I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew's research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creating artificial intelligence. This was a very interesting conversation, i'm sure you'll enjoy. Recently we hit a very exciting milestone for the podcast: One Million Listens!!! We'd hate to miss an opportunity to show you some love, so we're holding another listener appreciation contest to celebrate the occasion.

Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems. In this post, you will discover how you can use transfer learning to speed up training and improve the performance of your deep learning model. A Gentle Introduction to Transfer Learning with Deep Learning Photo by Mike's Birds, some rights reserved. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task.

I recently started taking Deep Learning Specialization course on Coursera, and I'm enjoying it so much that I'm having even dreams of neural networks. I am currently on General Assembly London's Data Science Immersive course, of course we have a lot of projects, and all the lectures to catch up with, but we are on Christmas break, and I thought maybe I could make the best use of this break in addition to preparing my final project. Even though we have covered a broad spectrum of machine learning models and theories so far in our course, but we still didn't have a chance to deep dive into the topics of neural networks and deep learning. I'm sure that it will come later in the course, but to be honest, I couldn't wait. But at the same time, I don't want to just blindly fit and predict without understanding the underlying theory and concept of neural networks. So this was a perfect time for me to properly learn and try it myself.

Now you're thinking: Welcome to the world of machine learning and deep-neural networks Facebook automatically finds and tags friends in your photos. Google Deepmind's AlphaGo computer program trounced champions at the ancient game of Go last year. Skype translates spoken conversations in real time – and pretty accurately too. Behind all this is a type of artificial intelligence called deep learning. But what is deep learning and how does it work?

It's been a few years since I was first exposed to reinforcement learning. What got me into it was seeing this video that had trained a neural network to play Mario. As someone who grew up playing Mario, seeing deep learning being applied to something I knew so well seemed to provided the perfect introduction to the topic. Sadly though, the project was written using Torch, and I was still a naive young programmer. I didn't get too far along before the frustrations with learning lua lead me to give up, and just focus on other projects instead.

So…now we're all experts in how Deep Learning / machine learning algorithms operating on a neural network handle the task of interpreting difficult data, thanks to Part I of this series, right? Facial recognition was the example we used to show how a Deep Learning platform can train itself to model meaning out of masses of unlabeled data. But what value does it deliver for B2B marketing? That's simple: all marketing relies on targeting, and targeting relies on data. Take a look at the doubtlessly reputable product advertised on the left.

Artificial intelligence (AI) has been gaining recognition among investors and executives in various industries worldwide. The aim is to offer machines human-like intelligence and capacity to reason, upgrade functionality, fixing human errors and helping companies to improve their end user experience. AI technologies such as: speech recognition, natural language processing, deep learning and machine learning have impacted key industries during recent years despite the fact that certain companies have been trying to create fear from A.I and robotics. It is some companies' sales strategy. It all started when Elon Musk spread all over social media and PR platforms the fear from A.I bandwagon.

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. Specifically, the V100's new Tensor cores are not best suited for recurrent neural networks (RNN) broadly and a specialized version of them, long-short term memory models (LSTMs), according to Xcelerit; both are widely in finance applications for handling time series inputs. "For the tested RNN and LSTM deep learning applications, we notice that the relative performance of V100 vs. P100 increases with network size (128 to 1024 hidden units) and complexity (RNN to LSTM). We record a maximum speedup in FP16 precision mode of 2.05x for V100 compared to the P100 in training mode – and 1.72x in inference mode. Those figures are many-fold below the expected performance for the V100 based on its hardware specifications (spec below, click to enlarge)," reports Xcelerit, an Ireland-based provider of software tools for quantitative finance, engineering, and research.

If you missed Part 1, you can check it out here! The next step is obtaining unauthorized access to user accounts. Imagine cybercriminals' need to get unauthorized access to a user's session. The obvious way is to compromise the account. For mass hacking, one of the annoying things is a captcha bypass.

This article is a reboot for our Quintessentially series. Corsair's Institute is committed to excellence in learning. We are also committed to experimentation. In version 2.0, we hope to provide a little more visual, a little more context, and (as always) thought-provoking, educational content. Deep Learning is a big buzzword today.