Blockchain is a technology that everybody seems to think will revolutionize the global economy. If nothing else, the cryptocurrency boom has produced a flood of VC money that's trying to cash in on every potential application for this distributed hyperledger technology. It's no surprise that the artificial intelligence (AI) community is also trying to board the blockchain train. Clearly, none of these is a mature, built-out, widely adopted AI backbone, and many are highly speculative. To the extent that any of these initiatives gains a foothold in production AI environments, it will probably be to support enterprises' own heterogeneous modeling, training, and deployment pipelines for machine learning and deep learning.

This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you're a practicing machine-learning engineer, a software developer, or a college student, you'll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. You'll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems.

From the one that started it all "LeNet" (1998) to the deeper networks we see today like Xception (2017), here are some important CNN architectures you should know. If you like the video, show your support with a like, and SUBSCRIBE for more awesome content on Machine Learning, deep Learning, Data Science and AI MY EQUIPMENT (on a $350 budget) Camera (GoPro Hero 5 Black 32 GB Memory Kit): https://goo.gl/V4542j

Deep learning is the next big thing. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. This course will help you resolve problems during the execution of different tasks in deep learning, neural networks, and advanced machine learning techniques. We start with different packages in deep learning, neural networks, and structures.

TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you're a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing.

Get your hands on the latest and easiest TensorFlow Course on Udemy! Devices are getting smarter thanks to machine learning and artificial intelligence, and that is definitely going to continue. Machines are going to continue getting better and evolve, making tasks easier for humans. With machine learning and AI in the picture, the role of TensorFlow is unavoidable. TensorFlow is an open-source library that is commonly used for data flow programming.

Over the last several years, artificial intelligence (AI) has emerged as one of the most important trends in technology. The AI techniques of deep learning and machine learning have resulted in everything from improvements in search, to image recognition, to voice-controlled digital assistants, to self-driving cars. This article originally appeared in the Motley Fool. While the prospects created by this technology are enormous, estimates vary as to the size of the market. Deep learning, the most promising area of AI research, was forecast to generate $4.8 billion in 2017, growing to $261 billion by 2027, achieving a compound annual growth rate of 49% according to a report by Persistence Market Research.

Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously - e.g. microblogs or traditional media coverage. Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. We show RNN-MAS not only to outperform the current state-of-the-art Youtube popularity prediction system by 17%, but also to capture complex dynamics, such as seasonal trends of unseen influence. We define two new metrics: promotion score quantifies the gain in popularity from one unit of promotion for a Youtube video; the loudness level captures the effects of a particular user tweeting about the video. We use the loudness level to compare the effects of a video being promoted by a single highly-followed user (in the top 1% most followed users) against being promoted by a group of mid-followed users. We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos. This work provides more accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.

We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in $\beta$-VAE, as training progresses. From these insights, we propose a modification to the training regime of $\beta$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in $\beta$-VAE, without the previous trade-off in reconstruction accuracy.

Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving maneuvers. Therefore, a intention prediction method that can adapt to different traffic scenarios is needed. To further improve the overall vehicle prediction performance, motion information is usually incorporated with classified intentions. As suggested in some literature, the methods that directly predict possible goal locations can achieve better performance for long-term motion prediction than other approaches due to their automatic incorporation of environment constraints. Moreover, by obtaining the temporal information of the predicted destinations, the optimal trajectories for predicted vehicles as well as the desirable path for ego autonomous vehicle could be easily generated. In this paper, we propose a Semantic-based Intention and Motion Prediction (SIMP) method, which can be adapted to any driving scenarios by using semantic-defined vehicle behaviors. It utilizes a probabilistic framework based on deep neural network to estimate the intentions, final locations, and the corresponding time information for surrounding vehicles. An exemplar real-world scenario was used to implement and examine the proposed method.