Time series forecasting has a wide range of applications: finance, retail, healthcare, IoT, etc. Recently deep learning models such as ESRNN or N-BEATS have proven to have state-of-the-art performance in these tasks. Nixtlats is a python library that we have developed to facilitate the use of these state-of-the-art models to data scientists and developers, so that they can use them in productive environments. Written in pytorch, its design is focused on usability and reproducibility of experiments. For this purpose, nixtlats has several modules:

Data: contains datasets of various time series competencies.
Models: includes state-of-the-art models.
Evaluation: has various loss functions and evaluation metrics.

Objective:

- To introduce attendees to the challenges of time series forecasting with deep learning.
- Commercial applications of time series forecasting.
- Describe nixtlats, their components and best practices for training and deploying state-of-the-art models in production.
- Reproduction of state-of-the-art results using nixtlats from the winning model of the M4 time series competition (ESRNN).

Project repository: https://github.com/Nixtla/nixtlats.

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Welcome! Join us for a live workshop where you can follow along with the creator of FastAPI Sebastián Ramírez to build your very own AI image generation web application! He will outline the core components of the FastAPI web framework, and his application will leverage the newly-released Stable Diffusion text-to-image deep learning model.

We will be taking questions during the event. Please submit your question or upvote others' here:
https://app.sli.do/event/cgvyaL8y7m12zZLb1c6sP8

Speakers
Sebastián Ramírez, Creator of FastAPI, Senior Staff Software Engineer at Forethought
https://www.linkedin.com/in/tiangolo/

Dr. Greg Loughnane, Head of Product & Curriculum at FourthBrain
https://www.linkedin.com/in/gregloughnane/

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Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns.

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The RNTN was conceived by Richard Socher in order to address a key problem of current sentiment analysis techniques – double negatives being treated as negatives. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead they are groups of neurons – the more complicated the input data the more neurons are required. As expected, the root group connects to each leaf group, but the leaf groups do not share a connection with each other. Despite the simple structure of the net, an RNTN is capable of extracting deep, complex patterns out of a set of data.

An RNTN detects patterns through a recursive process. In a sentence-parsing application where the objective is to identify the grammatical elements in a sentence (like a noun phrase or a verb phrase, for example), the first and second words are initially converted into an ordered set of numbers known as a vector. The conversion method is highly technical, but the numerical values in the vector indicate how closely related the words are to each other compared to other words in the vocabulary.

Once the vectors for the first and second word are formed, they are fed into the left and right leaf groups respectively. The root group outputs, among other things, a vector representation of the current parse. The net then feeds this vector back into one of the leaf groups and, recursively, feeds different combinations of the remaining words into the other leaf group. It is through this process that the net is able to analyze every possible syntactic parse. If during the recursion the net runs out of input, the current parse is scored and compared to the previously discovered parses. The one with the highest score is considered to be the optimal parse or grammatical structure, and it is delivered as the final output.

After determining the optimal parse, the net backtracks to figure out the appropriate labels to apply to each substructure; in this case, substructures could be noun phrases, verb phrases, prepositional phrases, and so on.

RNTNs are used in Natural Language Processing for both sentiment analysis and syntactic parsing. They can also be used in scene parsing to identify different parts of an image.

Have you ever worked with data where the underlying patterns were hierarchical? Please comment and let us know what you learned.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Hello and welcome to a deep learning with Python and Pytorch tutorial series, starting from the basics.

Text-based tutorials and sample code: https://pythonprogramming.net/....introduction-deep-le

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This video covers the additional work and considerations you need to think about once you have a deep neural network that can classify your data. We need to consider that the trained network is usually part of a larger system and it needs to be incorporated into that design. We also want to have some amount of confidence that the model will work on unseen data and that it’s going to interact as expected with the other system components. Ultimately, we also want to deploy it onto a target device which requires certain performance characteristics.

Check out these other links:

• MATLAB Deep learning examples: https://bit.ly/3deCj40​
• 5 Reasons to use MATLAB for deep learning: https://bit.ly/2QlbNNc
• Getting Started with Deep Network Designer: https://bit.ly/2Qof12l
• Quantization of Deep Neural Networks: https://bit.ly/3daOj6u
• Multi-Loop PI Control of a Robotic Arm: https://bit.ly/3sevqDS
• Deploying Deep Neural Networks to GPUs and CPUs using MATLAB Coder and GPU Coder: https://bit.ly/3tfhEST
• Deep Learning HDL Toolbox: https://bit.ly/3tl88xF

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See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

Blog: https://lambdalabs.com/blog/de....ep-learning-is-the-f

An overview of how deep learning is completely transforming the video game industry. We go over: photorealistic neural rendering, deepfakes for gaming, GAN theft auto and dreaming up game engines with neural networks, large language models for building realistic NPCs, and using OpenAI Codex to automatically program games.

It's really clear to us at Lambda that deep learning is the most important technology to impact gaming since the advent of 3D graphics.

A unique dual chipset makes AXIS P3255-LVE the perfect platform for analytics based on deep learning. Ideal for various surveillance situations, this outdoor-ready dome camera features granular object classification and all the high-quality AXIS P32 Series features.
Discover more at https://www.axis.com/products/axis-p3255-lve

At Axis Communications we offer scalable network video surveillance solutions, including intelligent analytics, for businesses of any size. We can provide you with a complete A-Z network video solution. Or you can integrate exactly what you need right now into your existing solution and add more products from Axis at your own pace. Our solutions protect your premises – and your bottom line.

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The video is copyright protected and belongs to Axis Communications AB (Axis). Axis does not authorize reproduction of any copyright protected material for commercial purposes. Nor do we permit local hosting of Axis materials on third-party webpages or social media accounts. Instead, we encourage and specifically authorize you to “deep link” to relevant Axis videos available e.g. on our official YouTube channel.

In this 2018 GDC session, Valve's John McDonald discusses how Valve has utilized Deep Learning to combat cheating in Counter-Strike: Global Offensive.

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GDC talks cover a range of developmental topics including game design, programming, audio, visual arts, business management, production, online games, and much more. We post a fresh GDC video every day. Subscribe to the channel to stay on top of regular updates, and check out GDC Vault for thousands of more in-depth talks from our archives.

Dawn Song is well-known for her research on the intersection of deep learning and security. Aside from her research, Song is also a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley and CEO of Oasis Labs, a blockchain startup that is creating a privacy-first cloud computing platform no blockchain. She has received several awards for her work including the MacArthur Fellowship, the Guggenheim Fellowship, and Best Paper awards from top conferences.

Andrew sits down with Song to chat about her unconventional career path and her current research projects.

Here’s what you’ll learn in the interview:

00:34: How Song first got started in deep learning and security
4:00: How Song self-designed a reading program structured around representational learning
13:22: How computer security can help deep learning
17:03: Song’s research on how to build resilient machine learning systems
21:55 How a “consistency check” approach can defend against attacks
25:31: Song’s work in AI and data privacy
27:49: How deep learning can help computer security
30:16: How Song’s startup, Oasis Labs, is creating privacy-preserving smart contracts
34:42: Song’s advice for learners breaking into a new field


Want to build your own career in deep learning? Get started by taking the Deep Learning Specialization.

H2O.ai is a software platform that offers a host of machine learning algorithms, as well as one deep net model. It also provides sophisticated data munging, an intuitive UI, and several built-in enhancements for handling data. However, the tools must be run on your own hardware.

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H2O.ai was founded by SriSatish Ambati, Cliff Click, and Arno Candel. In addition to its only deep net – a vanilla MLP – the platform offers a variety of models like GLM, Distributed Random Forest, Naive Bayes, a K-Means clustering model, and a few others. H2O.ai can be linked to multiple data sources in order to train data loads.

The UI is highly intuitive, but you can also work with the tools through other apps like Tableau or Excel. These interfaces allow you to set up a deep net by configuring its hyper-parameters.

H2O.ai needs to be deployed and maintained on your own hardware, which may be a limiting factor. However, the platform comes with many performance enhancements like in-memory map-reduce, columnar compression, and distributed parallel processing. Depending on your hardware’s capabilities, training on big data sets could be completed in a reasonable amount of time. As an added note, it’s unclear whether or not GPU support is a built-in feature at this point in time.

Do you have any experience with the H2O.ai platform? Please comment and share your thoughts.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Submission Link (for Bootcamp candidates):https://forms.gle/iG3dYP35cwDptAkc7

👽👽Last submission Date: 16th July 2022👽👽
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01:01:29 Dataset Link and Jam board Link


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From the MaRS Discovery District, to ventureLAB, to the Durham College AI/HUB, the support you need to grow your AI business is here.

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This demo uses MATLAB® to train a SVM classifier with features extracted, using a pretrained CNN for classifying images of four different animal types: cat, dog, deer, and frog. Images are used from the CIFAR-10 dataset (https://goo.gl/oNH94f).

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MATH COURSES (7 day free trial)
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REFERENCES
[1] The main Paper: https://arxiv.org/abs/1706.03762
[2] Tensor2Tensor has some code with a tutorial: https://www.tensorflow.org/tut....orials/text/transfor
[3] Transformer very intuitively explained - Amazing: http://jalammar.github.io/illustrated-transformer/
[4] Medium Blog on intuitive explanation: https://medium.com/inside-mach....ine-learning/what-is
[5] Pretrained word embeddings: https://nlp.stanford.edu/projects/glove/
[6] Intuitive explanation of Layer normalization: https://mlexplained.com/2018/1....1/30/an-overview-of-
[7] Paper that gives even better results than transformers (Pervasive Attention): https://arxiv.org/abs/1808.03867
[8] BERT uses transformers to pretrain neural nets for common NLP tasks. : https://ai.googleblog.com/2018..../11/open-sourcing-be
[9] Stanford Lecture on RNN: http://cs231n.stanford.edu/sli....des/2018/cs231n_2018
[10] Colah’s Blog: https://colah.github.io/posts/....2015-08-Understandin
[11] Wiki for timeseries of events: https://en.wikipedia.org/wiki/....Transformer_(machine