If you read through the human history, especially the last 2,000 years, you will notice that famines, diseases and wars were the three most formidable challenges that we had to face (or as I call it -- the trifecta of human sufferings). Our response to the trifecta was mostly to seek safety and pray for our survival. History also tells us that when faced with external threats and no possible solutions in sight, we turned inwards and sought solace in numbers to overcome some serious challenges to our species' survival. Gradually, these cycles of sufferings led to emergence and prevalence of the unifying behavior of collaboration amongst humans. Tremendous efforts and resources, collaborated at extraordinary scale, were thrown at solving the trifecta of problems with some amount of success.

While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today. For prediction or decision models to be trained properly, they need data. As many people have put it, data is now one of the most sought-after commodities ousting oil.

Since OpenAI released GPT-3, you have probably come across examples of impressive and/or problematic content that people have used the model to generate. Here we summarise the outputs of GPT-3 as seen through the eyes of the Twitter-sphere. We're releasing an API for accessing new AI models developed by OpenAI. See how companies are using the API today, or join our waitlist: https://t.co/SvTgaFuTzN First, let me summarize the API documentation.

Summary: Transfer Learning (TL) may be the most important aid to adoption of deep learning in the last several years. This new LEEP measure predicts the accuracy of the transfer and should make TL faster, cheaper, and better. What is the single most important innovation in deep learning in the last several years? There might be several candidates. You might argue for tensorflow specific chips, or BERT architecture for improving NLP.

Reinforcement learning (RL) is a hugely popular area of deep learning, and many data scientists are exploring this AI technology to broaden their skillet to include a number of important problem domains like chatbots, robotics, discrete optimization, web automation and much more. As a result of this wide-spread interest in RL, there are many available educational resources specifically tailored to this class of deep learning – boot camps, training certificates, educational specializations, etc. But if you're a data scientist who has been programming in Python (with object oriented features) for a while, and has some experience with other forms of deep learning using a framework like TensorFlow, then maybe this new book, "Deep Reinforcement Learning Hands-On," by Maxim Lapan from Packt, might be a great way to kick-start yourself into becoming productive with RL. RL development is being driven by a number of large companies and research groups, including Google, Microsoft, and Facebook. RL requires considerable investment in research as the field is growing to enable data scientists to be able to take prescribed methods and apply them to a problem domain.

ImageNet (Deng et al. 2009) is an image database organized according to the WordNet (Miller 1995) hierarchy which, historically, has been used in computer vision benchmarks and research. However, it was not until AlexNet (Krizhevsky, Sutskever, and Hinton 2012) demonstrated the efficiency of deep learning using convolutional neural networks on GPUs that the computer-vision discipline turned to deep learning to achieve state-of-the-art models that revolutionized their field. Given the importance of ImageNet and AlexNet, this post introduces tools and techniques to consider when training ImageNet and other large-scale datasets with R. Now, in order to process ImageNet, we will first have to divide and conquer, partitioning the dataset into several manageable subsets. Afterwards, we will train ImageNet using AlexNet across multiple GPUs and compute instances. Preprocessing ImageNet and distributed training are the two topics that this post will present and discuss, starting with preprocessing ImageNet. When dealing with large datasets, even simple tasks like downloading or reading a dataset can be much harder than what you would expect.

Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks--such as Google's TensorFlow--that ease the process of acquiring data, training models, serving predictions, and refining future results. Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C .

For a very long time, Artificial Intelligence was a part of fictional stories, Science Fiction books, and even movies. It was tech-magic to the eyes. Today, as things are slowly moving towards reality, we can see that AI is still exciting, even though it's not as advanced as shown in the movies. Businesses are slowly investing in AI technology so that they can grow to be smarter and more efficient. Investments in AI as a technology haven't met the hype, but there is a positive sign of acceptance towards the smart technology in IoT.

Our editors have compiled this directory of the best Python books based on Amazon user reviews, rating, and ability to add business value. There are loads of free resources available online (such as Solutions Review's Data Analytics Software Buyer's Guide, visual comparison matrix, and best practices section) and those are great, but sometimes it's best to do things the old fashioned way. There are few resources that can match the in-depth, comprehensive detail of one of the best Power BI books. The editors at Solutions Review have done much of the work for you, curating this comprehensive directory of the best Python books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in the first section of this compilation have met a minimum criteria of 15 reviews and a 4-star-or-better ranking. Below you will find a library of titles from recognized industry analysts, experienced practitioners, and subject matter experts spanning the depths of Python coding for beginners all the way to advanced data science best practices for Python users. This compilation includes publications for practitioners of all skill levels. "Python Crash Course is the world's best-selling guide to the Python programming language. In the first half of the book, you'll learn basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. You'll also learn how to make your programs interactive and test your code safely before adding it to a project. In the second half, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, a set of data visualizations with Python's handy libraries, and a simple web app you can deploy online."

Deep neural networks have gained increased attention in machine learning, but they are limited by the fact that many such regression and classification models do not capture prediction uncertainty. Though this might be acceptable for certain non-critical applications, it is not so for oil and gas industry applications where business and economic consequences of wrong or even sub-optimal decision is quite high. In this work I discuss the application of deep neural networks as a framework for approximate Bayesian inference in oilfield reservoir simulation study. Surrogate models with different neural network architecture are proposed to speed up compute- and labor-intensive simulation workflow. Regularization tools such as dropout and batch normalization, variational autoencoder for regression, and probabilistic distribution layers are used to quantify prediction uncertainty. Monte-Carlo dropout approach is further applied to estimate uncertainty given by standard deviation values for the predictions. Probabilistic distribution layers are used to compare its efficacy in capturing the model prediction uncertainty with respect to deterministic neural layers. Deep ensemble approach is also used to train multiple surrogates which capture uncertainty. Among different models tested, VAE based regression model with multivariate-normal latent features works best for prediction uncertainty assessment. Compute time required by surrogate model for prediction is a small fraction of that for full-physics reservoir simulator. Prediction uncertainty information can be used in various simulation workflows to decide when to use surrogate model and when to further explore the solution space using reservoir simulator, thus reducing total computational cost.