It explores the study and construction of algorithms that can learn from and make predictions on data sets that are provided by building a model from sample data sets provided during a "training" period. In a supervised training period, a human feeds the data set to the computer along with the correct answer. The algorithms must build a model identifying how the correct answer is indeed the correct answer. An unsupervised training period is when the data set is provided to the computer which, in turn, discovers both the correct answer and how to figure out the correct answer.
One of these is neural networks – the algorithms that underpin deep learning and play a central part in image recognition and robotic vision. Inspired by the nerve cells (neurons) that make up the human brain, neural networks comprise layers (neurons) that are connected in adjacent layers to each other. So we need to compile a training set of images – thousands of examples of cat faces, which we (humans) label "cat", and pictures of objects that aren't cats, labelled (you guessed it) "not cat". In 2001, Paul Viola and Michael Jones from Mitsubishi Electric Research Laboratories, in the US, used a machine learning algorithm called adaptive boosting, or AdaBoost, to detect faces in an image in real time.
These are two basic approaches in CF: user-based collaborative filtering and item-based collaborative filtering, respectively. Imagine, we're building a big recommendation system where collaborative filtering and matrix decompositions should work longer. According to the study "Deep Neural Networks for YouTube Recommendations", the YouTube recommendation system algorithm consists of two neural networks: one for candidate generation and one for ranking. Taking events from a user's history as input, the candidate generation network significantly decreases the amount of videos and makes a group of the most relevant ones from a large corpus.
With the help of ultra-low latency, the system processes requests as fast as it receives them. He added that the system architecture reduces latency, since the CPU does not need to process incoming requests, and allows very high throughput, with the FPGA processing requests as fast as the network can stream them. Microsoft is also planning to bring the real-time AI system to users in Azure. "With the'Project Brainwave' system incorporated at scale and available to our customers, Microsoft Azure will have industry-leading capabilities for real-time AI," Burger noted.
In this post I will discuss a way to compress images using Neural Networks to achieve state of the art performance in image compression, at a considerably faster speed. This article assumes some familiarity with neural networks, including convolutions and loss functions. Again, the function may look complicated, but it is a mostly standard neural network loss function (MSE). The models are trained iteratively, similar to the way GANs are trained.
Big Data and Analytics are cool things that computers do and are used in building Enterprise AI systems of intelligent engagement. It requires a new class of technology, data, methods, and skills focused around a business led portfolio of industry optimized AIs and not just point solutions or science projects. Manoj Saxena is the Executive Chairman of CognitiveScale an industry AI software company focused on delivering systems of intelligent engagement. Portfolio companies include Spark Cognition, a cognitive security analytics company where Saxena is Chariman of the Board and WayBlazer, a B2B AI platform for the travel industry where Saxena is a Board member.
In the pre-cloud era, time, cost and computational constraints meant that large scale research was prohibitively difficult. Why did ANNs suddenly need to be deeper (more layers) and wider (more neurons in each layer)? There were also other mathematical hurdles to overcome but the real breakthrough was the rise of cloud computing drastically reducing the cost of large scale computation. So go with the experts – when Andrew Ng, one of the fathers of AI and Deep Learning research, isn't worried there isn't anything to worry about.
For example if we had a dataset containing past advertising budgets for various media (TV, Radio and Newspapers) as well as the resulting Sales figures we could train a model to use this information to predict expected Sales figures under various future advertising scenarios. Much of Machine Learning theory centres around data preparation, data sampling techniques, tuning algorithms as well as best practices for training processes to ensure best generalisation and statistical validity of results. The idea was to get computers to simulate this process to build a new kind of machine learning approach: Artificial Neural Networks. It would not be until the early 2000s that the birth of the cloud created a springboard that would catapult Artificial Neural Network research out of its winter and into the realm of Deep Learning.
After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years. Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Meanwhile, we're continuing to make foundational advances towards human-level artificial general intelligence (AGI), also known as strong AI. The definition of an AGI is an artificial intelligence that can successfully perform any intellectual task that a human being can, including learning, planning and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or… reprogramming itself.
PyTorch is essentially a GPU enabled drop-in replacement for NumPy equipped with higher-level functionality for building and training deep neural networks. In PyTorch the graph construction is dynamic, meaning the graph is built at run-time. TensorFlow does have the dynamic_rnn for the more common constructs but creating custom dynamic computations is more difficult. I haven't found the tools for data loading in TensorFlow (readers, queues, queue runners, etc.)