In this third part of my Deep Learning series, we will present six different types of neural networks, provide a brief overview of each, then delve a little deeper into each network and its subtypes. Neural networks use mathematical functions found in the calculus and linear algebra realm of mathematics, however, I will try to avoid getting into the math. There are better places to learn this level of mathematics than inside a technical blog. So let's see how much further down the rabbit hole we can go. The Feedforward Neural Network was the first and simplest of all neural networks.

With AI (Artificial Intelligence) being the one of the hottest topics of 2017 for #InsurTech, here is a quick round-up of some of the takeaway points from the AI Summit that I attended earlier as part of the 2017 InsurTech Rising event. This is interesting, because within the insurance industry, many understand this fact but the distinction between AI, Machine Learning (ML) and Deep Learning (DL) is still misunderstood. AI being so topical is fuelling the misbelief that it is new. ML which is a subset of AI, is where machines learn a function from the data, namely patterns and trends, which we as humans can't always determine ourselves and not as quickly. DL which is a subset of ML, and thus, a subset of AI, is where neural networks (on a much bigger scale) work to think like humans.

Captioning an image involves generating a human readable textual description given an image, such as a photograph. It is an easy problem for a human, but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. Recently, deep learning methods have displaced classical methods and are achieving state-of-the-art results for the problem of automatically generating descriptions, called "captions," for images. In this post, you will discover how deep neural network models can be used to automatically generate descriptions for images, such as photographs. Describing an image is the problem of generating a human-readable textual description of an image, such as a photograph of an object or scene.

These new applications require a new way of thinking about the development process. Traditional application development has been enhanced by the idea of DevOps, which forces operational considerations into development time, execution, and process. In this tutorial, we outline a "cognitive DevOps" process that refines and adapts the best parts of DevOps for new cognitive applications. Specifically, we cover applying DevOps to the training process of cognitive systems including training data, modeling, and performance evaluation. A cognitive or artificial intelligence (AI) system fundamentally exhibits capabilities such as understanding, reasoning, and learning from data. At a deeper level, the system is built upon a combination of various types of cognitive tasks, which, when combined, make up a part of the overall cognitive application. The science upon which a cognitive system is built includes, but is not limited to, machine learning (ML) including deep learning and natural language processing.

Our fellowship program is for people who are beginning a career in deep learning and AI. As a Fellow, you'll have the opportunity to work on one of our teams and be mentored by a top deep learning expert. We give preference to people who would be available to join full-time after their fellowship. Fellowships are available year-round, and typically last 4-6 months. In addition, candidates should be at ease with programming and be motivated by AI and its potential.

Keras is definitely the weapon of choice when it comes to building deep learning models ( with tensorflow backend). At SearchInk, we are solving varied problems in the field of document analysis by architecting and implementing deep learning models. One of the bigger challenges in doing this is the time taken to run each experiment. With the need for more and more experimentation to be carried out in shorter spans of time, we decided it was the right time for us to start distributed computations on the GPU for deep learning models. We were evaluating different options on how to perform distributed GPU computing and we stumbled upon Horovod.

Video streaming has been popular for quite some time, but its growth seems to keep expanding. While it was initially popularized by platforms such as YouTube and then Facebook, today they are being joined by video-on-demand services such as Netflix, Amazon and Hulu. Recently a study suggested that as much as 70% of all online traffic consists of streaming video and audio, and it is placing a heavy demand on internet bandwidths across the world. Although internet speeds and bandwidth have grown over the years, the demand for videos with higher resolutions and bitrates has grown as well. To help optimize the usage of available bandwidth for streaming video, most streaming platforms use algorithms known as Adaptive Bitrate (ABR). Traditional ABR algorithms are either rate-based that vary the video quality based on connection speed, or buffer-based that attempt to constantly keep a certain percentage of the video pre-loaded as a buffer so that the stream is smooth.

Bengaluru: Hewlett Packard Enterprise (HPE) today announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. To help customers overcome these challenges and realize the potential of AI, HPE is announcing the following offerings: • HPE's Rapid Software Development for AI: HPE introduced an integrated hardware and software solution, purpose-built for high performance computing and deep learning applications.

On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe. The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.

This is the code for my article "Coloring B&W portraits with neural networks" Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. Read the article to understand the context of the code. If you are new to FloydHub, do their 2-min installation, check my 5-min video tutorial or my step-to-step guide - it's the best (and easiest) way to train deep learning models on cloud GPUs.