Artificial Intelligence For Managers Getting Started Non-Coding Approach To Learn and Apply Artificial Intelligence, Machine Leaning and Deep Learning for Managers Udemy Coupon Coding New What you'll learn Understand the Basic Terminologies of AI, Machine Learning and Deep Learning Develop AI Models, Without writing a single line of code, using platforms and tools developed by Google, Microsoft and Amazon Understand the step by step approach to solve machine learning problems In Depth Discussion of various fields of AI and It Applications Understand AI algorithms and how to select one Learn how to train and tune models for optimal performance Learn What is Big Data and its importance Requirements You do not need any prior experience in AI Having basic understanding school level mathematical concepts will be useful Description AI for Managers, will help you develop AI Skills, with an objective to apply these skills at your organisation or business. Along with learning the basics, you will be learning how to build AI models from the scratch, using Non Coding Tools developed by Microsoft Azure and Google Cloud Platform and more. After you have completed the tutorials, you will have developed 4 deep learning & machine learning projects without writing a single line of code. We will explore the domains in which AI is being used and help you develop an understanding of how the logic of it works. We are going to introduce you to the core skills that will get you a foot in the door.

In this tutorial, we present the high-level steps that are involved in connecting an Android device to the cloud and developing analytics models to analyze sensor data. By the end of this tutorial you should be able to set up your own IoT hub for streaming, storing and processing device data. The following figure shows the architecture of our sample app. This tutorial requires an Android device (smartphone), an internet connection, and an IBM Cloud account. In Step 1 you will create an account on IBM Cloud and install an application on your Android phone.

Data often are formed of multiple modalities, which jointly describe the observed phenomena. Modeling the joint distribution of multimodal data requires larger expressive power to capture high-level concepts and provide better data representations. However, multimodal generative models based on variational inference are limited due to the lack of flexibility of the approximate posterior, which is obtained by searching within a known parametric family of distributions. We introduce a method that improves the representational capacity of multimodal variational methods using normalizing flows. It approximates the joint posterior with a simple parametric distribution and subsequently transforms into a more complex one. Through several experiments, we demonstrate that the model improves on state-of-the-art multimodal methods based on variational inference on various computer vision tasks such as colorization, edge and mask detection, and weakly supervised learning. We also show that learning more powerful approximate joint distributions improves the quality of the generated samples.

In this paper we explore a connection between deep networks and learning in reproducing kernel Krein space. Our approach is based on the concept of push-forward - that is, taking a fixed non-linear transform on a linear projection and converting it to a linear projection on the output of a fixed non-linear transform, pushing the weights forward through the non-linearity. Applying this repeatedly from the input to the output of a deep network, the weights can be progressively "pushed" to the output layer, resulting in a flat network that has the form of a fixed non-linear map (whose form is determined by the structure of the deep network) followed by a linear projection determined by the weight matrices - that is, we take a deep network and convert it to an equivalent (indefinite) kernel machine. We then investigate the implications of this transformation for capacity control and uniform convergence, and provide a Rademacher complexity bound on the deep network in terms of Rademacher complexity in reproducing kernel Krein space. Finally, we analyse the sparsity properties of the flat representation, showing that the flat weights are (effectively) Lp-"norm" regularised with 0

Lately, posts and tutorials about new deep learning architectures and training strategies have dominated the community. However, one very interesting research area, namely few-shot learning, is not getting the attention it deserves. If we want widespread adoption of ML we need to find ways to train them efficiently, with little data and code. In this tutorial, we will go through a Google Colab Notebook to train an image classification model using only 5 labeled samples per class. Using only 5 exemplary samples is also called 5-shot learning.

Financial Engineering and Artificial Intelligence in Python Getting Started Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE! Get Udemy Course New What you'll learn Forecasting stock prices and stock returns Time series analysis Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Exploratory data analysis Distributions and correlations of stock returns Modern portfolio theory Mean-Variance Optimization Efficient frontier, Sharpe ratio, Tangency portfolio CAPM (Capital Asset Pricing Model) Q-Learning for Algorithmic Trading Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?

The way we think about work, employment and skills is rapidly evolving in our digital-first world. Technologies like artificial intelligence (AI) and machine learning (ML) are shaping the way we work, learn, shop, socialise and much more. There is a greater need for technical skills than ever before as technology continues to transform careers and every sector of our economy. From agriculture to zoology, emerging technologies like AI have the potential to revolutionise our efficiency and productivity, improve outcomes, create entirely new jobs and free us up to focus our time and energy on higher impact, more valuable tasks and innovation. A look at how the workforce has changed over time helps us understand the impact of technology on the labour market, underlining the need for both more technical skills and a "lifelong learning" mindset.

Deep Learning (from basics) Getting Started Deep Learning with Python/ Keras At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters New What you'll learn The students will be able to understand what is Deep Learning. How to create various model and solve the problems hands-on using Keras.

Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. What do these events all have in common? It's artificial intelligence that has guided all these decisions. And the force behind them all is machine-learning algorithms that use data to predict outcomes. Now, before we look at how machine learning aids data analysis, let's explore the fundamentals of each.

Deep learning is a machine learning method that uses neural networks for prediction tasks. Deep learning methods can be used for a variety of tasks including object detection, synthetic data generation, user recommendation, and much more. In this post, I will walk through some of the best resources for getting started with deep learning. There are several online resources that are great for getting started with deep learning. Sentdex is a YouTube channel, run by Harrison Kinsley, that has several tutorials on how to implement machine learning algorithms in python.