Recently, there has been a lot of work on automating machine learning, from a selection of appropriate algorithm to feature selection and hyperparameters tuning. Several tools are available (e.g. AutoML and TPOT), that can aid the user in the process of performing hundreds of experiments efficiently. Likewise, the deep neural network architecture is usually designed by experts; through a trial and error approach. Although, this approach resulted in state-of-the-art models in several domains but is very time-consuming.

Artificial intelligence is bringing new demands to processors. The algorithmic data crunching is different from earlier models of processing data highlighted by benchmarks like LINPACK. It is also changing computing architectures by de-emphasizing the CPU and harnessing the faster computing power of coprocessors. The CPU is just a facilitator, and a lot of deep-learning is done on accelerator chips like GPUs, FPGAs and Google's Tensor processing unit. Major hardware companies like IBM, Intel, Nvidia and AMD are embracing the change in architecture and tuning hardware that encourage the creation of artificial neural nets, as envisioned by researchers in 1960s.

In this post, I will be sharing how one can use popular deep learning models for their own specific task using transfer learning. We will cover some concepts like differential learning rates which are not even currently in implementation in some of the deep learning libraries. I have learned about these from the fast.ai This course content will be available to the general public early 2018 as a MOOC. It is the process of using the knowledge learned in one process/activity and applying it to a different task. Let us take a small example, a player who is good at carroms can apply that knowledge in learning how to play a game of pool.

For the next 90 days, data scientists will have the chance to submit algorithms that can identify nuclei in cell samples without human intervention.

Previously, we talked about object detection, what it is and how it has been recently tackled using deep learning. If you haven't read our previous blog post, we suggest you take a look at it before continuing. Last year, we decided to get into Faster R-CNN, reading the original paper, and all the referenced papers (and so on and on) until we got a clear understanding of how it works and how to implement it. We ended up implementing Faster R-CNN in Luminoth, a computer vision toolkit based on TensorFlow which makes it easy to train, monitor and use these types of models. So far, Luminoth has raised an incredible amount of interest and we even talked about it at both ODSC Europe and ODSC West. Based on all the work developing Luminoth and based on the presentations we did, we thought it would be a good idea to have a blog post with all the details and links we gathered in our research as a future reference for anyone is interested in the topic. Faster R-CNN was originally published in NIPS 2015. After publication, it went through a couple of revisions which we'll later discuss.

Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration. Naturally, this has led to the belief that these intelligent security solutions will spot - and stop - the next WannaCry attack much faster than traditional, legacy tools. "It's still a nascent field, but it is clearly the way to go in the future. Artificial intelligence and machine learning will dramatically change how security is done," said Jack Gold, president and principal analyst at J.Gold Associates, when speaking recently to CSO Online.

Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing is considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.

AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology. The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry. These definitions aren't meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added.

In 2017 we could not get away from the conversation around artificial intelligence. Is it good, is it bad, will it take our jobs, or will it help solve the world's problems? There are conflicting answers for all of the questions posed, but the reality of artificial intelligence and machine learning is that it is here. The technology that seemed far, far away is here. Our apps, software, smart phones, cameras, cars, and smart speakers, are no longer just reacting, they are learning our behavior and adapting to the way we use them.

Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.