Hey guys! this is my first blog on the History of Deep Learning it is very interesting please read it. It all started in the 1960s after the world war countries are spending there more money on research and US was the main contender for that, at that time in the US there was lots of research happening in the field of Computer Science and Space. There was this guy called Frank Rosenblatt this guy conceptualize this thing called Perceptron. He said that I have invented something that can learn and it is very close to our actual human neuron and this was a big thing back then because media play a big role they promote perceptron on a large scale and this is the starting of A.I. At that time Frank Rosenblatt made a statement about perceptron that caused a heated controversy Rosenblatt's statement, The New York Time reported the perceptron to be " the embryo of an electronic computer that [the navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence" this was huge there was a lot of research and projects happening on perceptron.

In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States. We utilize multiple supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, XGBoost, CatBoost, LightGBM, Chi-Squared Test and mutual information method on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to find out relationships between COVID-19 related negative effects and alcohol consumption habit changes among healthcare workers. Our findings suggest that COVID-19-related school closures, COVID-19-related work schedule changes and COVID-related news exposure may lead to an increase in alcohol use among healthcare workers in the United States.

Recovering the geometry of a human head from a single image, while factorizing the materials and illumination is a severely ill-posed problem that requires prior information to be solved. Methods based on 3D Morphable Models (3DMM), and their combination with differentiable renderers, have shown promising results. However, the expressiveness of 3DMMs is limited, and they typically yield over-smoothed and identity-agnostic 3D shapes limited to the face region. Highly accurate full head reconstructions have recently been obtained with neural fields that parameterize the geometry using multilayer perceptrons. The versatility of these representations has also proved effective for disentangling geometry, materials and lighting. However, these methods require several tens of input images. In this paper, we introduce SIRA, a method which, from a single image, reconstructs human head avatars with high fidelity geometry and factorized lights and surface materials. Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization. Experiments show that SIRA obtains state of the art results in 3D head reconstruction while at the same time it successfully disentangles the global illumination, and the diffuse and specular albedos. Furthermore, our reconstructions are amenable to physically-based appearance editing and head model relighting.

We present two approaches to determine the dynamical stability of a hierarchical triple-star system. The first is an improvement on the Mardling-Aarseth stability formula from 2001, where we introduce a dependence on inner orbital eccentricity and improve the dependence on mutual orbital inclination. The second involves a machine learning approach, where we use a multilayer perceptron (MLP) to classify triple-star systems as `stable' and `unstable'. To achieve this, we generate a large training data set of 10^6 hierarchical triples using the N-body code MSTAR. Both our approaches perform better than previous stability criteria, with the MLP model performing the best. The improved stability formula and the machine learning model have overall classification accuracies of 93 % and 95 % respectively. Our MLP model, which accurately predicts the stability of any hierarchical triple-star system within the parameter ranges studied with almost no computation required, is publicly available on Github in the form of an easy-to-use Python script.

AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines. The book is split into four main chapters -- excluding chapter 1 (introduction) and chapter 6 (conclusion): Chapter 2 introduces neural networks and covers all the basic building blocks that are used to build deep networks such as those used by AlphaZero. Contents include the perceptron, back-propagation and gradient descent, classification, regression, multilayer perceptron, vectorization techniques, convolutional networks, squeeze and excitation networks, fully connected networks, batch normalization and rectified linear units, residual layers, overfitting and underfitting. Chapter 3 introduces classical search techniques used for chess engines as well as those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte Carlo tree search. Chapter 4 shows how modern chess engines are designed. Aside from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela Chess Zero, Fat Fritz, Fat Fritz 2 and Efficiently Updatable Neural Networks (NNUE) as well as Maia. Chapter 5 is about implementing a miniaturized AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for that. Hexapawn is solved by minimax search and training positions for supervised learning are generated. Then as a comparison, an AlphaZero-like training loop is implemented where training is done via self-play combined with reinforcement learning. Finally, AlphaZero-like training and supervised training are compared.

Before we get into the gates topics, we need to understand the concept of Perceptron Algorithm. Here in this image x1,x2,……,xn are being the inputs that will be taken into function where weights and the inputs will be get multiplied and it will be taken into the activation function and the output from the activation function will be classified according to the thresh hold function. How we train the perceptron to get perfect output? One way to learn acceptable weight vector is to begin with random weights then apply iteratively to the each of the training example and modifies the perceptron weights if it misclassifies the output. We will try to understand this through the Mathematical knowledge lets consider the w1 1.2,w2 0.6,learning rate 0.5 and Threshold 1 Whose threshold it is not equal to one so its zero.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. An "earable" that uses sonar to read facial expressions was among the projects that caught our eyes over these past few weeks. So did ProcTHOR, a framework from the Allen Institute for AI (AI2) that procedurally generates environments that can be used to train real-world robots. Among the other highlights, Meta created an AI system that can predict a protein's structure given a single amino acid sequence.

Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contaminate the training set, misleading the HAR to produce erroneous outcomes. This paper presents the design of a label flipping data poisoning attack for a HAR system, where the label of a sensor reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.

We address the new problem of language-guided semantic style transfer of 3D indoor scenes. The input is a 3D indoor scene mesh and several phrases that describe the target scene. Firstly, 3D vertex coordinates are mapped to RGB residues by a multi-layer perceptron. Secondly, colored 3D meshes are differentiablly rendered into 2D images, via a viewpoint sampling strategy tailored for indoor scenes. Thirdly, rendered 2D images are compared to phrases, via pre-trained vision-language models. Lastly, errors are back-propagated to the multi-layer perceptron to update vertex colors corresponding to certain semantic categories. We did large-scale qualitative analyses and A/B user tests, with the public ScanNet and SceneNN datasets. We demonstrate: (1) visually pleasing results that are potentially useful for multimedia applications. (2) rendering 3D indoor scenes from viewpoints consistent with human priors is important. (3) incorporating semantics significantly improve style transfer quality. (4) an HSV regularization term leads to results that are more consistent with inputs and generally rated better. Codes and user study toolbox are available at https://github.com/AIR-DISCOVER/LASST

The philosophical movement known as computational mind theory or computationalism, which promotes the idea that neural computation accounts cognition, has ties to neural computation [1-4]. Nowadays, these types of algorithms are used in manufacturing and materials sectors for the determination of mechanical and microstructure properties of fabricated alloys or specimens [5-6]. An artificial neural network (ANN) was used by Shiau et al. [7] to model Taiwan's industrial energy demand in relation to subsector industrial output and climate change. It was the first investigation to measure the relationship between industrial energy use, manufacturing output, and climate change using the ANN technique. A multilayer perceptron (MLP) with a feedforward backpropagation neural network was used as the ANN model in this investigation. In order to improve the implementation of natural fibers in green bio-composites, Jarrah et al. [8] used doubly interconnected artificial neural networks to make unique classifications and prediction of the inherent mechanical properties of natural fibers.