This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! Learn to use TensorFlow 2.0 for Deep Learning Leverage the Keras API to quickly build models that run on Tensorflow 2 Perform Image Classification with Convolutional Neural Networks Use Deep Learning for medical imaging Forecast Time Series data with Recurrent Neural Networks Use Generative Adversarial Networks (GANs) to generate images Use deep learning for style transfer Generate text with RNNs and Natural Language Processing Serve Tensorflow Models through an API Use GPUs for accelerated deep learning This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning!

"(AI) will rank among our greatest technological achievements, and everyone deserves to play a role in shaping it." Encompassing all facets of AI, the General Secretariat of MONTREAL.AI introduces, with authority and insider knowledge: "Artificial Intelligence 101: The First World-Class Overview of AI for the General Public". AI opens up a world of new possibilities. This AI 101 tutorial harnesses the fundamentals of artificial intelligence for the purpose of providing participants with powerful AI tools to learn, deploy and scale AI. Theoretical Physics in 1 (one) year, followed by a Master's degree in Government Policy Analysis (1998) and a Master's degree in Aerospace Engineering (Space Technology) (2000).

From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

EduAstrum is thrilled to announce the second edition of our very popular scholarship program – Jumpstart. Last year the program was offered under the banner of ExcelR. This year, our name has changed, and our offering has been enhanced with up to 90% fee discounts! Our specially tailored courses have been created to offer 360 degrees of opportunities to students and working professionals. As part of this program we provide 5 innovative trainings that can be found nowhere else in Kerala.

What is a data scientist? If you ask the Harvard Business Review, it's the "sexiest job of the 21st century." If you ask a technologist interested in crunching data, they'll tell you it's a potentially lucrative, intellectually fulfilling career. And if you ask a CEO, they'll probably say that data scientists mean the difference between strategic success and failure. But how do you actually become one? At the most basic level, data scientists analyze massive datasets for insights that can change how companies operate and strategize. As terms, "data scientist" and "data science" are relatively new, first appearing a little over a decade ago (roughly around the time that "Big Data" emerged into the mainstream as a buzzword, which isn't a coincidence).

Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning Understand the business scenarios where Artificial Neural Networks (ANN) is applicable Building a Artificial Neural Networks (ANN) in R Use Artificial Neural Networks (ANN) to make predictions Use R programming language to manipulate data and make statistical computations Learn usage of Keras and Tensorflow libraries You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?You've found the right Neural Networks course!

Bengaluru: Akshaya Patra provides mid-day meals to 1.8 million school children across India. The NGO came to Accenture a couple of years ago with a simple query: how do we feed more children? The consultant looked at the supply chain and then worked with three startups from different domains for a solution. One startup used data from IoT sensors to streamline cooking processes and monitor the quality of food. Another one used machine learning and artificial intelligence (AI) to predict the demand for food. And a third startup used blockchain to put feedback from schools on a distributed ledger in a tamper-proof manner.

Know the importance of voice-cloning technology that is far superior compared to TTS (Text to Speech) conversion Clone your voice using Artificial Intelligence Tools like Lyrebird, iSpeech etc. Know about a special cloud tool that can create fictitious voices for audiobooks Know about a special cloud tool that can clone your voice in minutes by uploading bulk fragments of audio to it This course "Digital Voice Cloning using Artificial Intelligence Tools" created by Digital Marketing Legend "Srinidhi Ranganathan" primarily deals with explaining about a Montreal-based AI startup named "Lyrebird" which provides an online platform that, when trained on 30 or more recordings, can imitate a person's mimic speech. Lyrebird is an AI research division within Descript, currently and the team is building a new generation of tools for media editing and synthesis that make content creation more accessible and expressive. Sounding to be a wow factor, this new neural voice cloning technology from Lyrebird (that is discussed in the course) synthesises the voice of a human from audio samples fed to it. Know the importance of voice-cloning technology that is far superior compared to TTS (Text to Speech) conversion Clone your voice using Artificial Intelligence Tools like Lyrebird, iSpeech etc. Know about a special cloud tool that can create fictitious voices for audiobooks Know about a special cloud tool that can clone your voice in minutes by uploading bulk fragments of audio to it

With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the www.ted.com website when deciding to view a talk.

The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.