Written by Dr. Yassine Ghouzam, this notebook serves as a beginner tutorial on how to build a 5 layer CNN for digit recognition. Built with Keras API, this in-depth tutorial goes through each step from data preparation to model evaluation.

Artificial intelligence is finally taking off. Why now, and how are businesses using it? What are the challenges to implementation? In this white paper, we explore AI trends, the importance of choosing the right tools, and how to strategically deploy people in your tech-and-human stack.

Accurate segmentation is one of the cornerstones of an effective campaign. Personalizing messaging allows you to use the right content at the right time to grab and retain your audience's attention. AI-driven segmentation can increase campaign revenue up to 760%. See how other organizations, like yours, have found major success through segmentation and take the first steps in making this your success story.

Learn to build cybertrolls detection engine with CNN, keras, Glove and popular programming language Python.NEW by Evergreen Technologies What you'll learn Detect cybertroll in social messages using CNN, Glove embeddings and Keras Description Course Description Learn to build cybertrolls detection engine with CNN, keras, Glove and popular programming language Python. Understanding of cybertrolls classification Understand the world of world embeddings Learn CNN from scratch Leverage CNN, Keras, Glove to classify cybertrolls in social messages Learn how to represent text as numeric vectors using glove embeddings Learn how to evaluate model from scratch User Jupyter Notebook for programming Build a real life web application to classify social messages A Powerful Skill at Your Fingertips Learning the fundamentals of text classification puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation. No prior knowledge of deep learning or Machine learning is assumed. I a, covering topics like CNN, Word Embeddings Precision, Recall in depth so that even beginners can understand this course very well.

The global artificial intelligence and education Market is significantly driven by the integration of intelligent algorithms as well as Advanced Technologies in to e-learning platforms. Education software, machine learning, and artificial intelligence are some of the Innovative learning models and Technologies change the rules and creating tremendous shift from the teaching methods. These technologies have completely transformed with a classroom. The sophistication level has increased tremendously with the increasing adoption of artificial intelligence and machine learning algorithms. These Technologies are becoming extremely useful for developing user-friendly decision support systems and used in knowledge acquisition applications, language translation, and information retrieval.

This JavaScript tutorial explains how to build a powerful Virtual Machine in JavaScript. Support for signed, unsigned and floating point operations. Ability to do memory mapping for IO. We'll use and expand the library from the parser combinators from scratch series. In this episode we begin implementing a 16-bit virtual machine from scratch in JavaScript.

We study the problem of learning a series of tasks in a fully online Meta-Learning setting. The goal is to exploit similarities among the tasks to incrementally adapt an inner online algorithm in order to incur a low averaged cumulative error over the tasks. We focus on a family of inner algorithms based on a parametrized variant of online Mirror Descent. The inner algorithm is incrementally adapted by an online Mirror Descent meta-algorithm using the corresponding within-task minimum regularized empirical risk as the meta-loss. In order to keep the process fully online, we approximate the meta-subgradients by the online inner algorithm.

Continual learning, the setting where a learning agent is faced with a never-ending stream of data, continues to be a great challenge for modern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay.

A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous work often depend on task boundary and i.i.d. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning.

In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast and effective reinforcement learning (RL) strategies. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting these task distributions can be challenging and time-consuming. Can useful'' pre-training tasks be discovered in an unsupervised manner? We develop an unsupervised algorithm for inducing an adaptive meta-training task distribution, i.e. an automatic curriculum, by modeling unsupervised interaction in a visual environment. The task distribution is scaffolded by a parametric density model of the meta-learner's trajectory distribution.