You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard datasets that are well understood and widely used so that you can compare your results to see if you are making progress. In this post, you will discover a suite of standard datasets for natural language processing tasks that you can use when getting started with deep learning. I have tried to provide a mixture of datasets that are popular for use in academic papers that are modest in size.
Artificial intelligence (AI) takes the power of computing systems to a different level. It is amazing to even think that a computing system can emulate human beings. There are many fantastic examples of AI in various areas of our lives. That said, computing systems are still considered limited in their capabilities because they cannot think creatively like human beings. While AI can process and analyze complex data, it still does not have much prowess in areas that involve abstract, nonlinear and creative thinking.
The Ubuntu DSVM is supported as a native VM image in Batch AI. The Ubuntu DSVM comes with many deep learning frameworks, GPU drivers, CUDA, and cuDNN pre-installed, so it is easy to get started with a deep learning project. Data scientists can develop an initial version of a model on a single DSVM, using a smaller dataset, then easily scale out across many DSVMs and larger datasets in Batch AI when ready. Using the same DVM image in Batch AI minimizes the setup time required for your cluster's VMs and reduces incompatibilities between Batch AI and your development environment. Batch AI handles the details of setting up your cluster, can automatically scale up and down based on demand, and supports low-priority VMs for additional cost savings.
A recent example of such work is the ICLR 2016 paper "Learning to Diagnose with LSTM Recurrent Neural Networks" (of which Mr. Kale is a joint first author in his capacity as a PhD candidate at the USC Information Science Institute). In it, the authors trained a LSTM RNN or LSTM, to classify acute care diseases such as respiratory distress in critically ill children. The RNN (and the more complex LSTM RNN) is a neural net architecture with recurrent connections between hidden states, giving it a form of persistent state (or "memory") across sequential inputs. These connections enable RNNs to detect relationships not only between inputs, e.g., heart rate and blood pressure, but also over time, e.g., between a patient's state at time of admission and later in an ICU stay. This makes it especially well-suited to health problems, which often involve modeling changes over time.
In a blog post today, Intel (NASDAQ:INTC) CEO Brian Krzanich announced the Nervana Neural Network Processor (NNP). The Intel Nervana NNP promises to revolutionize AI computing across myriad industries. Using Intel Nervana technology, companies will be able to develop entirely new classes of AI applications that maximize the amount of data processed and enable customers to find greater insights – transforming their businesses... We have multiple generations of Intel Nervana NNP products in the pipeline that will deliver higher performance and enable new levels of scalability for AI models. This puts us on track to exceed the goal we set last year of achieving 100 times greater AI performance by 2020.
Paypal has a deep learning system that filters out deceptive merchants and cracks down on sales of illegal products. Citibank's Citi Ventures arm recently invested in Feedzai, a machine learning company that identifies and prevents fraudulent transactions before they're completed. A few investment firms, including Aidyia Limited of Hong Kong, have launched funds managed entirely by AI. San Francisco startup Sentient Technologies, which develops AI software, created its own hedge fund based on its deep learning technologies. Swiss AI startup NNSAISENSE and Acatis Investments, a German fund manager, recently launched "Quantenstein," a deep learning software platform that helps investors choose the best stocks and build portfolios.
Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle. Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures.
Why is everyone talking about it all of a sudden? If you skim online headlines, you'll likely read about how AI is powering Amazon and Google's virtual assistants, or how it's taking all the jobs (debatable), but not a good explanation of what it is (or whether the robots are going to take over). We're here to help with this living document, a plain-English guide to AI that will be updated and refined as the field evolves and important concepts emerge. Artificial intelligence is software, or a computer program, with a mechanism to learn. It then uses that knowledge to make a decision in a new situation, as humans do.
As artificial intelligence continues to be adopted by businesses of all shapes and sizes, tech giants such as Google, Facebook, Apple and Microsoft are investing in AI startups left, right and centre. It can be assumed that a hiring someone to lead the AI strategy and research is paramount to the success of the company. You'd be hard pressed to find a serious tech company that doesn't have an AI team in place, and millions of dollars are being pumped into intelligent systems and solutions. Companies are no longer just hiring AI experts, but their entire business strategies centralise around their application. But what does a Chief AI Officer (CAIO) do, and do we really need them?
ODSC gather a large community of Data Scientists around the world, with 3 organizations in Europe, West and East. Apart from the conference they hold every year, they also provide a newsletter, a job board and organize meetups to animate the community. At the ODSC London 2017 there were 10 training sessions, 28 workshops and 75 talks for 1500 attendees. The various topics covered were: Deep Learning, Predictive Analytics, Machine Learning, NLP, Cognitive Computing, AI, and Data Wrangling. Many tools were presented, from Big Data tools such as Apache Spark (SQL, Mllib, Streaming), Hadoop, Apache Storm and Apache Flink, to Deep Learning tools such as Tensorflow, Caffee, Torch, and some well known visualization tools like Neo4J, D3.js, R-Shiny.