Thanks to the strength of the open source community, the second part is getting easier every day. There are many great tutorials on the specifics of how to train and use Deep Learning models using libraries such as TensorFlow -- many of which publications like Towards Data Science publish on a weekly basis. The implication of this is that once you have an idea for how you'd like to use Deep Learning, implementing your idea, while not easy, involves standard "dev" work: following tutorials like the ones linked throughout this article, modifying them for your specific purpose and/or data, troubleshooting via reading posts on StackOverflow, and so on. They don't, for example, require being (or hiring) a unicorn with Ph.D who can code original neural net architectures from scratch and is an experienced software engineer. This series of essays will attempt to fill a gap on the first part: covering, at a high level, what Deep Learning is capable of, while giving resources for those of you who want to learn more and/or dive into the code and tackle the second part.

According to dual process theory human reasoning consists of two different kinds of thinking. System 1 is a fast, unconscious and automatic mode of thought, also known as intuition. System 2 is a slow, conscious, explicit and rule-based mode of reasoning that is believed to be an evolutionarily recent process. When learning to complete a challenging planning task, such as playing a board game, humans exploit both processes: strong intuitions allow for more effective analytic reasoning by rapidly selecting interesting lines of play for consideration. Repeated deep study gradually improves intuitions.

Tasks that humans take for granted are often difficult for machines to complete. That's why when you're asked to prove yourself human through those CAPTCHA tests, you're always asked a ridiculously simple question, e.g., whether an image contains a road sign or not, or selecting a subset of images that contain food (see Moravec's Paradox). These tests are effective in determining whether a user is human precisely because image recognition in context is difficult for machines. Training computers to accurately answer these kinds of questions in an automated, efficient way for large amounts of data is complicated. To get around this, companies like Facebook and Amazon spend a lot of money to manually deal with image and video classification problems.

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role of the recurrent neural network in addressing the problem. In this post, you will discover the inject and merge architectures for the encoder-decoder recurrent neural network models on caption generation. Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model Photo by Bernard Spragg.

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role of the recurrent neural network in addressing the problem. In this post, you will discover the inject and merge architectures for the encoder-decoder recurrent neural network models on caption generation. Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model Photo by Bernard Spragg.

Look what I got for Christmas!! If you don't recognize it, it's two Titan V cards from Nvidia. A single Titan V has a systolic array unit dubbed as a TensorCore that is capable of 110 teraflops peak performance. In addition, it includes a conventional GPU that's capable of 25 teraflops half-precision. So we are roughly speaking here about 135 teraflops (half-precision) per card.

Natural language conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. Previous works in this direction mainly focus on either rule-based or learning-based methods. These types of methods often rely on manual effort in designing rules or automatic training of model with a particular learning algorithm and a small amount of data, which makes it difficult to develop an extensible open domain conversation system. Chatbots (also known as Conversation Agents or Dialog based Agents) make use of Natural Language conversation models. They are the latest trend.

I made a melanoma diagnosis model named "Model Melanoma" based on deep learning algorithm (http://melanoma.modelderm.com). ResNet152 and VGG19 were used as a CNN model, around 300,000 images (179 classes) were used as a training dataset. For reference, the skin cancer detection model published on nature showed 0.96 with 225 melanocytic (58 malignant, 167 benign) test images of the Edinburgh dermofit library. The web-based test platform provides the miss rate or false negative rate (1-sensitivity) in the diagnosis of melanoma. In addition, we made 176 skin diseases diagnosis model (http://modelderm.com)

The Artificial Intelligence revolution is here. We are moving further into an age, where the imagination stirred from our childhood spent watching movies, is now becoming reality. Leading us into this age are the typical (and untypical) tech giants, who are fiercely competing for the next break through. Project Oxford is Microsoft's venture into the world of artificial intelligence and deep learning. It takes in several key areas, including image, facial, text and speech recognition, and hopes to implement the technology into its computer operating systems and smartphone software.

One of the best way to get better at machine learning and deep learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. In this article, I have provided a list of YouTube videos, which you can use to improve your knowledge in these areas. You've got to follow a ritual (Just Kidding!). For your ease, I have created a'to be followed' sequence / order of these videos.