Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna†s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.

NeurIPS 2019 was with around 13,000 attendees the largest ML conference of the year. The NeurIPS 2019 Program Chairs did a fantastic analysis of the reviewing process. NeurIPS has no free-loader problem: Most of the authors of submitted papers participate in reviewing. It is still unclear how to filter papers before the full review. Review quality (as measured by length) is not lower compared to smaller conferences.

Santanu Pattanayak works as a Staff Machine Learning Specialist at Qualcomm Corp R&D and is an author of the deep learning book Pro Deep Learning with TensorFlow - A Mathematical Approach to Advanced Artificial Intelligence in Python. He has around 12 years of work experience and has worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time where he ranks in top 500.

Get introduced to Deep Learning and learn history of Deep Learning.

TensorFlow has become the first choice for deep learning tasks because of the way it facilitates building powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This course shows you how to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models. With this video course, you will use the power of Google's Cloud Platform to train deep neural networks faster.

Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and other diseases. Many key cancer oncogenes are driven by super-enhancers, and the mutations associated with common diseases such as Alzheimer's disease are significantly enriched with super-enhancers. Super-enhancers have shown great potential for the identification of key oncogenes and the discovery of disease-associated mutational sites. In this paper, we propose a new computational method called DEEPSEN for predicting super-enhancers based on convolutional neural network. The proposed method integrates 36 kinds of features.

OpenAI has benchmarked reinforcement learning by mitigating most of its problems using the procedural generational technique. RL has been a central methodology in the field of artificial intelligence. However, over the years, researchers have witnessed a few shortcomings with the approach. Developers often use a colossal amount of data to train and increase the efficiency of machine learning models. But this has resulted in overfitting of data in many cases, thereby, causing hindrance in the adoption of ML technologies.

Following consumer adoption and changing attitudes, purpose-built smart assistants like Alexa for Business are paving the way for AI at work. Smart assistants naturally complement intelligent smartphone apps, such as Edison, formerly EasilyDo. Using predictive analytics and deep learning, these smart apps extract meaningful, actionable data in real-time. For example, Edison proactively notifies you when it's time to leave based on traffic patterns and meeting start times. While these apps classify as consumer offerings, it's easy to understand why employees want these tools at work.

This session will outline some of the NLP tasks and approaches that were used to build and deploy an AI system that detects sensitive information in enterprise scale. In predictive language solutions - from categorizing entire documents to extracting sensitive sentences or entities - the varying context of each sample is crucial. Yet, the encoding of context in ways that machines can interpret is still possible using sufficient labeled data and deep neural networks. The road to building useful, useable AI products goes through much more grounded technologies. Research has shown that 28% of worker's time in the office is dedicated to email work.

Recent advances in machine learning have enabled the development of techniques to detect and recognize human emotions. Some of these techniques work by analyzing electroencephalography (EEG) signals, which are essentially recordings of the electrical activity of the brain collected from a person's scalp. Most EEG-based emotion classification methods introduced over the past decade or so employ traditional machine learning (ML) techniques such as support vector machine (SVM) models, as these models require fewer training samples and there is still a lack of large-scale EEG datasets. Recently, however, researchers have compiled and released several new datasets containing EEG brain recordings. The release of these datasets opens up exciting new possibilities for EEG-based emotion recognition, as they could be used to train deep-learning models that achieve better performance than traditional ML techniques.