We all in the hype of the AI world, but the reality is we are creating a world of Math and Science. It is as Simple as that. Yes, From Emotion to War field everywhere we apply math and check for feasibility for which we derive facts called Science. Now, in that prospect of understanding how this science is applied to Machines is the very raw side of Human life - a data life. For a better idea of getting it observed, here we have few books to take your life on Machine Learning.

The author selected Girls Who Code to receive a donation as part of the Write for DOnations program. Keras is a neural network API that is written in Python. It runs on top of TensorFlow, CNTK, or Theano. It is a high-level abstraction of these deep learning frameworks and therefore makes experimentation faster and easier. Keras is modular, which means implementation is seamless as developers can quickly extend models by adding modules. TensorFlow is an open-source software library for machine learning. It works efficiently with computation involving arrays; so it's a great choice for the model you'll build in this tutorial. Furthermore, TensorFlow allows for the execution of code on either CPU or GPU, which is a useful feature especially when you're working with a massive dataset. In this tutorial, you'll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations.

This blog post contains all my learnings from Google's Laurence Moroney's nice Coursera course named Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning. It's a great news that Google developers have released the alpha version of TensorFlow 2.0 (at the time of writing this post) which now focuses more on usability, clarity and flexibility just like Keras. What this means is that you can now use Keras inside TensorFlow itself in addition to all those advanced functions that TensorFlow offers. Furthermore, 2.0 has eager execution enabled by default which means you no longer need to create a session and run the computational graph inside that. Everything is dynamic just like PyTorch now.

All rights reserve. 5. DEEP LEARNING JOURNEY SO FAR 1980 Training them becomes difficult due to inadequate compute & dataThe power of neural networks is recognized GPUs make the required compute easily accessible for a broad range of problems Vast amounts of data flow through the internet Techniques for training "deep" networks open up new opportunities "Convolutional" networks – DCNNs, in particular – have changed the image analysis landscape 1990 2012 2015 2017 5Private and confidential. DEEP NEURAL NETWORK SHALLOW LAYERS Sensory features DEEP LAYERS Higher cognitive features Deep Neural Networks 10Private and confidential. FONTLI: 45,000 images used to train the network 14Private and confidential. Which whale is it, anyway? Face recognition to identify whales using deep learning A non-random sample from the dataset 4544 training images only & 447 categories 15Private and confidential.

Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity. A major step in this direction is under way at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University, where a team of scientists working with a Harvard graduate student is for the first time applying deep learning -- a powerful new version of the machine learning form of AI -- to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions. "This research opens a promising new chapter in the effort to bring unlimited energy to Earth," Steve Cowley, director of PPPL, said of the findings, which are reported in the current issue of Nature magazine. "Artificial intelligence is exploding across the sciences and now it's beginning to contribute to the worldwide quest for fusion power." Fusion, which drives the sun and stars, is the fusing of light elements in the form of plasma -- the hot, charged state of matter composed of free electrons and atomic nuclei -- that generates energy.

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I'd like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data. Recall that machine learning is a class of methods for automatically creating models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you're solving, the computing resources available, and the nature of the data.

How to build your own AlphaZero AI using Python and Keras Teach a machine to learn Connect4 strategy through self-play and deep learning In this article I'll attempt to cover three things: Two reasons why AlphaZero is a massive step forward for Artificial Intelligence How you can build a replica of the AlphaZero methodology to play the game Connect4 How you can adapt the code to plug in other games First, a quick note about a new platform, The Network -- a place where data scientists can find paid contract projects with businesses! AlphaGo AlphaGo Zero AlphaZero In March 2016, Deepmind's AlphaGo beat 18 times world champion Go player Lee Sedol 4–1 in a series watched by over 200 million people. A machine had learnt a super-human strategy for playing Go, a feat previously thought impossible, or at the very least, at least a decade away from being accomplished. Match 3 of AlphaGo vs Lee Sedol This in itself, was a remarkable achievement. However, on 18th October 2017, DeepMind took a giant leap further.

Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. To accomplish this, we combine the scenario-specific learning with a model-agnostic sequential meta-learning and unify them into an integrated end-to-end framework, namely Scenario-specific Sequential Meta learner (or s^2 meta). By doing so, our meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks while effectively adapting to specific tasks by leveraging learning-to-learn knowledge. Extensive experiments on various real-world datasets demonstrate that our proposed model can achieve significant gains over the state-of-the-arts for cold-start problems in online recommendation. Deployment is at the Guess You Like session, the front page of the Mobile Taobao.

There is a high demand for high-quality Non-Player Characters (NPCs) in video games. Hand-crafting their behavior is a labor intensive and error prone engineering process with limited controls exposed to the game designers. We propose to create such NPC behaviors interactively by training an agent in the target environment using imitation learning with a human in the loop. While traditional behavior cloning may fall short of achieving the desired performance, we show that interactivity can substantially improve it with a modest amount of human efforts. The model we train is a multi-resolution ensemble of Markov models, which can be used as is or can be further "compressed" into a more compact model for inference on consumer devices. We illustrate our approach on an example in OpenAI Gym, where a human can help to quickly train an agent with only a handful of interactive demonstrations. We also outline our experiments with NPC training for a first-person shooter game currently in development.

A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation, thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we show that the proposed framework is more effective, efficient, and flexible, compared to other state-of-the-art time series clustering methods.