As I noted in a February blog post, many enterprises today need solutions that couple high-performance computing with data analytics. This convergence of technologies is blurring the boundaries between HPC and big data, and clearing the way forward for the advent of high-performance data analytics (HPDA). In a parallel trend, enterprises increasingly need solutions that merge technologies for machine learning and deep learning -- a need I will explore more deeply in today's post. Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence (AI) wanted to see if computers could learn from data and the process of iterative training on new data sets.

IBM recently announced that it reached a new industry record in conversational speech recognition, which could have big implications for the future of artificial intelligence (AI). The IBM team's system achieved a 5.5% word error rate--down from 6.9% last year. The benchmark was measured on a difficult speech recognition task, with the machine deciphering recorded conversations between humans discussing day-to-day topics such as buying a car. This recording is known as SWITCHBOARD, and has been used for more than two decades to test speech recognition systems, according to a blog post by George Saon, a principal research scientist at IBM. IBM used deep learning technologies to reach the 5.5% record.

Let me also mention some of the advances in my main area of expertise: Recommender Systems. Of course, Deep Learning has also impacted this area. While I would still not recommend DL as the default approach to recommender systems, it is interesting to see how it is already being used in practice, and in large scale, by products like Youtube. That said, there has been engaging research in the area that is not related to Deep Learning. The best paper award in this year's ACM Recsys went to "Local Item-Item Models For Top-N Recommendation," an interesting extension to Sparse Linear Methods (i.e.

Personally, I am working with Vamsi on some awesome content for an upcoming DZone item. I'm also preparing for Oracle Code New York, where I will be doing a talk on NiFi, Deep Learning, Machine Learning, NLTK, streaming, IoT, and Java microservices. Not sure how I will cram that into 45 minutes at 5 p.m. with a hands-on demonstration -- I think I will need compression. If you are at that event in two weeks, come say hi! Mention the secret password, "DZone," and get a free sticker. Check out the source for yourself in Using Python With Keras Intro of Deep Learning 4J (on top of Hortonworks HDP).

Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, will discuss the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

Whenever i think about the impact that deep learning will have in Africa in the next 5–10 years from now, My curiosity and passion to understand and implements deep learning increases. Improving Agriculture yields in Africa with deep learning becomes my first focus. As a result of these reasons, it is now possible for anyone to apply deep learning techniques to real problems in a way that is both affordable and fast.

Note to reader: Diving into this material here can be a bit overwhelming. Deep Learning Architecture can be described as a new method or style of building machine learning systems. Deep Learning is more than likely to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring.

TensorFlow has become the most popular AI programming project on software code sharing service GitHub, leapfrogging well-regarded systems created by universities and corporate rivals, according to data gathered by Bloomberg. Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We've used it to demonstrate that concepts like "cat" can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet's Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.