As you might know, deeplearning.ai The first batch contains Course 1 to 3. And only recently (as of November 15), Course 4, "Convolution Neural Networks" was released. And Course 5 is supposedly released in late November. So Course 3, "Structuring Machine Learning Projects" was more the "final" course in the first batch.

As you all know, Prof. Ng has a new specialization on Deep Learning. I wrote about the course extensively yet informally, which include two "Quick Impressions" before and after I finished Course 1 to 3 of the specialization. I also wrote three posts just on Heroes on Deep Learning including Prof. Geoffrey Hinton, Prof. Yoshua Bengio and Prof. Pieter Abbeel and Dr. Yuanqing Lin . This is my full review of Course 1 after finish watching all the videos. I will give a description on what the course is about, and why you want to take it.

Google's AI researchers recently showed off a new method for teaching computers to understand why some images are more aesthetically pleasing than others. Traditionally, machines sort images using basic categorization – like determining whether an image does or does not contain a cat. The new research demonstrates that AI can now rate image quality, regardless of category. The process, called neural image assessment (NIMA), uses deep learning to train a convolutional neural network (CNN) to predict ratings for images. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network … Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline.

In a previous tutorial, I demonstrated how to create a convolutional neural network (CNN) using TensorFlow to classify the MNIST handwritten digit dataset. TensorFlow is a brilliant tool, with lots of power and flexibility. However, for quick prototyping work it can be a bit verbose. Enter Keras and this Keras tutorial. Keras is a higher level library which operates over either TensorFlow or Theano, and is intended to stream-line the process of building deep learning networks.

I just finished a post on explaining the relationship between Artificial Intelligence, Machine Learning, and Deep Learning. And somebody immediately pointed out: But what about Data Science? How does Data Science relate to all this? That's what I am going to write about today then. In case you do not want to read the whole post from yesterday (shame on you!), here is a quick summary: Deep Learning is a subset of methods from Machine Learning.

After seeing a bunch of online quizzes that could guess what kind of year I will have in 2018 based off of strange things such as my preferred pizza toppings (mushrooms and green peppers) or my favorite Beatles songs (Penny Lane and Come Together FYI), I felt inspired to put together a list of AI predictions for 2018. As with all things AI, I reached out to my fellow ROSS cofounder, Jimoh Ovbiagele, the brains and the beauty of the operation, for his take on where he sees deep learning going in 2018, what deep learning improvements he sees are on the horizon, challenges the AI community will face this coming year and lastly, to get his take on what his AI wish for 2018 is. Andrew Arruda: First things first Jimoh, I'd like your prediction on where you think deep learning will make the biggest impact in 2018? Amazon, Google, and Apple sold millions of devices with voice interfaces in 2017. To deliver on their promises, they are betting heavily on deep learning to process and fulfill the voice commands given by their users.

The world is a confusing place, especially for an AI. But a neural network developed by UK artificial intelligence firm DeepMind that gives computers the ability to understand how different objects are related to each other could help bring it into focus. Humans use this type of inference – called relational reasoning – all the time, whether we are choosing the best bunch of bananas at the supermarket or piecing together evidence from a crime scene. The ability to transfer abstract relations – such as whether something is to the left of another or bigger than it – from one domain to another gives us a powerful mental toolset with which to understand the world. It is a fundamental part of our intelligence says Sam Gershman, a computational neuroscientist at Harvard University.

Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

Speaker: Ilya Sutskever, OpenAI Hosted by: Vector Institute Date and Time: Thursday, December 14, 2017 - 12:00pm to 1:00pm Location: Fields Institute, Room 230 Abstract: In the first part, I will talk about meta learning, which is the problem of training a system that quickly learns to solve a wide variety of tasks. I will present meta reinforcement learning algorithms that can quickly solve simulated robotics tasks, and show how a simple meta learning approach can address the sim2real problem in robotics. The second part will be on self play. Self play systems provide a perfectly-fined grained curriculum, a potentially indefinite incentive for improvement, and a way of converting compute into data. I will present several recent results in self play and discuss their future potential.

Machine learning as a field has been around for a long time before deep neural networks took over the scene. Here are a list of the algorithms you need to know, so you can tackle any problem that comes your way. This isn't an exhaustive list, but your bases will be mostly covered. Also wanted to announce that my medium blog will be transitioning from general ML focus to a deep learning focus. Most of my work nowadays involves creating novel deep learning systems, so I want to spend more time writing about it!