What is machine learning / ai? How to lean machine learning in practice? Machine learning and neural networks are got a lot of attention recently. Self driving cars, predictive analytics and other highly advanced topics are closely related to those topics. Therefore it's of the utmost of importance to familiarize oneself with machine learning and neural networks.

You've eaten all the turkey, the trimmings, and the mince pies, and you're starting to turn your mind to new years resolutions. Rather than the usual resolutions of hitting the gym more, giving up chocolate, or watching less TV how about a resolution to stay ahead of the AI trends of 2018? Throughout 2018 not only are we bringing the RE•WORK AI Summits to new locations such as Hong Kong, Toronto and New York, but we have brand new topics: Deep Learning for Robotics Summit, and AI in Industrial Automation. We will also be hosting stand alone dinners and workshops throughout the year. For one week only we are offering 25% off all RE•WORK events in 2018 using the code NEWYEAR (excluding dinners).

There has been tremendous buzz around Artificial Intelligence in the past year, and I expect it only to increase in 2018. From self-driving cars to computers that create their own languages (and so had to be shut down) - we've heard and read about it all. Some of these stories depict AI accurately but many of them are plain hyperbole. There hasn't been another technological advance in the past that has the power to change our lives so profoundly and is yet so misunderstood. Computer Vision, the branch of AI that deals with making computers process and recognize images better has probably benefited the most from the recent developments in Deep Learning technology.

What is the key problem in data-science and text mining? There simply aren't enough datasets with seven figure sample sizes (from thousand to million samples) to teach neural networks in many industries, including banking, telecom, media and government agencies. Even if the business does have such datasets, using machine learning to gather, clean and make training and test sets is extremely difficult and time consuming even for skilled data scientists. Training such as this can take between 5 and 10 months for a normal sized dataset, and even though we use neural networks and Deep Learning frameworks to simplify the task, it is a one time solution - every step must be repeated over and over again to keep your linguistic models up to date.

The biggest and most important trend in 2018 will be the transformation of machine learning practice from a hand-crafted ad hoc operation to a more systematic and automated process. Data preparation, exploratory analysis, and model fitting will all be done using visual platforms that will reduce the importance of coding, and place more emphasis on the ability to formulate business problems and the understanding of techniques. The adoption of comprehensive data science platforms and the reduced importance of coding will mean that domain experts, rather than professional data scientists, will be able to do a lot of sophisticated analysis. The role of machine learning engineers will change, and they will work in a team with domain experts where they will function less as end-to-end developers, and more as technical consultants. Deep learning will continue to be the most important machine learning technology in 2018.

Databricks is giving users a set of new tools for big data processing with enhancements to Apache Spark. The new tools and features make it easier to do machine learning within Spark, process streaming data at high speeds, and run tasks in the cloud without provisioning servers. On the machine learning side, Databricks announced Deep Learning Pipelines, which are designed to make it possible for data scientists and AI novices to implement neural nets in their big data processing. It provides developers with high-level APIs designed to help with tasks like loading images, tuning a model's hyperparameters, and modifying a more general model to help in a specific case. The company has also integrated Spark's Structured Streaming feature into the beta of its enterprise product to accelerate the processing of real-time data.

Deep learning is a subset of machine learning based on neural networks. With deep learning the more data the better which can require more computing power. In this case that computing power comes from graphics processing units (GPU), as their architecture is bested suited for the job. Typically the GPU is needed in the training stage of machine learning. At this stage more cores and faster GPUs mean you can train the system faster.

An old joke claims that meteorology is the only profession where you can be wrong half of the time and still get paid. It's a true and sometimes frustrating reality that weather forecasts are not always 100 percent accurate. Sometimes this is due to a weather system that changed rapidly or unexpectedly. But incorrect predictions can also result from errors, inefficiencies, or lack of quality data in weather forecasting models. Using computer models to simulate and predict the weather, known as Numerical Weather Prediction, is still not a perfect science, but recent advancements in computing technologies combined with the growing availability of weather-related data has served to dramatically improve the accuracy of forecasts.

Artist's concept: Programmable nanophotonic processors integrated on a printed circuit board, carrying out deep learning computing. Computer systems based on artificial neural networks mimic the way the brain learns, accumulating by example. It's currently used for face- and voice-recognition software. The potential exists to use it for searching through vast amounts of medical data to find patterns useful to diagnosis, or scan chemical formulas to help develop new pharmaceuticals. But the computations required are highly complex and demanding.

The best job to have in 2017 according to Glassdoor? The #1 skill you need to start a career in Data Science? So, if you are interested in a career in data science, algorithmic trading, robotics, or any industry where human labor is getting replaced by machines, you have come to the right place! We have prepared an amazing course not only to get you acquainted with, but help you understand how deep machine learning works! Worried you have no experience?