This year, the controversy about AI industrialization has become a hot topic. There are not only negative phenomena such as criticism of AI "research results are hard to break through in academia, and also difficult to commercialize in industry" from academia, AI scientists leaving the industry and returning to academia, but also positive encouragement from the successful listing of a number of AI unicorns.So, is there an opportunity for AI industrialization? And where are the opportunities? On these industry hot topics, Yuan Jinhui, the founder of OneFlow, launched a systematic elaboration in the QbitAI live. In previous years, society was crazy about AI. For example, there were discussions about the coming singularity, AI replacing humans, and fully automated driving by 2020.

Restricted Boltzmann Machine is used to detect patterns in data, in an unsupervised way. If you haven't read the previous posts yet, you can read them by clicking the below links. RBMs are self-learning shallow neural networks that learn to reassemble data. They're significant models because they can extract meaningful features from a given input without having to identify them. Let's start with the fact that we have access to a matrix of viewer ratings for a specific number of Netflix movies, where each row represents a movie and each column represents a user's rating.

Deep Belief Networks (DBN) and Autoencoders, Let's take a look at DBNs and how they are created on top of RBMs. If you haven't read the previous posts yet, you can read them by clicking the below links. A DBN is a network that was created to overcome a problem that existed in standard artificial neural networks. Backpropagation is a phenomenon that might result in "local minima" or "vanishing gradients." DBN is designed to solve this problem by stacking numerous RBMs.

Once in a while every data scientist needs some inspiration. Or maybe you just want to learn new things or to see what's going on in the awesome field called Machine Learning. On Github there's a lot of brilliant and well crafted ML repos. Here's just a fraction of what Github has to offer. I hope you will enjoy it and let's get started!

Surprisingly, we're in an era where Tech is changing the narrative. An era where nearly all manual tasks are being automated. Machine Learning algorithms now help computers drive cars, perform surgeries, and even simulate human intelligence. Now is a time of constant technological progress, and looking at how computing has advanced over the years, one can predict what's to come in the days ahead. One of the main features of this revolution that stands out is how computing tools and techniques have been democratized.

Artificial intelligence (AI), machine learning (ML), and deep neural networks (DNNs) are the talk of the town these days. However, few people understand the difference between these innovative technologies. Artificial intelligence is an overarching concept that comprises several fields of computer science. It is geared toward solving tasks intrinsic to the human mind, such as speech recognition and object classification. Machine learning is part of the artificial intelligence ecosystem.

Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model’s reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems. Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics and kinetics arising in chemical or biological physics.

At the #universityofoxford I focus a lot on the mathematics aspect of AI   I recommend eight books for the mathematics of AI     The Nature Of Statistical Le…

Presently, nearly all manual tasks are being automated. Machine learning algorithms are changing the definition of manual. It is very evident that machine learning is one of the hottest trends in the tech industry and is incredibly powerful to make predictions, and calculated suggestions based on large amounts of data. Machine learning engineers should be thorough with the routine algorithms to understand ML operations and execute advanced techniques. Here are the top 10 machine learning algorithms every engineer should know.

Created by Lazy Programmer Inc. Understand and enumerate the various applications of Markov Models and Hidden Markov Models Understand how Markov Models work Write a Markov Model in code Apply Markov Models to any sequence of data Understand the mathematics behind Markov chains Apply Markov models to language Apply Markov models to website analytics Understand how Google's PageRank works Understand Hidden Markov Models Write a Hidden Markov Model in Code Write a Hidden Markov Model using Theano Understand how gradient descent, which is normally used in deep learning, can be used for HMMs Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!