Deep learning, an advanced subset of machine learning, is probably the hottest trend in AI, and for good reason. Deep nets are flexible, powerful learning algorithms that can solve some of the world’s hardest problems.
WHY DEEP LEARNING?
- Enterprises that apply deep learning will outperform their competitors in areas such as fraud detection and e-commerce recommendations. Fraud costs companies hundreds of billions of dollars annually, so being able to adapt and stop fraud (without human intervention) means enormous cost savings.
- The Internet age has produced more data than humans can cope with. For example, the popularity of cloud computing makes it difficult for sysadmins to effectively manage tens of thousands of servers using existing tools. Deep neural networks can automatically learn from signals and predict the likelihood of server failure before it happens, enabling sysadmins to rebalance their workloads, maintain uptime and save money.
- Researchers are using deep learning to tackle problems that were once deemed impossible, with new breakthroughs published every month. Deep learning is the catalyst for new insights in diverse fields such as neural science, medicine, physics, genetics and augmented reality.
- With the growth of the internet, there is lot more data available. With more data, you can train neural networks to produce more accurate models of the world. That data is being streamed in time series from IoT devices, published as text on social media, and created as logs on cloud computing instances. Big data changed everything.
- Advances in algorithms such as deep convolutional neural nets mean we can perform tasks like image recognition with record-breaking accuracy. These new powers also extend to automatic machine translation, predictive analytics and sound recognition.
- Hardware advances in GPUs and distributed cloud computing have put incredible computing power at our disposal. This level of computing power is necessary to train deep neural nets.
DL4J: JVM + GPUs + HADOOP + SPARK
- Boost existing models using cutting edge tools that has been proven elsewhere to be radically better.
- “Ready to go” deep learning that fits within the enterprise stack.