The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career."

I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. For my final project I worked on a question answering model built on Stanford Question Answering Dataset (SQuAD). In this blog, I want to cover the main building blocks of a question answering model. You can find the full code on my Github repo. Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage.

Deep learning is the next big thing. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.

The course on learning problems in theorem proving introduces the design of automated and interactive theorem proving systems as well as proof certifiers and discusses the various machine learning problems that correspond to the built in heuristics. First, the course will focus on the high-level learning problems in proof assistants and techniques for the selection of relevant lemmas in large libraries. Next the focus of the course will be on strategy selection and strategy tuning using learning. Finally internal guidance of automated reasoning systems, including prediction of useful inference steps and tactics, as well as the evaluation of intermediate proof states will be discussed. Registration for the exams is mandatory, 5-2 weeks before the exam!

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Welcome to this course: Learn TensorFlow Slim(TF-Slim) From Scratch. TensorFlow-Slim is a light-weight library for defining, training, and evaluating complex models in TensorFlow. With the TensorFlow-Slim library, we can build, train, and evaluate the model easier by providing lots of high-level layers, variables, and regularizers. At the end of this course, you will be geared up to take on any challenges of implementing TensorFlow-Slim in your machine learning environment.

In this course, we have already seen several key machine learning algorithms. However, before moving on to the more fancy ones, we'd like to take a small detour and talk about data preparation. The well-known concept of "garbage in -- garbage out" applies 100% to any task in machine learning. Any experienced professional can recall numerous times when a simple model trained on high-quality data was proven to be better than a complicated multi-model ensemble built on data that wasn't clean. This article will contain almost no math, but there will be a fair amount of code. Some examples will use the dataset from Renthop company, which is used in the Two Sigma Connect: Rental Listing Inquiries Kaggle competition. In this task, you need to predict the popularity of a new rental listing, i.e. classify the listing into three classes: ['low', 'medium', 'high']. To evaluate the solutions, we will use the log loss metric (the smaller, the better).

Artificial intelligence (AI) is making headlines in newsrooms across the country. One of the latest trends we've seen is in the education system, and it's made many people wary of the ramifications that using Artificial Intelligence in the classroom will have. While AI can never replace human teachers, it can play a great role in the classroom. Read on to learn more. Electronic grading has existed for many years in the form of computer tests and Scantron testing.

When faced with the choice of saving the life of the passenger or the pedestrian passing by, how should an autonomous car be programed to act? In many scenarios, the most logical decision isn't always the most ethical one, which is why objective decision-making alone in algorithms is not enough. As machine learning and advanced artificial intelligence algorithms become more prevalent in our daily lives, it becomes increasingly important to address the inherent biases and ethical blind spots built into these systems. In fact, when we don't, we risk unleashing systems that may have far-reaching and disastrous consequences across many areas of society, from medical diagnoses to judicial decisions. Join Nathana Sharma, as she talks about the importance of designing AI algorithms that are capable of making decisions that are not just rationally correct, but also ethically right.

The kindergarten students who entered the school gate for the first time in 2017 will be graduating by 2030. These students and the young people to finish school over the next two decades will be the workers of 2040. Developments in AI are one of a number of interrelated --megatrends-- changing the nature of the labour markets across the world. The profound changes ahead demand an education approach that will provide young people with enduring capabilities and skills to harness the opportunities of technological change. There is significant uncertainty about the full impact of artificial intelligence and automation on employment but the effects are already starting to be felt.