language modeling, machine...
The text discusses how to speed up a neural network using the Theano library and GPU. It explains what Theano is, how to define computations using Theano, and how to run the code on a GPU for faster performance. The author also pr...