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Watch my previous video on the paper walkthrough to understand the theory behind VAEs. Hopefully you find the video useful to understand VAEs in depth! :)

Useful article on VAEs:
https://sannaperzon.medium.com..../paper-summary-varia

Timestamps:
0:00 - Introduction
2:45 - Model architecture
15:50 - Training loop
31:10 - Inference example
39:10 - Ending

Welcome to part 2 of the web scraping with Beautiful Soup 4 tutorial mini-series. In this tutorial, we're going to talk about navigating source code to get just the slice of data we want.

Tutorial code: https://pythonprogramming.net/....navigating-pages-scr

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It's a question that physical media fans are debating: Is 8K coming? In this episode, we take a look at what we know and explore what the future may hold for movie fans and home media collectors!

Cereal At Midnight is viewer supported! To unlock the entire Collecting At Midnight series plus hours of collection tours, secret commentaries and videos, and over 100 EXCLUSIVE EPISODES, visit Patreon.com/CerealAtMidnight!

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For this video, I thought we could just chat about movies so let's dive into it! Make sure you have all notifications turned on and hit that SUBSCRIBE button to get all my #ASMR every week - thank you so much! #8K #movies

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With so many alternatives available, why are neural nets used for Deep Learning? Neural nets excel at complex pattern recognition and they can be trained quickly with GPUs.

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Historically, computers have only been useful for tasks that we can explain with a detailed list of instructions. As such, they tend to fail in applications where the task at hand is fuzzy, such as recognizing patterns. Neural Networks fill this gap in our computational abilities by advancing machine perception – that is, they allow computers to start to making complex judgements about environmental inputs. Most of the recent hype in the field of AI has been due to progress in the application of deep neural networks.

Neural nets tend to be too computationally expensive for data with simple patterns; in such cases you should use a model like Logistic Regression or an SVM. As the pattern complexity increases, neural nets start to outperform other machine learning methods. At the highest levels of pattern complexity – high-resolution images for example – neural nets with a small number of layers will require a number of nodes that grows exponentially with the number of unique patterns. Even then, the net would likely take excessive time to train, or simply would fail to produce accurate results.

Have you ever had this problem in your own machine learning projects? Please comment.

As a result, deep nets are essentially the only practical choice for highly complex patterns such as the human face. The reason is that different parts of the net can detect simpler patterns and then combine them together to detect a more complex pattern. For example, a convolutional net can detect simple features like edges, which can be combined to form facial features like the nose and eyes, which are then combined to form a face (Credit: Andrew Ng). Deep nets can do this accurately – in fact, a deep net from Google beat a human for the first time at pattern recognition.

However, the strength of deep nets is coupled with an important cost – computational power. The resources required to effectively train a deep net were prohibitive in the early years of neural networks. However, thanks to advances in high-performance GPUs of the last decade, this is no longer an issue. Complex nets that once would have taken months to train, now only take days.

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Google's TensorFlow is currently the most popular Deep Learning library on GitHub. This video will provide an overview of the library's strengths, weaknesses, and numerous features.

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TensorFlow grew out of a proprietary deep net library called DistBelief, which was built by Google as part of the Google Brain project. The goal of this project is to build a system for large scale machine learning models so that they can be deployed on a variety of platforms – from smart phones to clusters consisting of 100s of nodes and 1000s of GPUs. Another goal is to increase the portability of machine learning and to simplify the process of transferring research models to commercial-grade applications.

Like Theano, TensorFlow is based on a computational graph – a directed graph where nodes represent mathematical operations and edges represent the flow of data between nodes. The data set carried by an edge from one node to another is called a tensor, a type of multi-dimensional array. The library's name stems from the way these tensors flow across the network. Due to its structure, TensorFlow is not limited to neural net applications. Any domain where computation can be modelled as a data flow graph can benefit from the TensorFlow library. TensorFlow also shares several important features with Theano such as auto differentiation, shared and symbolic variables, and common sub-expression elimination.

Different types of deep nets can be built using TensorFlow, although TensorFlow does not allow for hyper-parameter configuration of deep nets. The TensorFlow Roadmap suggests a possible layer configuration feature, but there is currently no mention of when this would be implemented. For now, the library Keras provides TensorFlow with a layer configuration option. TensorFlow also provides a “no-nonsense” interface for C++. In addition, TensorFlow has comprehensive and informative documentation, including the March 2016 release of a free Massive Open Online Course (MOOC) on Udacity.

TensorFlow and Theano are very similar, but TensorFlow was several orders of magnitude slower than Theano from its release until March 2016. The TensorFlow community has since worked to combat performance and other issues. An April 2016 update from Soumith Chintala of Facebook shows that TensorFlow performed well in the ImageNet category, with no Theano-based libraries in the rankings.

An important feature of TensorFlow v0.8 is the implementation of data parallelism, which is similar to the Iterative Map-Reduce from Deeplearning4j. This version of TensorFlow also implements model parallelism, where different portions of the graph can be trained on multiple devices in parallel. The TensorBoard feature allows users to visualize both performance and the different levels of the network architecture. Due to community requests, TensorFlow's Roadmap also includes support for OpenCL, a fast-rising standard for GPU computing.

URLs
Soumith Chintala benchmarks - https://github.com/soumith/convnet-benchmarks
TensorFlow road map - https://www.tensorflow.org/ver....sions/r0.8/resources
Github ML Showcase - https://github.com/showcases/machine-learning
Somatic Ruby post - http://www.somatic.io/blog/ten....sorflow-is-coming-to
TensorFlow MOOC - https://www.udacity.com/course..../deep-learning--ud73

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

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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

Playlist link: https://www.youtube.com/playli....st?list=PLQVvvaa0QuD

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Torsten Hoefler presents an overview of sparsity in deep learning. Check the markers for various parts of the talk.

arXiv: https://arxiv.org/abs/2102.00554

Chapters:
0:00 Introduction to deep learning
7:29 Introduction to hardware scaling and locality
11:25 Overview of model compression and optimization
12:47 Introduction to sparsification
18:17 Overparameterization, SGD dynamics, and Occam's hill and generalization
26:00 Sparse storage formats and representational overheads
31:10 Overview of sparsification techniques - model and ephemeral sparsity
35:13 Sparsification schedules - when to sparsify
41:36 Fully sparse training
47:53 Retraining example
50:35 How to sparsify - picking elements for removal
54:50 Data-free pruning - magnitude
56:52 Data-driven pruning - sensitivity, activity, and correlation
59:42 Training-aware pruning - Taylor expansions of the loss and regularization
1:09:19 Learnable gating functions (approximations)
1:12:49 Structured sparsification
1:15:34 Variational removal methods
1:18:08 Parameter budgets between layers and literature statistics
1:22:10 Re-growing elements in fully-sparse training
1:24:53 Ephemeral sparsity - activations, gradients, dynamic networks
1:33:07 Putting everything together - case studies with CNNs
1:36:39 Parameter efficiency and slack
1:41:03 Compute efficiency and sparse transformers
1:43:41 Acceleration for sparse deep learning
1:50:04 Lottery tickets and sparse subnetworks
1:53:37 Best practices for sparse deep learning
1:56:06 Open research questions and summary

Abstract: The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biological counterparts, sparse networks generalize just as well, if not better than, the original dense networks. Sparsity can reduce the memory footprint of regular networks to fit mobile devices, as well as shorten training time for ever growing networks. In this paper, we survey prior work on sparsity in deep learning and provide an extensive tutorial of sparsification for both inference and training. We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit sparsity in practice. Our work distills ideas from more than 300 research papers and provides guidance to practitioners who wish to utilize sparsity today, as well as to researchers whose goal is to push the frontier forward. We include the necessary background on mathematical methods in sparsification, describe phenomena such as early structure adaptation, the intricate relations between sparsity and the training process, and show techniques for achieving acceleration on real hardware. We also define a metric of pruned parameter efficiency that could serve as a baseline for comparison of different sparse networks. We close by speculating on how sparsity can improve future workloads and outline major open problems in the field.

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