Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy!

Code for this video:
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http://geometricdeeplearning.com/
https://arxiv.org/abs/1611.08097
http://3ddl.stanford.edu/CVPR1....7_Tutorial_Intrinsic
https://github.com/rusty1s/pytorch_geometric
https://pemami4911.github.io/p....aper-summaries/deep-

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When Geoffrey Hinton, a researcher at Google and professor emeritus at the University of Toronto, began his work in deep learning in the 1970s, he was told he would spend his life toiling away in obscurity. Deep learning is a form of artificial intelligence that mimics the human brain. Now, four decades later, his research is revolutionizing AI. He joins The Agenda to discuss his work and what kept him going.

The Intelligent Inspection toolset, powered by Deep Learning technology, provides powerful and robust on-device anomaly detection as well as object classification that is not possible with rule-based machine vision.

The user-friendly interface, on-device traning and example-based approach makes it easy to ensure that inspected items fulfill required quality and sorting demands, which helps to improve yield, reliability, productivity and increase customer satisfaction.

Intelligent Inspection toolset is part of the SICK Nova 2D SensorApp and runs directly on the InspectorP6xx 2D vision sensors.

And that’s not all, traditional rule-based machine vision tools are also included, combining benefits with deep learning side-by-side.

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In this video I am going to discuss about the complete road map to prepare for deep learning which will be definitely helpful for preparing for interviews
Complete DL Playlist :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only
All Playlist In My channel
Complete ML Playlist :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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Machine Learning Pipelines: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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In this video we will set up a Pytorch deep learning environment by installing Anaconda and PyCharm so that you have everything that you need so you can focus on the important stuff: coding and learning about machine learning! If you are just starting out then do not focus on the irrelevant parts, which is the IDE that you use etc. Just a small tip that I feel have benefited me :)

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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Dato GraphLab is a good software platform for Deep Learning projects that require graph analytics and other important algorithms. It provides two deep nets, sophisticated data munging, an intuitive UI, and built-in enhancements for handling big data.

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Dato GraphLab currently offers a vanilla MLP and a convolutional net. An important feature of the platform is the Graph Analytics toolset, which can be run alongside the deep learning models. Other provided tools include text analytics, a recommender, classification, regression, and clustering. You can also point GraphLab at multiple data sources in order to train data loads.

The platform has an intuitive UI along with an extension called the GraphLab Canvas. This extension offers highly sophisticated visualizations of your models.

Even though GraphLab needs to be deployed and maintained on your own hardware, the platform comes with many performance enhancements that speed up training on big data sets.
GraphLab offers three different types of built-in storage – tabular, columnar, and graph. In addition, the platform provides built-in GPU support which is extremely beneficial for training. You can also set up each type of model as a service that can be accessed programmatically through an API.

Under what circumstances would you use a graph in your deep learning projects? 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

An RBM can extract features and reconstruct input data, but it still lacks the ability to combat the vanishing gradient. However, through a clever combination of several stacked RBMs and a classifier, you can form a neural net that can solve the problem. This net is known as a Deep Belief Network.

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The Deep Belief Network, or DBN, was also conceived by Geoff Hinton. These powerful nets are believed to be used by Google for their work on the image recognition problem. In terms of structure, a Deep Belief is identical to a Multilayer Perceptron, but structure is where their similarities end – a DBN has a radically different training method which allows it to tackle the vanishing gradient.

The method is known as Layer-wise, unsupervised, greedy pre-training. Essentially, the DBN is trained two layers at a time, and these two layers are treated like an RBM. Throughout the net, the hidden layer of an RBM acts as the input layer of the adjacent one. So the first RBM is trained, and its outputs are then used as inputs to the next RBM. This procedure is repeated until the output layer is reached.

Have you ever used this method to train a Deep Belief Network? Please comment and let me know your thoughts.

After this training process, the DBN is capable of recognizing the inherent patterns in the data. In other words, it’s a sophisticated, multilayer feature extractor. The unique aspect of this type of net is that each layer ends up learning the full input structure. In other types of deep nets, layers generally learn progressively complex patterns – for facial recognition, early layers could detect edges and later layers would combine them to form facial features. On the other hand, A DBN learns the hidden patterns globally, like a camera slowly bringing an image into focus.

In the end, a DBN still requires a set of labels to apply to the resulting patterns. As a final step, the DBN is fine-tuned with supervised learning and a small set of labeled examples. After making minor tweaks to the weights and biases, the net will achieve a slight increase in accuracy.

This entire process can be completed in a reasonable amount of time using GPUs, and the resulting net is typically very accurate. Thus the DBN is an effective solution to the vanishing gradient problem. As an added real-world bonus, the training process only requires a small set of labelled data.

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

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.

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This Edureka video on "𝐂𝐎𝐕𝐈𝐃 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 " will provide you with a comprehensive and detailed knowledge of Image classification and how it can be implemented using a Convolutional Neural Network. In this video, you will be working on image processing with Python and also will learn about the convolutional neural network. Finally, we will build an end-to-end model to process and identify the Covid X-Ray images with CNN. Below are the topics covered in this COVID Detection Using Deep Learning video :
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00:00:47 Why CNN?
00:04:55 What is CNN?
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About the Course :
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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