Google Cloud Platform provides us with a wealth of resources to support data science, deep learning, and AI projects. Now all we need to care about is how to design and train models, and the platform manages the rest tasks. In current pandemic environment, the entire process of an AI project from design, coding to deployment, can be done remotely on the Cloud Platform. IMPORTANT: If you get the following notification when you create a VM that contains GPUs. You need to increase your GPU quota.

The term artificial intelligence was initially revealed in 1956, yet AI has become more mainstream today on account of expanded data volumes, progressed algorithms, and enhancements in computing power and storage. During the 1960s, the US Department of Defense checked out this kind of work and started training computers to emulate fundamental human reasoning. For instance, the Defense Advanced Research Projects Agency (DARPA) finished road planning projects during the 1970s. What's more, DARPA created intelligent personal assistants in 2003, some time before Siri, Alexa or Cortana were easily recognized names. Artificial intelligence (AI), is the capacity of a digital computer or computer-controlled robot to perform activities usually connected with smart creatures.

The last two layers are fully-connected and a dropout is applied on each of them. Note that the dropout probability for the layers is passed into the network's constructor. This enables us to flexibly pass different values for this hyperparameter, as we will see in a bit. The final layer contains a logarithmic softmax function which gives us the probability for each of the 10 digit classes from MNIST. The digit with the highest probability is the one our network thinks is most likely visible on the input picture.

With Moneybrain's solution, MBN is able to report videos of breaking news vividly and quickly with an AI anchor. In addition, AI models will be put into MBN's various programs, allowing the company to start producing broadcasts in the same time slot. The Moneybrain solution introduced by MBN is a real-time video synthesis technology based on deep learning and provides AI model videos that express the same person as the actual person. By simply entering an article script, it converts into voice and video and it provides various costume choices, making it easier for users to produce AI models. As a result, existing broadcasting officials have been able to save a lot of resources such as time, personnel, and cost for filming.

Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they're correct? Alexander Amini and his colleagues at MIT and Harvard University wanted to find out. They've developed a quick way for a neural network to crunch data, and output not just a prediction but also the model's confidence level based on the quality of the available data.

Under the umbrella of data science fields, natural language processing (NLP) is one of the most famous and important subfields. Natural language processing is a computer science field that gives computers the ability to understand human -- natural -- languages. Although the field has gained a lot of traction recently, it is -- in fact -- a field as old as computers themselves. However, the advancement of technology and computing power has led to incredible advancements in NLP. Now, speech technologies are becoming as famous as written text technologies.

Image capture makes a snapshot in time of a person, place, or object. Many devices include cameras for taking pictures. This is integrated into everyday life. When taking the picture, there is recognition of that picture and often an autocorrection. Taking that further, there is Optical Character Recognition (OCR) that can take a picture of text and create a usable file that is same as document.

The foundation of machine learning and deep learning systems wholly base upon mathematics principles and concepts. It is imperative to understand the fundamental foundations of mathematical principles. During the baseline and building of the model, many mathematical concepts like the curse of dimensionality, regularization, binary, multi-class, ordinal regression, and others must be artistic in mind. The basic unit of deep learning, commonly called a neuron, is wholly based on its mathematical concept, and such involves the sum of the multiplied values involving input and weight. Its activation functions like Sigmoid, ReLU, and others, have been built using mathematical theorems. Linear algebra plays a requisite role in machine learning due to vectors' availability and several rules to handle vectors.

Certain neural network architectures, in the infinite-layer limit, lead to systems of nonlinear differential equations. Motivated by this idea, we develop a framework for analyzing time signals based on non-autonomous dynamical equations. We view the time signal as a forcing function for a dynamical system that governs a time-evolving hidden variable. As in equation discovery, the dynamical system is represented using a dictionary of functions and the coefficients are learned from data. This framework is applied to the time signal classification problem. We show how gradients can be efficiently computed using the adjoint method, and we apply methods from dynamical systems to establish stability of the classifier. Through a variety of experiments, on both synthetic and real datasets, we show that the proposed method uses orders of magnitude fewer parameters than competing methods, while achieving comparable accuracy. We created the synthetic datasets using dynamical systems of increasing complexity; though the ground truth vector fields are often polynomials, we find consistently that a Fourier dictionary yields the best results. We also demonstrate how the proposed method yields graphical interpretability in the form of phase portraits.

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.