Perhaps the most well-known resource for learning deep learning is Andrew Ng's series of 5 courses on Coursera. Those courses are still a great resource for anyone learning the fundamentals of the field but they are now a few years old (their launch was announced in August 2017). In this post, I will give you three main reasons why you should instead start from MIT's course that I am going to tell you about. Before I try to convince you to start your deep learning journey from there, here is a brief description of the course itself. This course is getting released now, as we speak (rather, … as you read).

BESTSELLER, 5.0 (2 ratings), Created by Alexander Hagmann, English [Auto-generated] This is the first ever comprehensive Python Course for Business & Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business & Finance world. You will understand and master all required theoretical concepts behind the projects and the code from scratch. Learning Python is more effective when having the right context and the right examples (avoid toy examples!). Learning and mastering essential theories and concepts in Business, Finance, Statistics and Regression is way easier and more effective with Python as you can simulate, visualize and dynamically explain the intuition behind theories, math and formulas.

If there is one thing we learned from the COVID-19 pandemic, it's that when humans are sent home, machines keep working. This doesn't mean that robots will take over the world. It does, however, mean that our technical landscape is changing. Human history has a long and favorable track record of technological advancements, particularly when it comes to ideas that seem ludicrous at the time (Wright brothers, anyone?). The printing press, assembly line and personal computer have all helped move civilization forward by leaps and bounds over the last few centuries.

New resolutions to become a data scientist have to be made! And surely things can only get better after the tumultuous ride that's been 2020? And what better way to end this year and welcome the new one than planning out your entire career in one place? That's right – we are back with the most in-demand learning path in the data science community! Every year we release the data science learning path which is viewed and loved by thousands of data science aspirants all around the globe.

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation Figure 1: Operational representation of Avalanche with its of continual learning algorithms.

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated into deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this paper, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be utilized to improve artificial neural networks.

This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation (Müller et al., 2019). We begin by introducing the motivation behind on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We provide a novel connection on how label smoothing affects distributions of semantically similar and dissimilar classes. Then we propose a metric to quantitatively measure the degree of erased information in sample's representation. After that, we study its one-sidedness and imperfection of the incompatibility view through massive analyses, visualizations and comprehensive experiments on Image Classification, Binary Networks, and Neural Machine Translation. Finally, we broadly discuss several circumstances wherein label smoothing will indeed lose its effectiveness. Recently a large body of studies is focusing on exploring the underlying relationships between these two methods, for instance, Müller et al. (Müller et al., 2019) discovered that label smoothing could improve calibration implicitly but will hurt the effectiveness of knowledge distillation. Yuan et al. (Yuan et al., 2019) considered knowledge distillation as a dynamical form of label smoothing as it delivered a regularization effect in training. The recent study (Lukasik et al., 2020) further noticed label smoothing could help mitigate label noise, they showed that when distilling models from noisy data, the teacher with label smoothing is helpful.

Claim verification is the task of predicting the veracity of written statements against evidence. Previous large-scale datasets model the task as classification, ignoring the need to retrieve evidence, or are constructed for research purposes, and may not be representative of real-world needs. In this paper, we introduce a novel claim verification dataset with instances derived from search-engine queries, yielding 10,987 claims annotated with evidence that represent real-world information needs. For each claim, we annotate evidence from full Wikipedia articles with both section and sentence-level granularity. Our annotation allows comparison between two complementary approaches to verification: stance classification, and evidence extraction followed by entailment recognition. In our comprehensive evaluation, we find no significant difference in accuracy between these two approaches. This enables systems to use evidence extraction to summarize a rationale for an end-user while maintaining the accuracy when predicting a claim's veracity. With challenging claims and evidence documents containing hundreds of sentences, our dataset presents interesting challenges that are not captured in previous work -- evidenced through transfer learning experiments. We release code and data to support further research on this task.

The'Scientific Programming with C ' is easiest and the most innovative and complete hands-on practical C course on the Udemy Platform for learning scientific and research data programming! While languages like Python and R are increasingly popular for Scientific Programming or Data sciences, C/ C can be a stronger choice for efficient and effective data and scientific computing. The focus of this course lies on learning beginner to advanced programming on high-performance computers, object-oriented software design, generic or template-based programming, and the efficient implementation of numerical algorithms. C is the best choice for efficient and effective programming in Research Data mining & Scientific Computing. Learn from the basics of C to the advanced and useful libraries like STL, BOOST, OpenMP and MPI! Main learning goals in this awesome course can be formulated as: The focus of this course lies on aspects of software development like programming on high-performance computers, object-oriented software design, generic (template-based) programming, and the efficient implementation of numerical algorithms.