Deep Learning ultimately is about finding a minimum that generalizes well -- with bonus points for finding one fast and reliably. Our workhorse, stochastic gradient descent (SGD), is a 60-year old algorithm (Robbins and Monro, 1951) [1], that is as essential to the current generation of Deep Learning algorithms as back-propagation. Different optimization algorithms have been proposed in recent years, which use different equations to update a model's parameters. Adam (Kingma and Ba, 2015) [18] was introduced in 2015 and is arguably today still the most commonly used one of these algorithms. This indicates that from the Machine Learning practitioner's perspective, best practices for optimization for Deep Learning have largely remained the same.

MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks. However, it is hard for MLPs to do classification and regression on sequences. In this Python deep learning tutorial, a GRU is implemented in TensorFlow. Tensorflow is one of the many Python Deep Learning libraries. By the way, another great article on Machine Learning is this article on Machine Learning fraud detection.

Artificial intelligence, machine learning and deep learning have dominated the headlines in recent years and are steadily being woven into our daily lives. With robots having already transformed several industries, the question on everybody's mind today is: how will AI affect me and my job?

Deep learning's pre-eminence to the enterprise today is significant for two reasons. It represents the ultimate expression of machine learning's advanced capabilities and, as such, has become virtually synonymous with artificial intelligence because of its progressive learning prowess. Deep learning is at the core of the most intricate AI capabilities including speech recognition, image and video recognition, speech generation and aspects of robotics. In considering the massive influx of unstructured data besieging enterprises such as healthcare organizations, the ascending interest in AI, and the pivotal context with which deep learning purveys nearly any use case, it's clear 2018 is the year this technology's utility will finally supersede classic machine learning's. "Traditional machine learning is more like statistics," indico CEO Tom Wilde reflected.

There has been a discussion recently about using RL to land a SpaceX booster. Coincidentally I've been working on exactly this in OpenAI. It was as much fun as it was frustrating at times. It's trained with a PPO implementation from Unity that I've changed to work with OpenAI (GitHub). The official OpenAI implementation is convoluted and impossible to work with in my opinion. This particular agent took 200'000 tries over the course of 12 hours and 20 million frames (with a frame skip value of 5, so 100 million total frames).

Well, some people try to apply algebraic topology (and even algebraic geometry) to ML, so abstract algebra, a prerequisite for AT and AG, is useful in that sense. However, I'd rather read many good recent papers in deep learning to apply them for your research instead of studying AA and AT, as I see that's likely to result in more substantial results. Some recent AT application to ML includes On Characterizing the Capacity of Neural Networks using Algebraic Topology .

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Every time a new tool or app is invented, a new word follows. So, let's tackle two that have been flying around our heads for the past few years: Machine Learning (ML) and Deep Learning (DL). Techies, business gurus, and marketers love these words and throw them around whether or not they understand the differences. Side Note: We know that this topic is old news, it's discussed continuously. Which is why we had to write about it, clearly it's not being fully understood because all the current content out there is either too simple or too complicated.

Spotlight is a well-implemented python framework for constructing a recommender system. It contains two major types of models, factorization model and sequence model. The former one makes use of the idea behind SVD, decomposing the utility matrix (the matrix that records the interaction between users and items) into two latent representation of user and item matrices, and feeding them into the network. The latter one is built with time-series model such as Long Short-term Memory (LSTM) and 1-D Convolutional Neural Networks (CNN). Since the backend of Spotlight is PyTorch, make sure you have installed proper version of PyTorch before using it.

Can deep learning be done at the edge of the network, in real time, without a team of data scientists in attendance? That's the promise of Boston-based startup Neurala Inc. and its twist on deep learning, a technology it's dubbed lifelong deep neural networks or L-DNNs. L-DNNs are designed to "overcome the catastrophic forgetting" problem encountered with traditional deep neural nets, technology that uses a hierarchy of algorithms and layers of processing to produce an outcome. Deep neural nets learn sequentially. To teach a deep neural net to recognize a new object, data scientists have to start the entire training process over, which requires time and computational power via the cloud.