If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning projects are favorably accepted, as they were either the pioneers to providing specific niche services, or they have provided a large range of required services to users. Despite there being many projects, what will work best for you depends upon your machine learning goals - and also on the ecosystem you work in. It is possible the projects you are considering may differ, but they all have the same feature of providing services to a massive number of users. Besides the big machine learning projects, there are several smaller projects that are quite popular, as they provide both flexible and niche services for a smaller number of users. Machine learning is quite expensive.
Deep Learning ultimately is about finding a minimum that generalizes well -- with bonus points for finding one fast and reliably. Our workhorse, stochastic gradient descent (SGD), is a 60-year old algorithm (Robbins and Monro, 1951) , that is as essential to the current generation of Deep Learning algorithms as back-propagation. Different optimization algorithms have been proposed in recent years, which use different equations to update a model's parameters. Adam (Kingma and Ba, 2015)  was introduced in 2015 and is arguably today still the most commonly used one of these algorithms. This indicates that from the Machine Learning practitioner's perspective, best practices for optimization for Deep Learning have largely remained the same.
I'm always on the lookout for ideas that can improve how I tackle data analysis projects. I particularly favor approaches that translate to tools I can use repeatedly. Most of the time, I find these tools on my own--by trial and error--or by consulting other practitioners. I also have an affinity for academics and academic research, and I often tweet about research papers that I come across and am intrigued by. Often, academic research results don't immediately translate to what I do, but I recently came across ideas from several research projects that are worth sharing with a wider audience.
Are you familiar with Scikit-learn Pipelines? They are an extremely simple yet very useful tool for managing machine learning workflows. A typical machine learning task generally involves data preparation to varying degrees. We won't get into the wide array of activities which make up data preparation here, but there are many. Such tasks are known for taking up a large proportion of time spent on any given machine learning task.
Deep neural networks--a form of artificial intelligence--have demonstrated mastery of tasks once thought uniquely human. Their triumphs have ranged from identifying animals in images, to recognizing human speech, to winning complex strategy games, among other successes. Now, researchers are eager to apply this computational technique--commonly referred to as deep learning--to some of science's most persistent mysteries. But because scientific data often looks much different from the data used for animal photos and speech, developing the right artificial neural network can feel like an impossible guessing game for nonexperts. To expand the benefits of deep learning for science, researchers need new tools to build high-performing neural networks that don't require specialized knowledge.
Machine learning continues to gain headway, with more organizations and industries adopting the technology to do things like optimize operations, improve inventory forecasting and anticipate customer demand. Recent research from the McKinsey Global Institute found that total annual external investment in AI was between $8 billion and $12 billion in 2016, with machine learning attracting nearly 60 percent of that investment. What's more, organizations with senior management support for machine learning and AI initiatives reportedly stand to increase profit margins anywhere from 3 percent to 15 percent. Despite this momentum, many organizations struggle with simple machine learning best practices and miss out on the benefits as a result. Following are 10 tips for organizations who want to use machine learning more effectively.
PBT - like random search - starts by training many neural networks in parallel with random hyperparameters. But instead of the networks training independently, it uses information from the rest of the population to refine the hyperparameters and direct computational resources to models which show promise. This takes its inspiration from genetic algorithms where each member of the population, known as a worker, can exploit information from the remainder of the population. For example, a worker might copy the model parameters from a better performing worker. It can also explore new hyperparameters by changing the current values randomly.
Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more general and robust method, you have made the problem easier.
Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more generaland robust method, you have made the problem easier.
Recently, one of my friends and I were solving a practice problem. After 8 hours of hard work & coding, my friend Shubham got a score of 1153 (position 219). How did I get there? What if I tell you there exists a library called MLBox, which does most of the heavy lifting in machine learning for you in minimal lines of code? From missing value imputation to feature engineering using state-of-the-art Entity Embeddings for categorical features, MLBox has it all.