Here’s a great tutorial on using TensorFlow to do regression with multiple distinctive attributes.

As we did in the previous tutorial will use Gradient descent optimization algorithm. Additionally, we will divide our data set into three slices, Training, Testing, and validation. In our example, we have data in CSV format with columns “height weight age projects salary”. In this tutorial, we will use […]

Here’s an interesting article in Nature about the use of AI in evaluating embryos with AI — another use of computer vision in the medical field. Could this bring down healthcare costs? What if the algorithm mislabels an embryo? Are there ethical implications?

Deep learning algorithms, in particular convolutional neural networks (CNNs), have recently been used to address a number of medical-imaging problems, such as detection of diabetic retinopathy,18 skin lesions,19 and diagnosing disease.20 They have become the technique of choice in computer vision and they are the most successful type of models for image analysis. Unlike regular neural networks, CNNs contain neurons arranged in three dimensions (i.e., width, height, depth). Recently, deep architectures of CNNs such as Inception21 and ResNet22 have dramatically increased the progress rate of deep learning methods in image classification.23 In this paper, we sought to use deep learning to accurately predict the quality of human blastocysts and help select the best single embryo for transfer (Fig. 1).

Here’s another story of how big data and high performance computing and TensorFlow is reshaping medicine as we know it.

Virtual drug screening has the potential to accelerate the development of new treatments. Using molecular docking, molecular dynamics and other algorithms, researchers can quickly screen for new drug candidates. This saves the enormous expense and time that would have been required to make the same conclusions about those candidates in the lab and in clinical trials.

Machine Learning and AI have clearly taken off and demand for Machine Learning Engineers is at an all time high.

This is happening now due to the confluence of evolving technology and massive amounts of data. This article from edureka goes over the most  amazing Machine Learning Projects you should definitely know about and experiment with.

Here’s an example that anyone drowning in email can relate to:

Business Challenge: Motion Studio is the largest Radio production house in Europe. Having a revenue of more than a Billion Dollars, the company has decided to launch a new reality show: RJ Star. Response to the show is unprecedented and the company is flooded with voice clips. You as an ML expert have to classify the voice as either male/female so that the first level of filtration is quicker.

It doesn’t take a fortune teller to see that AI on IoT is going to be where the next wave of innovation and opportunity is going to be.

Here’s an interesting piece in VentureBeat that explores the space and why it’s taking off now.

“We’re seeing things today that people have always seen in movies and dreamed of doing at home become ordinary everyday use cases for users on their smartphones,” says Jeff Gehlhaar, Vice President, Technology and Head of AI Software Platforms at Qualcomm Technologies, Inc.

That includes always-on capabilities, for instance, smartphone assistant features like voice wake-up, always-on noise suppression, language understanding, disambiguation of circumstance, or ability to hear and understand you at varying distances from your device’s speaker. It also powers on-demand, high-performance smartphone capabilities such as instantaneous language translation and more.

The battle for top talent in the AI space is heating up with Apple looking to boost their AI stable. This reminds me of the 90’s when major tech firms would poach talent from each other with impunity. It’s interesting to see how “hot” AI skills have become.

Artificial Intelligence Apple has poached another top engineer from Google as it continues to grow its artificial intelligence and machine learning divisions, with Google’s Dr. Ian Goodfellow having left his role as a “Senior Staff Research Scientist” with Google to join Apple as a “Director of Machine Learning” in […]

The most popular dataset on Kaggle is  Credit Card Fraud Detection. It’s an easy to understand problem space and impacts just about everyone. Fraud detection is a practical application that many businesses care about.  There’s a also something intrinsically cool about stopping crime with AI.

Here’s an interesting article on how to implement a fraud detection system with TensorFlow, PySpark, and Cortex.

While it would be cool to just build an accurate model, it would be more useful to build a production application that can automatically scale to handle more data, update when new data becomes available, and serve real-time predictions. This usually requires a lot of DevOps work, but we can do it with minimal effort using Cortex, an open source machine learning infrastructure platform. Cortex converts declarative configuration into scalable machine learning pipelines. In this guide, we’ll see how to use Cortex to build and deploy a fraud detection API using Kaggle’s dataset.