Deep learning is already helping the designers to make a new edition of a product. With the help of data from the sensors and some of the finest deep learning methods, the experts are able to optimize the designs. We have already seen that deep learning, AI, ML and big data have augmented creativity the design space. The biggest example is the IBM's machine learning system, Watson. A large number of images were inserted into the system, and of the images were the work of artist Gaudi.

David Duvenaud was collaborating on a project involving medical data when he ran up against a major shortcoming in AI. An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient's health over time. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. A traditional neural network struggles to handle this. Its design requires it to learn from data with clear stages of observation.

Interested in understanding the algorithm used by AlphaGo to beat the human world champion? Then this article is for you. We will discuss what is Reinforcement learning (RL), Elements of Reinforced Learning, terms related to RL like value function, and Q value function. As kids, teenagers or grownups, when we we need to learn a new skill, we either have someone to help or we learn on our own by trial and error. Let's map this to Reinforced Learning.

Deep learning has been used to identify 1.47 million solar installations across the United States, exceeding the latest estimate of 1.02 million. What's new: Solar panels are becoming increasingly popular across the US, but it's proved difficult to pinpoint their exact number. Researchers from Stanford University have got us much closer, thanks to a new system called DeepSolar, which uses deep learning to scan satellite images for solar panels. How it worked: The team trained DeepSolar on 370,000 satellite images by teaching it which ones included solar panels. The program then worked out how to spot solar panels, finding them correctly 93% of the time. It took about a month for the system to scan the billion images needed to reach its final figure.

Speech recognition systems has been one of the most developed areas in the deep learning ecosystem. The current generation of speech recognition models rely mostly on recurrent neural networks(RNNs) for acoustic and language modeling and on computationally-expensive artifacts such as feature extraction pipelines for knowledge building. While RNN-based techniques have proven to be effective in speech recognition tasks, they require large volumes of training data and computing power often resulting prohibited for most organizations. Recently, Facebook AI Research(FAIR) team published a research paper proposing a new speech recognition technique based solely on convolutional neural networks(CNNs). The FAIR team went beyond research and open sourced the Wav2letter, a high performance speech recognition toolkit based on the fully-convolutional method. The use of CNNs in speech recognition systems is not new in the deep learning space but has been mostly constrained to specific tasks and if often combined with RNNs into more complete architecture.

In today's world, we are making extensive use of images. Have you ever wonder how Facebook automatically detects faces in an image and then tells who is in the image like it did in the above figure. When I move the pointer near to one of the faces in the image, it automatically tells the name of the person and which is none other than Yann LeCun (who proposed the CNN Architecture). Have you ever used Snapchat? It automatically first detects your face and then accordingly apply the selected filter chosen by you. Something similar is also done by Apple's Face ID which will automatically unlock your phone when it founds your face in front of the device.

We humans model the world and use our intelligence to understand the way things work and use that understanding and knowledge to solve difficult and complex problems. Modeling is the process of understanding the "reality", the world around us, but creating a higher level prototype that will describe the things we are seeing, hearing and feeling, but it's a representative thing, not the "actual" or "real" thing. The Model here is created by an AI like a deep learning (DL) algorithm. Ok so you just created a model (let's say a DL model) to solve a business problem, an now you want to have it in production. Going into production can mean something different for each one of you.

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Dr. Diagnosio that uses deep learning, matures to a virtual doctor with the capability to make both differential diagnoses and to triage patients. Dr. Diagnosio is now able to triage patients based on a questionnaire on the patient symptoms progression. The Swedish company BraineHealth recently launched Dr. Diagnosio – a virtual doctor that proposes diagnoses from vital patient data such as age, gender and patient symptoms. Dr. Diagnosio uses machine-learning methods to match the most relevant diagnoses based on a validated database. The virtual doctor Diagnosio has now extended its capabilities by asking the patient additional essential questions about how the symptoms are developing, current discomfort, medication, medical condition and more.

What if you could use Artificial Intelligence to enhance your photos like those seen on TV? Image super-resolution is the technology which allows you to increase the resolution of your images using deep learning so as to zoom into your images. Image super-resolution is a software technique which will let us enhance the image spatial resolution with the existing hardware. Low Resolution(LR) Image: Pixel density within an image is small, hence it offers few details. High Resolution(HR) Image: Pixel density within an image is large, hence it offers a lot of details. A technique which is used to reconstruct a high-resolution image from one or many low-resolution images by restoring the high-frequency details is called as "Super-Resolution".