We're supposed to eat less, work out more and use less salt. These goals rarely materialize into a productive pattern. Even with the incredible amount of information available, we choose not to change. Artificial neural networks (ANN) have the ability to influence medical diagnoses and change our behavior. Change is more than what you should or shouldn't do. How you connect data and squeeze out information also impacts our ability to change.

All of us have been in a hospital for one or another reason and are aware of the medical procedures that follow during initial check up and diagnosis. Sometimes it gets a lot tedious to get so many tests done before a diagnosis is made and even some of the diseases reach a stage where they become incurable before their clinical diagnosis, like cancers. To circumvent this worldwide issue of timely diagnosis and to make procedures less laborious, researchers and technologist are working together to develop doctors of future by integrating a large set of data into computers along with sophisticated algorithms to sort useful information out of it. These systems, although, are in their initial stage of development but they are already making an impact on the field of medicine. Here are some of the popular AI systems that are paving the way for future medicine.

We have discussed Tensorflow before on this blog when we talked about some common libraries used by data science professionals. It doesn't hurt to talk about it again though! The fact is, if you are in the world of machine learning, you have probably heard, tried, or implemented some form of deep learning algorithm. Are they necessary, not all the time. Are they cool when done right, yes.

For example, deep learning, a subset of AI and machine learning, requires massive amounts of data and computational power, but until recently, few viable use cases existed for deep learning, largely due to a lack of data. Today, we're beginning to see deep learning technology take hold, thanks to the massive amounts of data that IoT devices create. In the coming months, the relationship between IoT and AI will become even more symbiotic – and we'll likely see AI, including deep learning, play a critical role in the next big thing for IoT.

While deep learning racks up the likes among the big data crowd, a potentially bigger phenomenon is the emergence of extremely simple machine learning models that do not require sophisticated technical and mathematical skills, or what machine learning expert Ted Dunning calls "cheesy and cheap machine learning," or simply "cheap learning." "Deep learning is all the rage, all the fashion, and of course everybody has to like it because of that, because we're all so fashionable in the tech industry," Dunning, who is MapR Technologies' chief application architect, tells Datanami in a recent interview. "But what also is happening is cheap learning, which are very simple models to solve very simple problems, but which in aggregate give a very large value." In the new cheap learning age, developers will avail themselves to the powerful and easy to use machine learning frameworks -- and do so without having to understand all the complex mathematics driving the predictions and recommendations and optimizations under the covers, Dunning says. "They can use machine learning without even realizing it's machine learning, and without having to become mathematical sophisticates," says says Dunning, who co-authored with Ellen Friedman two machine learning books, including "Practical Machine Learning: Innovations in Recommendation" and "Practical Machine Learning: A New Look at Anomaly Detection."

Why is deep learning called deep? It is because of the structure of those ANNs. Four decades back, neural networks were only two layers deep as it was not computationally feasible to build larger networks. Now, it is common to have neural networks with 10 layers and even 100 layer ANNs are being tried upon. Using multiple levels of neural networks in deep learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans. Normally data scientists spend a lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and makes life easier. To spur this development, many technology companies have made their deep learning libraries as open source, like Google's Tensorflow and Facebook's open source modules for Torch. Amazon released DSSTNE on GitHub, while Microsoft also released CNTK -- its open source deep learning toolkit -- on GitHub.

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on. In this article, we propose a solution to solve this problem based on a novel deep learning model dubbed Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machine (DFFW-CRBM). Our method improves state-of-the-art deep learning techniques for high dimensional time-series modeling by introducing a novel tensor factorization capable of driving forth order Boltzmann machines to considerably lower energy levels, at no computational costs. DFFW-CRBMs are capable of accurately estimating, recognizing, and performing near-future prediction of three-dimensional trajectories from their 2D projections while requiring limited amount of labeled data. We evaluate our method on both simulated and real-world data, showing its effectiveness in predicting and classifying complex ball trajectories and human activities.

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training methodcalled "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization. While it might not be the sexy story that generates the unending wave of headlines around deep learning, hyperparameter tuning is a big barrier when it comes to new leaps in deep learning performance. In more traditional machine learning, there are plenty of open sources tools for this, but where it is needed most is in deep learning--an area that does appear to be gaining a solid enterprise foothold outside of the initial web companies that spun services based on image, speech, and video recognition. Optimizing traditional machine learning and newer deep learning frameworks like TensorFlow is not simple--and it can have an incredible impact when it is done (or not done) well, providing many orders of magnitude improvements in accuracy, performance, or efficiency--depending on what users tune for. Configuring around the number and scope of hypermeters in a TensorFlow-driven workload leaves humans in the dust and optimizing with brute force methods is computationally wasteful, at least if there is a more targeted, streamlined way of knob-turning for the desired model modifications (performance, accuracy, etc.).

We are trying to put all the AI related resources in one place so that anyone can find their relevant information from this page. The list contains all the resources for beginners, advanced learners as well as for researchers. We will keep updating the list. Please feel free to give any suggestions and comments to make this list better.