This article was published as a part of the Data Science Blogathon. This is Part 1 of the Comprehensive tutorial on Deep learning. This tutorial or guide is mostly for beginners, and I'll try to define and emphasize the topics as much as I can. Since Deep learning is a very Huge topic, I would divide the whole tutorial into few parts. Be sure to read the other parts if you find this one useful.

Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python. Recommender system is a promising approach to boost sales to the next level by suggesting the right products to the right customers. This course starts by showing you the main solutions of recommender systems in the industry and the hypotheses behind the main solutions. You'll then learn how to build collaborative filtering models with fastai, and exercise the trained model on test datasets. As you advance, you'll visualize latent features, interpret weights and biases, and check what similar users/Items are from the model's perspective.

This tutorial will introduce Survival Analysis and its wide scope. Survival analysis is applicable to all fields that deal with time-dependent data: medicine, biology, engineering, marketing, finance, and many others. A data scientist in the role of a survival analyst is involved in questions concerning clinical trials, customer churn, time till violent death of Roman emperors, the failure rate of products or systems, and a practically infinite number of other situations that raise the question: "When will it happen, and what's its risk of failure or its chance of success?" After the introduction in chapter 1, we will explore a number of examples in chapter 2 -- and then complete the tutorial with bathtub-shaped lifetimes. To begin with: What is force of mortality? It is the conditional probability of death at a particular instant after having survived up to that instant. Also called the intensity of mortality or the instantaneous death rate in actuarial science. In fields that are less concerned about predicting the transition to the afterlife, it is better known as the hazard rate.

Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine. Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI. Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI. Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate. Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.

Background Analyzing kinematic and video data can help identify potentially erroneous motions that lead to sub-optimal surgeon performance and safety-critical events in robot-assisted surgery. Methods We develop a rubric for identifying task and gesture-specific Executional and Procedural errors and evaluate dry-lab demonstrations of Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize erroneous parts of demonstrations by labeling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures. Results Executional error frequency varies by task and gesture, and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analyzing error-specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style. Conclusions This study provides insights into context-dependent errors that can be used to design automated error detection mechanisms and improve training and skill assessment.

We describe the constraint modelling tool Savile Row, its input language and its main features. Savile Row translates a solver-independent constraint modelling language to the input languages for various solvers including constraint, SAT, and SMT solvers. After a brief introduction, the manual describes the Essence Prime language, which is the input language of Savile Row. Then we describe the functions of the tool, its main features and options and how to install and use it.

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.

In this tutorial, we are implementing a Deep Convolutional GAN (DCGAN) with TensorFlow 2 / Keras, based on the paper, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Radford et al., 2016). This was one of the earliest GAN papers and is typically what you'd read to get started with learning GANs. Before we get started, are you familiar with how GANs work? If not, be sure to look at my previous post, "Intro to GANs," for a high-level intuition of how GANs work in general. Each GAN has at least one generator and one discriminator.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!

There are several courses on Machine Learning and AI. What is unique about this course? Don't waste your time sifting through mountains of techniques that are in the wild What exactly will you learn in this course? My name is Chandra Lingam, and I am the instructor for this course. I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.