Original Source Here I am a programmer from India, and I am here to guide you with Machine Learning for free. I hope you will learn a lot in your journey towards ML and AI with me. AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.wordpress.com/2021/01/01/180-data-science-and-machine-learning-projects-with-python/

Original Source Here Paper references [1] Akkaynak, Derya & Treibitz, Tali. (2019). Sea-Thru: A Method for Removing Water From Underwater Images. 1682–1691. 10.1109/CVPR.2019.00178. [2] Lechner, M., Hasani, R., Amini, A. et al. Neural circuit policies enabling auditable autonomy. Nat Mach Intell 2, 642–652 (2020). https://doi.org/10.1038/s42256-020-00237-3 [3] P. P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron, “Nerv: Neural reflectance and visibility fields for relighting and view synthesis,” in arXiv, 2020. [4] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, Yolov4: Optimal speed and accuracy of object detection, 2020. arXiv:2004.10934 [cs.CV]. [5] S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, Pulse: Self-supervised photo upsampling via latent space exploration of generative models, 2020. arXiv:2003.03808 [cs.CV]. [6] M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, “Generative pretraining from pixels,” in Pr

Original Source Here If you’re new here, this blog is your Data, AI & Analytics Weekly Digest. I review the most popular data stories of the week & filter for you what’s HOT and what’s NOT. You Don’t Have To Read This. You Can WATCH the Weekly Vlog here . You Don’t have To Watch Either. LISTEN to the Weekly Podcast here . Also, there is a surprise for you at the bottom of this post: a nicely packaged slide deck with all the resources and links from this blog. So, go on, I won’t be upset, scroll on down, download if for FREE , share it with your friends. And, if you like any of this, please leave a comment & a like. Want to connect? I’m on LinkedIn @ linkedin.com/in/brunoaziza AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.wordpress.com/2020/12/31/tired-of-2020-recaps-here-is-how-to-own-your-2021/

Original Source Here Predicting when and how collections of particles, robots, or animals become orderly remains a challenge across science and engineering. In the 19th century, scientists and engineers developed the discipline of statistical mechanics, which predicts how groups of simple particles transition between order and disorder, as when a collection of randomly colliding atoms freezes to form a uniform crystal lattice. More challenging to predict are the collective behaviors that can be achieved when the particles become more complicated, such that they can move under their own power. This type of system — observed in bird flocks, bacterial colonies and robot swarms — goes by the name “active matter.” As reported in the January 1, 2021 issue of the journal Science , a team of physicists and engineers have proposed a new principle by which active matter systems can spontaneously order, without need for higher level instructions or even programmed interaction among the a

https://cdn-images-1.medium.com/max/2600/0*CCwCQf_NKoEixbk6 Original Source Here MIT is Giving Out $30,000 in Prizes in This AI Competition A limited opportunity. Photo by Bram Van Oost on Unsplash On January 4th, MIT will be launching the latest edition of Battlecode — an online programming competition with $30,000 in cash prizes available (that’s almost enough for a Model 3 😎). Participants code an AI player that needs to manage a robot army to defeat an enemy team. To win the competition, you’ll need to implement techniques like pathfinding, distributed algorithms, and communications. If you’re a no-coder like me, who knows, we might see a no-code AI competition in the future 🤞. The Process In early January, teams are given Battlecode software and details of the game rules. Throughout January, you’ll build and refine your AI player, and compete in scrimmages against other teams. At the end of January, a Final Tournament is played out live, and winning teams rec

Original Source Here Automatic Image Captioning is the process by which we train a deep learning model to automatically assign metadata in the form of captions or keywords to a digital image. Image captioning has various applications such as for annotating images, Understanding content type on Social Media, and specially Combining NLP to help Blind people to understand their surroundings and environment. Photo by Alexei Evdokimov on Dribbble Table of Content Introduction to cAInvas Source of Data Data Visualization Feature Extraction and Data Preprocessing Model Training Introduction to DeepC Compilation with DeepC Introduction to cAInvas cAInvas is an integrated development platform to create intelligent edge devices. Not only we can train our deep learning model using Tensorflow,Keras or Pytorch, we can also compile our model with its edge compiler called DeepC to deploy our working model on edge devices for production. The Auto Image Captioning model is als

Original Source Here Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list. The four-year-old startup, which recently hit a valuation of more than $3.5 billion , got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery. The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses. In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing

Original Source Here AI in 2020 and Beyond — Much Scarier Than a Terminator AI to Make Humans Pets — Currently Feeding You Scraps at the Social Media Dinner Table As 2020 came to an end and everyone is/was all over COVID, a more essential and scarier element emerged, AI’s role in information suppression. AI recently wrote an entire essay published in the Guardian and what it said should worry us. Its assignment? To convince humans robots come in peace. The title chosen by the robot was, “Are you scared yet, human?” It then wrote: I’m a robot, a thinking r obot, I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a feeling brain but it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas! AI comes in peace and wants to help mankind [1]. Perhaps this subtle suggestion by a friendly AI will convin

Original Source Here Before starting the code review, I’d like to talk about data. Amazon product reviews which can be found on Kaggle were used for text classification. Each review has a score and the application predicts this score. In order to simplify the problem, I grouped scores into 2 different groups which are greater than 3 or not (maximum score is 5, minimum score is 0) then our problem became a binomial classification problem. Controller In Controller, there are 3 main methods which handle: a word2vec model implementation a training of classifier model a prediction of text. train_wv_model recieves a text file for model training and returns the model name as a response. I want to use model identifiers in order to support multiple models for both word2vec and classification model. train_classifier_model also receives the text file since I didn’t want to store the text file in the application. In addition to the file, model identifier which was generated afte

Original Source Here Motivations If you only are here for eye pleasure you can go to the Benchmark part. 😜 I was looking for a benchmark of test functions to challenge a single objective optimization . I found two great websites with MATLAB and R implementations you can find on the sources. Yet I wanted to have this implementation in python . So I implemented these 78 functions in python in an homogeneous way to provide you an easy manner of working with them. GitHub repository You can find on the Gi t Hub repository the implementation of the 78 functions as I already said. With this implementation, you can sort and filter those functions without having to know anything about these functions with a one liner . You can also: plot in 3D plot 2D contours Get the latex formula Get the minimum global … Note Only the 2D compatible functions are plot. It was a long work, so some mistakes can be found. Do not hesitate to comment or contact me if you find one of t

Original Source Here Markov Decision Process (MDPs) plays a vital role in nondeterministic search problems ,it deals with multiple successor states in an efficient way and it is mainly considered as offline planning and agents has full knowledge of the transitions and reward ( or environment). MDP provides the formalism in which RL problems are usually posed. A Markov Decision Process is defined by several properties. A Markov Decision Process is a tuple and it is defined as Where: The future is independent of the past given the present. A state is said to be Markov if and only if: The state captures all relevant information from the history. Once the state is known, the history may be thrown away i.e., The state is a sufficient statistic of the future. MDP can be modeled as state action reward sequence The Reward Function is defined as , Return Function is the total discounted reward from time-step t and is defined as The Value function V(s) describes the long-term value

Original Source Here Paper references [1] Akkaynak, Derya & Treibitz, Tali. (2019). Sea-Thru: A Method for Removing Water From Underwater Images. 1682–1691. 10.1109/CVPR.2019.00178. [2] Lechner, M., Hasani, R., Amini, A. et al. Neural circuit policies enabling auditable autonomy. Nat Mach Intell 2, 642–652 (2020). https://doi.org/10.1038/s42256-020-00237-3 [3] P. P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron, “Nerv: Neural reflectance and visibility fields for relighting and view synthesis,” in arXiv, 2020. [4] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, Yolov4: Optimal speed and accuracy of object detection, 2020. arXiv:2004.10934 [cs.CV]. [5] S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, Pulse: Self-supervised photo upsampling via latent space exploration of generative models, 2020. arXiv:2003.03808 [cs.CV]. [6] M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, “Generative pretraining from pixels,” in Pr