Happy New Year! Every winter holiday, I pursue a learning goal around a new topic. In between visits with family, I end up reading a lot. About a decade ago, my holiday topic was pedagogy — I still remember lugging a heavy suitcase of books through the airport — and this...

We here at deeplearning.ai wish you a wonderful holiday season. As you consider your New Year’s resolutions and set goals for 2020, consider not just what you want to do, but what you want to learn: What courses do you want to take this year?

I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on a handful of ideas.

I’ve been thinking about AI and ethics. With the techlash and an erosion of trust in technology as a positive force, it’s more important than ever that we make sure the AI community acts ethically.

Recently I wrote about major reasons why AI projects fail, such as small data, robustness, and change management. Given that some AI systems don't work, users and customers sometimes rightly wonder whether they should trust an AI system.

I’ll be spending Thanksgiving with Nova and watching her taste turkey for the first time. To those of you who celebrate Thanksgiving, I hope you spend time with loved ones, reflect on what you are thankful for, and discuss some very important topics around the dinner table...

My last two letters explored robustness and small data as common reasons why AI projects fail. In the final letter of this three-part series, I’d like to discuss change management. Change management isn’t an issue specific to AI, but given the technology’s disruptive nature...

In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source.

Building AI systems is hard. Despite all the hype, AI engineers struggle with difficult problems every day. For the next few weeks, I’ll explore some of the major challenges. Today’s topic: The challenge of building AI systems that are robust to real-world conditions.

Welcome to the Halloween edition of The Batch! I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.

I’ve heard this conversation in multiple companies: Machine learning engineer: Look how well I did on the test set! Business owner: But your ML system doesn’t work. This sucks! Machine learning engineer: But look how well I did on the test set!

I just replaced my two-year-old phone with a new one and figured out how to take long-exposure photos of Nova even while she’s asleep and the lights are very low. This piece of technology brought me a surprising amount of joy!

Last week, I saw a lot of social media discussion about a paper using deep learning to generate artificial comments on news articles. I’m not sure why anyone thinks this is a good idea. At best, it adds noise to the media environment.

Thinking about the future of machine learning programming frameworks, I recently reread computer scientist Fred Brooks’ classic essay, “No Silver Bullet: Essence and Accidents of Software Engineering.” Three decades after...

I read an interesting paper comparing the results of traditional passive learning (sitting in a lecture) versus active methods like the flipped classroom, where students watch videos at home and work on exercises in class.