Julie  Donnelly

Julie Donnelly


Lecture: CNN Applications, RNN, and Attention

Week 6 – Lecture: CNN applications, RNN, and attention

0:00:00 – Week 6 – Lecture

LECTURE Part A: http://bit.ly/pDL-en-06-1
We discussed three applications of convolutional neural networks. We started with digit recognition and the application to a 5-digit zip code recognition. In object detection, we talk about how to use multi-scale architecture in a face detection setting. Lastly, we saw how ConvNets are used in semantic segmentation tasks with concrete examples in a robotic vision system and object segmentation in an urban environment.
0:00:43 – Word-level training with minimal supervision
0:20:41 – Face Detection and Semantic Segmentation
0:27:49 – ConvNet for Long Range Adaptive Robot Vision and Scene Parsing

LECTURE Part B: http://bit.ly/pDL-en-06-2
We examine Recurrent Neural Networks, their problems, and common techniques for mitigating these issues. We then review a variety of modules developed to resolve RNN model issues including Attention, GRUs (Gated Recurrent Unit), LSTMs (Long Short-Term Memory), and Seq2Seq.
0:43:40 – Recurrent Neural Networks and Attention Mechanisms
0:59:09 – GRUs, LSTMs, and Seq2Seq Models
1:16:15 – Memory Networks

#deep-learning #artificial-intelligence #machine-learning #data-science #developer

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Lecture: CNN Applications, RNN, and Attention
Gerhard  Brink

Gerhard Brink


The Rising Value of Big Data in Application Monitoring

In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.

From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis  to find breakthroughs and marketable insights.

Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.

According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.

Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.

In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.

#ai in business #ai application #application monitoring #big data #the rising value of big data in application monitoring #application monitoring

Why Transformers Are Becoming As Important As RNN & CNN?

Google AI unveiled a new neural network architecture called Transformer in 2017. The GoogleAI team had claimed the Transformer worked better than leading approaches such as recurrent neural networks and convolutional models on translation benchmarks.

Read more: https://analyticsindiamag.com/why-transformers-are-increasingly-becoming-as-important-as-rnn-and-cnn/

#rnn #cnn #googleai #google

Best of Crypto

Best of Crypto


Intro to Neural Networks: CNN vs. RNN

In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.

What is the Difference Between CNN and RNN?

The main difference between CNN and RNN is the ability to process temporal information or data that comes in sequences, such as a sentence for example. Moreover, convolutional neural networks and recurrent neural networks are used for completely different purposes, and there are differences in the structures of the neural networks themselves to fit those different use cases.

CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.

While it is a frequently asked question, once you look at the structure of both neural networks and understand what they are used for, the difference between CNN and RNN will become clear.

To begin, let’s take a look at CNNs and how they are used to interpret images.

#ai #machine-learning #artificial-intelligence #cnn #rnn #neural-networks #data-science #hackernoon-top-story

Willa Anderson

Willa Anderson


Here Are The Features That A Cloud Based SaaS Application Requires

Fast setup and slick UIs create incredible first impressions on users. However, enterprise managers are aware of the fact that they are at the tip of the iceberg. One of the features of a SaaS is interoperability, and such aspects are the ones that business owners need to lay a solid foundation.

Are you aware of the term “Software as a Service (SaaS)?” You probably heard it several times, but you may not know what it’s all about. Well, a SaaS, designed by a cloud-based application development company, is a cloud-based service that helps consumers gain access to software applications over the web. These applications remain hosted on the cloud and used for various purposes by companies as well as individuals.

SaaS created by a cloud-based application development company is the best alternative to traditional software installation systems. You may compare it with a TV channel that’s available for subscription. The user connects to a remotely-located base on a central server and uses a license to access data.

In other words, SaaS offers a method of software delivery by which you can access data from any device connected to the internet. Of course, this particular device should have a web browser. Software vendors host everything associated with the application, including servers, code, and databases.

Explore more: https://www.moontechnolabs.com/blog/here-are-the-features-that-a-cloud-based-saas-application-requires/

#mobile-application-development #cloud-based-saas-application #on-demand-applications #moontechnolabs #application-development-services

Joseph  Murray

Joseph Murray


Top 5 Java Web Application Technologies You Should Master in 2021

Web Development in Java

Java is a commonly used language for web development, especially on the server-side. Java web applications are distributed applications that run on the internet. Web development with Java allows us to create dynamic web pages where users can interact with the interface.

There are various ways through which you can create dynamic web pages in Java. The Java EE (Enterprise Edition) platform provides various Java technologies for web development to developers. Services like distributed computing, web services, etc. are provided by Java EE. Applications can be developed in Java without using any additional scripting language. Let us see how web applications are made via Java.

**Java Web Application **

Java Web Application Technologies

#software development #java #java web applications #web applications #java web application technologies #top 5 java web application technologies you should master