Inductive learning, or induction, is the process of creating generalizations from individual instances.
Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains. This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks. The course will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. We will also build a machine learning system that can make a medical diagnosis. By the end of this course, you will be ready create your own ML system and will also be able to take on your own machine learning problems.
One of the first lessons you'll receive in machine learning is that there are two broad categories: supervised and unsupervised learning. Supervised learning is usually explained as the one to which you provide the correct answers, training data, and the machine learns the patterns to apply to new data. Unsupervised learning is (apparently) where the machine figures out the correct answer on its own. Supposedly, unsupervised learning can discover something new that has not been found in the data before. Supervised learning cannot do that.
In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with 'weakly labeled' lidar data, using 'unsupervised' pre-training with the cloud locations of the Wang & Sassen (2001) cloud mask algorithm, and fully supervised learning with hand-labeled cloud locations. We show the model achieves higher levels of cloud identification compared to the cloud mask algorithm.
We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model parameters at first. Despite its simplicity, SaaS achieves state-of-the-art results in semi-supervised learning benchmarks.
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original formulation of graph-based SSL and prove the supermodularity of an AG-SSL objective function under a broad class of regularization functions parameterized by Stieltjes matrices. Under this setting, supermodularity yields a novel greedy label sampling algorithm with guaranteed performance relative to the optimal sampling set. Compared to three state-of-the-art graph signal sampling and recovery methods on two real-life community detection datasets, the proposed AG-SSL method attains superior classification accuracy given limited sample budgets.
The core of the TensorLayer working follows a modular approach (as shown in the image). The key functions covered with regard to DL are, building neural networks, layer implementations, gathering & creating datasets and, finally structuring a training plan for proper working of the library in light of any failures while performing learning tasks. The highlighting feature of TensorLayer lies in the integrated development environment(IDE)-like approach where the host of operations such as neural networks, their states, data and other parameters are assorted into modules with an abstraction level. This allows other developers to customise with respect to the specific inter-connected areas such as front-end applications or back-end servers by providing an interactive modular interface.
This is our first post in this sub series "Machine Learning Type" under master series "Machine Learning Explained". We will only talk about supervised machine learning in details here. Machine learning algorithms "learns" from the observations. When exposed to more observations, the algorithm improves its predictive performance. Supervised Learning is becoming a good friend for marketing business in particular.
Analysis IBM boasts that machine learning is not just quicker on its POWER servers than on TensorFlow in the Google Cloud, it's 46 times quicker. Back in February Google software engineer Andreas Sterbenz wrote about using Google Cloud Machine Learning and TensorFlow on click prediction for large-scale advertising and recommendation scenarios. He trained a model to predict display ad clicks on Criteo Labs clicks logs, which are over 1TB in size and contain feature values and click feedback from millions of display ads. Data pre-processing (60 minutes) was followed by the actual learning, using 60 worker machines and 29 parameter machines for training. The model took 70 minutes to train, with an evaluation loss of 0.1293.
I don't use TF for its speed, I use it because it's practical. I can use the excellent Keras API to painlessly build my applications, and I know I can run them on my CPU, my GPU or the cloud with very minor changes. For a competitor to be better, being faster isn't enough, it has to be equally good in all the other important ways as well.