Before Cortana, before Siri, before Alexa, even before IVR systems, there was Clippy. Clippy was one of Microsoft’s Office Assistant, that shipped as part of Microsoft Office in 1997. Even though Clippy is no more, he lives on as a meme. Here’s an interesting look at his origin story.
This article from Analytics India Magazine lists 10 comparisons between the two top deep learning frameworks: PyTorch and TensorFlow.
Libraries play an important role when developers decide to work in machine learning or deep learning researches. According to this article, a survey based on a sample of 1,616 ML developers and data scientists, for every one developer using PyTorch, there are 3.4 developers using TensorFlow.
As machine learning becomes more and more mainstream the more applications can be made smarter with the use of AI.
In the past, Web applications were comparatively simpler in nature. They mostly functioned as data collection platforms with simple interfaces. With the prolific growth in Web technologies, these apps have evolved into complex and dynamic entities.
Machine learning (ML) is evolving rapidly and is being applied to various domains. Web apps, too, can be enriched with ML capabilities and become more powerful. Machine learning can be incorporated into Web applications in two ways:
Here’s an interesting and skeptical walk through of neural networks vs deep neural networks and what, if anything, makes them different.
Here’s an excerpt:
The big bang of deep learning – or at least when I heard the boom for the first time – happened in an image recognition project, the ImageNet Large Scale Visual Recognition Challenge, in 2012. In order to recognize images automatically, a convolutional neural network with eight layers – AlexNet – was used. The first five layers were convolutional layers, some of them followed by max-pooling layers, and the last three layers were fully connected layers, all with a non-saturating ReLU activation function. The AlexNet network achieved a top-five error of 15.3%, more than 10.8 percentage points lower than that of the runner up. It was a great accomplishment!
Here’s an insightful blog post on the future of RL (reinforcement learning): Deep RL and why it’s going to be revolutionary.
Until few years back, reinforcement learning techniques were constrained on small discrete systems. An increase in state space(different parameters of the system), the memory and computation power increases exponentially. Before apply reinforcement learning techniques even continuous systems had to be discretized. Many things are now possible with the recent breakthroughs of Deep Neural Networks(DNN), and specially its approximation capability. Combining Reinforcement Learning and DNN, we have developed techniques taking advantage of both fields. The new field is called Deep Reinforcement Learning (DRL) and is responsible for unimaginable breakthroughs in many domains.
Lex Fridman of MIT demonstrates Driver Activity Recognition in a self-driving car by playing Black Betty on the guitar. Yes, you read that right. What a time to be alive, amirite?!
Here’s an interesting tutorial for Keras and TensorFlow that predicts employee retention.
In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll use this dataset available at Kaggle, which has features that measure employee satisfaction in a company. To create this model, you’ll use the Keras sequential layer to build the different layers for the model.
In case you have not heard already, Azure Cosmos DB is Microsoft’s globally distributed, horizontally partitioned, multi-model database service. The service is designed to allow customers to elastically (and independently) scale throughput and storage across any number of geographical regions.
What’s more, Azure Cosmos DB offers guaranteed low latency at the 99th percentile, 99.99% high availability, predictable throughput, and multiple well-defined consistency models.
How does it do this? Check out this post on the team’s blog.
The core type system of Azure Cosmos DB’s database engine is atom-record-sequence (ARS) based. Atoms consist of a small set of primitive types e.g. string, bool, number etc., records are structs and sequences are arrays consisting of atoms, records or sequences. The database engine of Azure Cosmos DB is capable of efficiently translating and projecting the data models onto the ARS based data model. The core data model of Azure Cosmos DB is natively accessible from dynamically typed programming languages and can be exposed as-is using JSON or other similar representations. The design also enables natively supporting popular database APIs for data access and query. Azure Cosmos DB’s database engine currently supports DocumentDB SQL, MongoDB, Azure Table Storage, and Gremlin graph query API.