Learn about PyTorch from zero to hero for Deep Learning. This tutorial is the most beginner-friendly place on the internet to learn PyTorch for deep learning.

Data preparation is required when working with neural network and deep learning models. Increasingly data augmentation is also required on more complex object recognition tasks. In this post you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras. Like the rest of Keras, the image augmentation API is simple and powerful. Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation.

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Take your first steps to becoming a 3D character artist - learn everything from modelling to painting to animating the character. The course is the sequel to the highly popular Blender Character Creator course, enjoyed by 10s of thousands of students. This course has been created using Blender 2.83 and is compatible with newer versions of Blender. Whether you're a beginner or more advanced, the experienced instructors will take you through every step of the process, ensuring that you aren't just "copy and pasting" what you see, but learning the tools and developing your own creative process as you go. For each concept taught this course includes a relevant challenge for you to implement yourself.

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. Learning such compositional structures has been a challenge for artificial systems, due to the underlying combinatorial search. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. This dissertation integrated these two lines of work to present a general-purpose framework for lifelong learning of functionally compositional structures. The framework separates the learning into two stages: learning how to combine existing components to assimilate a novel problem, and learning how to adapt the existing components to accommodate the new problem. This separation explicitly handles the trade-off between stability and flexibility. This dissertation instantiated the framework into various supervised and reinforcement learning (RL) algorithms. Supervised learning evaluations found that 1) compositional models improve lifelong learning of diverse tasks, 2) the multi-stage process permits lifelong learning of compositional knowledge, and 3) the components learned by the framework represent self-contained and reusable functions. Similar RL evaluations demonstrated that 1) algorithms under the framework accelerate the discovery of high-performing policies, and 2) these algorithms retain or improve performance on previously learned tasks. The dissertation extended one lifelong compositional RL algorithm to the nonstationary setting, where the task distribution varies over time, and found that modularity permits individually tracking changes to different elements in the environment. The final contribution of this dissertation was a new benchmark for compositional RL, which exposed that existing methods struggle to discover the compositional properties of the environment.

The 44th International Conference on Software Engineering(ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Since ICSE was held as a solely virtual conference for the last two years, the opportunity to interact with other members of the software engineering community in person and to engage in insightful discussions in a physical room was greatly welcomed. Each day was organized into paper sessions, poster sessions, and Birds of a Feather(BoF) sessions, in addition to plenty of time for networking. Each paper session consisted of around 6-10 5 minute talks and time for questions for the authors. The Birds of a Feather sessions allowed for a broader discussion on a topic; the sessions varied in terms of topics and format. In this document, we summarize themes of research that we observed at the conference.

Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based methods. To solve this problem, we propose a method for optimizing a pipeline system composed of modules implemented with arbitrary methods for dialogue performance. With our method, neural-based components called post-processing networks (PPNs) are installed inside such a system to post-process the output of each module. All PPNs are updated to improve the overall dialogue performance of the system by using reinforcement learning, not necessitating each module to be differentiable. Through dialogue simulation and human evaluation on the MultiWOZ dataset, we show that our method can improve the dialogue performance of pipeline systems consisting of various modules.

We study learning in periodic Markov Decision Process(MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period and as sub-linear with the horizon length. Numerical results demonstrate the efficacy of PUCRL2.

This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes. Our code is available at https://github.com/mbosc/twf.

One of the measures of the health of a deep learning project is the degree to which it utilizes the training resources that it was allocated. Whether you are training in the cloud or on your own private infrastructure, training resources cost money, and any block of time in which they are left idle represents a potential opportunity to increase training throughput and overall productivity. This is particularly true for the training accelerator -- typically the most expensive training resource -- whether it be a GPU, a Google TPU, or a Habana Gaudi. This blog is a sequel to a previous post on the topic of Overcoming Data Preprocessing Bottlenecks in which we addressed the undesired scenario in which your training accelerator, henceforth assumed to be a GPU, finds itself idle while it waits for data input from an overly tasked CPU. The post covered several different ways of addressing this type of bottleneck and demonstrated them on a toy example, all the while emphasizing that the best option would very much depend on the specifics of the model and project at hand.