This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on.We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.It focuses on the various tree-based machine learning models used by industry practitioners.We will also discuss k-nearest neighbors, Naive Bayes, Support Vector Machine and recommendation engine.By the end of the course, you will have mastered the required statistics for Machine Learning Algorithm and will be able to apply your new skills to any sort of industry problem. Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore.

Machine learning and deep learning, in particular, are developing at amazing speeds. Today, machine learning can be used to solve ever more complex tasks that have been considered impractical just a few years ago. Examples include autonomous cars, AlphaGo's win against the world's Go champion, the photo-realistic transformation of pictures, and neural machine translation systems. In this blog post, we describe a simple system to recognize monetary amounts on Swiss payment slips. The user interface is implemented using Eclipse Scout and we build, train, and run the deep neural net using Deeplearning4j.

Montreal-based Element AI has compiled a report and analysis on the global supply of AI researchers in an effort to get a better understanding of an industry in high demand. Overall, the report found that there are 22,064 PhD-educated researchers globally that are capable of working in AI research and applications, with only 3,074 candidates currently looking for work. The US had the highest concentration of researchers with 9,010 researchers, followed by the UK with 1,861 researchers. Canada fell in third place with 1,154 researchers. To conduct the broader survey, Element AI used results from LinkedIn searches that showed the total number of profiles according to specialized parameters.

TensorFlow is one of the most commonly used frameworks for Deep Learning and AI. This course will be your guide to understand and learn the concepts of Artificial intelligence by applying them in a real-world project with TensorFlow. This course will show you how to combine the power of Artificial Intelligence and TensorFlow to develop some exciting applications for the real world. This course will take you through all the relevant AI domains, tools, and algorithms required to build optimal solutions and will show you how to implement them hands-on. You will then be taken through techniques such as reinforcement learning, heuristic searches, neural networks, Computer Vision, OpenAI Gym, and more in different stages of your application.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.

What do you think of when you hear about AI? Do you picture your favorite sci-fi movie or a book that you read when you were younger? In that favorite book or movie, were the robots smart? In AI, we can find a subset of machine learning called "deep learning," which is defined as networks that can learn unsupervised from unstructured data. Now the bigger question is: Are you ready to take advantage of deep learning in your business? The vast ocean of data grows exponentially every day.

Text embeddings allow deep learning to be effective on smaller datasets. These are often first inputs to a deep learning archiectures and most popular way of transfer learning in NLP. Embeddings are simply vectors or a more generically, real valued representations of strings. Word embeddings are considered a great starting point for most deep NLP tasks. The most popular names in word embeddings are word2vec by Google (Mikolov) and GloVe by Stanford (Pennington, Socher and Manning).

In coming tutorials on this blog I will be dealing with how to create deep learning models that predict text sequences. However, before we get to that point we have to understand some key Natural Language Processing (NLP) ideas. One of the key ideas in NLP is how we can efficiently convert words into numeric vectors which can then be "fed into" various machine learning models to perform predictions. The current key technique to do this is called "Word2Vec" and this is what will be covered in this tutorial. After discussing the relevant background material, we will be implementing Word2Vec embedding using TensorFlow (which makes our lives a lot easier). To get up to speed in TensorFlow, check out my TensorFlow tutorial. Also, if you prefer Keras – check out my Word2Vec Keras tutorial.

In the last few years, deep neural networks have dominated pattern recognition. They blew the previous state of the art out of the water for many computer vision tasks. Voice recognition is also moving that way. But despite the results, we have to wonder… why do they work so well? In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it's a very elegant perspective. A neural network with a hidden layer has universality: given enough hidden units, it can approximate any function. This is a frequently quoted – and even more frequently, misunderstood and applied – theorem.