Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. Ng has been an early adopter of online learning with the creation of Coursera.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

Python is an object-orientated language that closely resembles the English language which makes it a great language to learn for beginners as well as seasoned professionals. Examples sites that use Python are Instagram, YouTube, Reddit, NASA, IBM, Nokia, etc. Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity. It can seamlessly be used with the data structures and other frequently used AI algorithms. This is because it is the ideal language to work with for general purpose tasks. Experienced coders tend to stay more organized and productive when working with Python, as well.

In this article, I want to talk about artificial intelligence (AI) and how it is transforming the training and education sector. Before we get to that part, let's quickly take a look at how formal and informal education has evolved side by side throughout history. Formalized education has existed for thousands of years. Greek philosophers used to deliver lectures and teach their students long before the time of the Romans. It goes back hundreds of years before the Julian calendar was even introduced.

Python is a big deal. More and more beginner programmers are choosing it as their first language to learn, which means its future is more than just bright - it's dazzling. It makes coding faster, easier and fun. When combined with the object oriented programming approach these qualities are further enhanced, which means Python is virtually unstoppable. If you want to future-proof your programming skills, this is exactly what you need to learn.

About this course: This introductory course is developed for high level business people (and those on their way) who want a broad understanding of new Information Technologies and understand their potential for business functions (e.g. This is not a course for people looking for guidance on how to become a deep technical expert or implement these technologies. From Blockchain over Artificial Intelligence to Virtual Reality technologies: This course will empower business leaders to embrace the concepts and bring the state of the art information technologies into their organizations to improve client and customer engagement and ultimately the bottom line of their businesses. Instead of digital disruption, the new technologies and management methods will become the foundation of a Digital Transformation journey for better customer relationship management and client satisfaction. The content is structured in a way that promotes discussions on challenges that business management and marketing functions face due to the rise of new technologies such blockchain, cryptocurrencies, internet of things (IoT), virtual, mixed and augmented reality (VR/AR), artificial intelligence (AI) and big data.

Few-shot learning aims to build a learner that quickly generalizes to novel classes even when a limited number of labeled examples (so-called low-data problem) are available. Meta-learning is commonly deployed to mimic the test environment in a training phase for good generalization, where episodes (i.e., learning problems) are manually constructed from the training set. This framework gains a lot of attention to few-shot learning with impressive performance, though the low-data problem is not fully addressed. In this paper, we propose Transductive Propagation Network (TPN), a transductive method that classifies the entire test set at once to alleviate the low-data problem. Specifically, our proposed network explicitly learns an underlying manifold space that is appropriate to propagate labels from few-shot examples, where all parameters of feature embedding, manifold structure, and label propagation are estimated in an end-to-end way on episodes. We evaluate the proposed method on the commonly used miniImageNet and tieredImageNet benchmarks and achieve the state-of-the-art or promising results on these datasets.

We consider stochastic gradient descent (SGD) for least-squares regression with potentially several passes over the data. While several passes have been widely reported to perform practically better in terms of predictive performance on unseen data, the existing theoretical analysis of SGD suggests that a single pass is statistically optimal. While this is true for low-dimensional easy problems, we show that for hard problems, multiple passes lead to statistically optimal predictions while single pass does not; we also show that in these hard models, the optimal number of passes over the data increases with sample size. In order to define the notion of hardness and show that our predictive performances are optimal, we consider potentially infinite-dimensional models and notions typically associated to kernel methods, namely, the decay of eigenvalues of the covariance matrix of the features and the complexity of the optimal predictor as measured through the covariance matrix. We illustrate our results on synthetic experiments with non-linear kernel methods and on a classical benchmark with a linear model.

Continual or lifelong learning (CL) is one of the most challenging problems in machine learning. In this paradigm, a system must learn new tasks, contexts, or data without forgetting previously learned information. We present a scalable approach to multi-context continual learning (MCCL) in which we decouple how a system learns to solve new tasks (i.e., acquires skills) from how it stores them. Our approach leverages two types of artificial networks: (1) a set of reusable, \textit{task-specific networks} (TN) that can be trained as needed to learn new skills, and (2) a lifelong, \textit{autoencoder network} (EN) that stores all learned skills in a compact, latent space. To learn a new skill, we first train a TN using conventional backpropagation, thus placing no restrictions on the system's ability to encode the new task. We then incorporate the newly learned skill into the latent space by first recalling previously learned skills using our EN and then retraining it on both the new and recalled skills. Our approach can efficiently store an arbitrary number of skills without compromising previously learned information because each skill is stored as a separate latent vector. Whenever a particular skill is needed, we recall the necessary weights using our EN and then load them into the corresponding TN. Experiments on the MNIST and CIFAR datasets show that we can continually learn new skills without compromising the performance of existing skills. To the best of our knowledge, we are the first to demonstrate the feasibility of encoding entire networks in order to facilitate efficient continual learning.

Although anyone who has completed a basic course in Maths knows what a function is, it is a good idea to review this basic concept and associated terminology, just in case you need a refresher. A function is relationship that defines how one quantity depends on another. A function takes an input from a set and maps it an output from another set. The input set is known as the domain and the output set is known as the codomain or target set of the function. There are many ways to denote functions in machine learning literature, or in Mathematics.