In 2009 I started playing with LLVM for some projects (data structure jit, for genetic programming, jit for tensorflow graphs, etc), and in these projects I realized how powerful LLVM design was at the time (and still is): using an elegant IR (intermediate representation) with an user-facing API and modularized front-ends and backends with plenty of transformation and optimization passes. Nowadays, LLVM is the main engine behind many compilers and JIT compilation and where most of the modern developments in compilers is happening. On a related note, PyTorch is doing an amazing job of exposing more of the torch tracing system and its IR and graphs, not to mention their work on recent fusers and TorchDynamo. In this context, I was doing a small test to re-implement Shine, but using ATen ops for tensors and realized that there were not many educative tutorials on how to use LLVM to JIT PyTorch graphs, so this is a quick series (if time helps there will be more following posts) on how to use LLVM (python bindings) to go from PyTorch graphs (as traced by torch.fx) to LLVM IR and native code. PyTorch itself also has a compiler that uses LLVM to generate native code for subgraphs that the fuser identifies.

This course with instructor Keith McCormick provides an introduction to some advanced techniques in causal inference and causal modeling. It builds upon a foundation in Keith's course, Machine Learning and AI Foundations: Prediction, Causality, and Statistical Inference. Keith focuses the course on three major topics: The power of experiments (and the reality that they aren't always available as an option); the Bayesian statistic philosophy and approach and when it's a good choice; and an introduction to causal modeling with techniques like structural equation modeling and Bayesian networks. Join Keith in this course to learn about these advanced techniques and what makes them both powerful and interesting.

In this paper, we introduce a new task, spoken video grounding (SVG), which aims to localize the desired video fragments from spoken language descriptions. Compared with using text, employing audio requires the model to directly exploit the useful phonemes and syllables related to the video from raw speech. Moreover, we randomly add environmental noises to this speech audio, further increasing the difficulty of this task and better simulating real applications. To rectify the discriminative phonemes and extract video-related information from noisy audio, we develop a novel video-guided curriculum learning (VGCL) during the audio pre-training process, which can make use of the vital visual perceptions to help understand the spoken language and suppress the external noise. Considering during inference the model can not obtain ground truth video segments, we design a curriculum strategy that gradually shifts the input video from the ground truth to the entire video content during pre-training. Finally, the model can learn how to extract critical visual information from the entire video clip to help understand the spoken language. In addition, we collect the first large-scale spoken video grounding dataset based on ActivityNet, which is named as ActivityNet Speech dataset. Extensive experiments demonstrate our proposed video-guided curriculum learning can facilitate the pre-training process to obtain a mutual audio encoder, significantly promoting the performance of spoken video grounding tasks. Moreover, we prove that in the case of noisy sound, our model outperforms the method that grounding video with ASR transcripts, further demonstrating the effectiveness of our curriculum strategy.

A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90 percent for course-specific models.

Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper, we tackle rapid adaptation to new tasks through the framework of meta-learning, which utilizes past tasks to learn to adapt with a specific focus on industrial insertion tasks. Fast adaptation is crucial because prohibitively large number of on-robot trials will potentially damage hardware pieces. Additionally, effective adaptation is also feasible in that experience among different insertion applications can be largely leveraged by each other. In this setting, we address two specific challenges when applying meta-learning. First, conventional meta-RL algorithms require lengthy online meta-training. We show that this can be replaced with appropriately chosen offline data, resulting in an offline meta-RL method that only requires demonstrations and trials from each of the prior tasks, without the need to run costly meta-RL procedures online. Second, meta-RL methods can fail to generalize to new tasks that are too different from those seen at meta-training time, which poses a particular challenge in industrial applications, where high success rates are critical. We address this by combining contextual meta-learning with direct online finetuning: if the new task is similar to those seen in the prior data, then the contextual meta-learner adapts immediately, and if it is too different, it gradually adapts through finetuning. We show that our approach is able to quickly adapt to a variety of different insertion tasks, with a success rate of 100% using only a fraction of the samples needed for learning the tasks from scratch. Experiment videos and details are available at https://sites.google.com/view/offline-metarl-insertion.

TensorFlow is a state-of-the-art, open-source framework that streamlines developing and executing advanced analytics applications. It is powerful and holds the potential for training a model for any system with the help of graphs. It is heavily used by data scientists, developers, and predictive modelers to automate processes, develop new systems and parallel processing applications, such as neural networks. We can train and run deep neural networks for things like image video recognition, word embeddings, handwritten digit classification, etc. One of the tremendous advantages of TensorFlow is its open-source community of data scientists, ml researchers and data engineers who contribute to its repository to make it faster and more effective to develop and train ML and Deep Learning models.

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses From Udemy and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. Then this course is for you! This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. Have you never learned coding before and want to learn the basics of programming and Object Oriented Programming? Are you confused about the basics of Object Oriented Programming?

A large number of corporations are moving toward the field of data science and machine learning. There are industries ranging from pharmaceuticals, retail, manufacturing, and automobile industries that are seeking ways to promote their products and services with the use of intelligent systems driven by artificial intelligence. To make things interesting, they are being used in the development of software for self-driving vehicles that are going to take the world by surprise in the next 2–3 years. In light of this, it is important to learn the most important technologies and innovations taking place, especially in the field of automation. To learn these new technologies and tools, there are a massive number of online courses that teach the fundamentals along with practical use cases.

Causal machine learning is a field that focuses on using machine learning method to tackle causality problems. Despite the recent progress of this field, there are still many unresolved challenges including missing data, selection bias, unobserved confounders, etc., which are ubiquitous in the real world. Advances in any of the above areas can greatly reduce the gap between research and real world impact. In this competition, we focus on two fundamental challenges of causal machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will not only impact the causal ML community but also enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests. We expect participants to develop novel machine learning methodologies for causal discover between different constructs and the impact estimation of learning one construct on other constructs, which should bring fundamental advances to causal ML.