Deep Learning


Deep Learning with Taxonomic Loss for Plant Identification

#artificialintelligence

Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively.


Deep Learning with Taxonomic Loss for Plant Identification

#artificialintelligence

Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively.


Moon Jellyfish and Neural Networks

#artificialintelligence

As efforts to make machine learning easier more accessible increase, different companies are creating tools to make the creation and optimization of deep learning models simpler. As VentureBeat reports, Amazon launched a new tool designed to help create and modify machine learning models in just a few lines of code. Carrying out machine learning on a dataset is often a long, complex task. The data must be transformed and preprocessed, and then the proper model must be created and customized. Tweaking the hyperparameters of a model and then retraining can take a long time, and to help solve issues like this Amazon has launched AutoGluon.


Tensorflow 2.0: Deep Learning and Artificial Intelligence

#artificialintelligence

Created by Lazy Programmer Inc., Lazy Programmer Team Comment Policy: Please write your comments according to the topic of this page posting. Comments containing a link will not be displayed before approval.


Deep Java Library: New Deep Learning Toolkit for Java Developers

#artificialintelligence

At the 2019 AWS re:Invent conference, Amazon released Deep Java Library (DJL), an open-source library with Java APIs to simplify training, testing, deploying, and making predictions with deep-learning models. While Java remains the first or second most popular programming language since the late 90s, Python is the most used language for machine learning, with numerous resources and deep-learning frameworks. DJL aims to make deep-learning open-source tools accessible to Java developers, using familiar concepts and intuitive APIs. Java developers can use their favorite IDE with DJL or Jupyter Notebook-based code execution for Java. DJL is framework agnostic; it abstracts away commonly used deep-learning functions, using Java Native Access (JNA) on top of existing deep-learning frameworks, currently providing implementations for Apache MXNet and TensorFlow.


RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

#artificialintelligence

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends.


Deep Learning (Interview With Leon Bottou)

#artificialintelligence

His research career took him to AT&T Bell Laboratories, AT&T Labs Research, NEC Labs America and Microsoft. He joined Facebook AI Research in 2015. The long term goal of Léon's research is to understand how to build human-level intelligence. Although reaching this goal requires conceptual advances that cannot be anticipated at this point, it certainly entails clarifying how to learn and how to reason. Leon Bottou best known contributions are his work on neural networks in the 90s, his work on large scale learning in the 00's, and possibly his more recent work on causal inference in learning systems.


This Week in AI – Issue #2 Rubik's Code

#artificialintelligence

Every week we bring to you best AI research papers, articles and videos that we have found interesting, cool or simply weird that week. Rubik's Code is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the services we provide. Eager to learn how to build Deep Learning systems using Tensorflow 2 and Python? Get our'Deep Learning for Programmers' ebook here!


Detection of Surface Cracks in Concrete Structures using Deep Learning

#artificialintelligence

We used Adam as the optimizer and train the model for 6 epochs. We use transfer learning to then train the model on the training data set while measuring loss and accuracy on the validation set. As shown by the loss and accuracy numbers below, the model trains very quickly. After the 1st epoch, train accuracy is 87% and validation accuracy is 97%!. This is the power of transfer learning. Our final model has a validation accuracy of 98.4%.


Introduction to Encoder-Decoder Models -- ELI5 Way

#artificialintelligence

My name is Niranjan Kumar and I'm a Senior Consultant Data Science at Allstate India. In this article, we will discuss the basic concepts of Encoder-Decoder models and it's applications in some of the tasks like language modeling, image captioning, text entailment, and machine transliteration. Citation Note: The content and the structure of this article is based on my understanding of the deep learning lectures from One-Fourth Labs -- PadhAI. Before we discuss the concepts of Encoder-Decoder models, we will start by revisiting the task of language modeling. Language Modeling is the task of predicting what word/letter comes next.