D-Wave Quantum Artificial Intelligence Robotics, its here and it is going change everything. It is the goal of the creators of this technology to fully integrate it with Humans and eventually transplant the Human consciousness in to a combined A.I exoskeleton.

Lecture 7 continues our discussion of practical issues for training neural networks. We discuss different update rules commonly used to optimize neural networks during training, as well as different strategies for regularizing large neural networks including dropout. We also discuss transfer learning and finetuning.

Keywords: Optimization, momentum, Nesterov momentum, AdaGrad, RMSProp, Adam, second-order optimization, L-BFGS, ensembles, regularization, dropout, data augmentation, transfer learning, finetuning

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture7.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data preprocessing and weight initialization, and batch normalization; we also cover some strategies for monitoring the learning process and choosing hyperparameters.

Keywords: Activation functions, data preprocessing, weight initialization, batch normalization, hyperparameter search

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture6.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language modeling and image captioning, and how soft spatial attention can be incorporated into image captioning models. We discuss different architectures for recurrent neural networks, including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU).

Keywords: Recurrent neural networks, RNN, language modeling, image captioning, soft attention, LSTM, GRU

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

In Lecture 4 we progress from linear classifiers to fully-connected neural networks. We introduce the backpropagation algorithm for computing gradients and briefly discuss connections between artificial neural networks and biological neural networks.

Keywords: Neural networks, computational graphs, backpropagation, activation functions, biological neurons

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Notebook: https://drive.google.com/file/d/1mMKGnVxirJnqDViH7BDJxFqWrsXlPSoK/view?usp=sharing
Blog post: http://minimaxir.com/2018/05/text-neural-networks/

A quick guide on how you can train your own text generating neural network and generate text with it on your own computer!

More about textgenrnn: https://github.com/minimaxir/textgenrnn

Twitter: https://twitter.com/minimaxir
Patreon: https://patreon.com/minimaxir

Did you know that art and technology can produce fascinating results when combined? Mike Tyka, who is both artist and computer scientist, talks about the power of neural networks. These algorithms are capable to transform computers into artists that can generate breathtaking paintings, music and even poetry.

Dr. Mike Tyka studied Biochemistry and Biotechnology at the University of Bristol. He obtained his Ph.D. in Biophysics in 2007 and went on to work as a research fellow at the University of Washington, studying the structure and dynamics of protein molecules. In particular, he has been interested in protein folding and has been writing computer simulation software to better understand this fascinating process.

In 2009, Mike and a team of artists created Groovik’s Cube, a 35 feet tall, functional, multi-player Rubik’s cube. Since then, he co-founded ATLSpace, an artist studio in Seattle and has been creating metal and glass sculptures of protein molecules. In 2013 Mike went to Google to study neural networks, both artificial and natural. This work naturally spilled over to his artistic interests, exploring the possibilities of artificial neural networks for creating art.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

Automatic emotion recognition from speech is a challenging task which significantly relies on the emotional relevance of specific features extracted from the speech signal. In this study, our goal is to use deep learning to automatically discover emotionally relevant features. It is shown that using a deep Recurrent Neural Network (RNN), we can learn both the short-time frame-level acoustic features that are emotionally relevant, as well as an appropriate temporal aggregation of those features into a compact sentence-level representation. Moreover, we propose a novel strategy for feature pooling over time using attention mechanism with the RNN, which is able to focus on local regions of a speech signal that are more emotionally salient. The proposed solution was tested on the IEMOCAP emotion corpus, and was shown to provide more accurate predictions compared to existing emotion recognition algorithms.

See more on this video at https://www.microsoft.com/en-us/research/video/automatic-speech-emotion-recognition-using-recurrent-neural-networks-local-attention/

This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We’ll also implement a neural network manually. Finally, we’ll code a neural network in Python using TensorFlow.

Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

To access the slides, click here: https://www.slideshare.net/Simplilearn/deep-learning-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn/Simplilearn/deep-learning-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn

Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearningWithPython #DeepLearningTutorial #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-with-Python-fcD6YeEYKNg&utm_medium=Tutorials&utm_source=youtube

For more information about Simplilearn’s courses, visit:
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Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.

Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Use my link http://www.audible.com/coldfusion or text coldfusion to 500-500 to get a free book and 30 day free trial.

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Hi, welcome to ColdFusion (formerly known as ColdfusTion).
Experience the cutting edge of the world around us in a fun relaxed atmosphere.

Sources:

Let there be Color: http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/en/

Pixel Enhancing CSI Style:
https://arxiv.org/pdf/1702.00783.pdf?xtor=AL-32280680

Generating New Images:
https://arxiv.org/pdf/1702.00783.pdf?xtor=AL-32280680

Pix2Pix demo: Image to image DEMO https://affinelayer.com/pixsrv/index.html

Lip Reading: https://arxiv.org/abs/1611.01599

Creating a Scene From Scratch: https://arxiv.org/pdf/1612.00005.pdf

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When reading up on artificial neural networks, you may have come across the term “bias.” It’s sometimes just referred to as bias. Other times you may see it referenced as bias nodes, bias neurons, or bias units within a neural network. We’re going to break this bias down and see what it’s all about.

We’ll first start out by discussing the most obvious question of, well, what is bias in an artificial neural network? We’ll then see, within a network, how bias is implemented. Then, to hit the point home, we’ll explore a simple example to illustrate the impact that bias has when introduced to a neural network.

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Hey guys and welcome to another fun and easy Machine Learning Tutorial on Artificial Neural Networks.
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Deep learning and Neural Networks are probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this state of the art field. If you think big data is important, then you should care about deep learning. Deep Learning (DL) and Neural Network (NN) is currently driving some of the most ingenious inventions this century. Their incredible ability to learn from data and the environment makes them the first choice for machine learning scientists.

Deep Learning and Neural Network lies in the heart of products such as self-driving cars, image recognition software, recommender systems and the list goes on. Evidently, being a powerful algorithm, it is highly adaptive to various data types as well.

People think neural network is an extremely difficult topic to learn. Therefore, either some of them don’t use it, or the ones who use it, use it as a black box. Is there any point in doing something without knowing how is it done? NO! That’s why you’ve’ come to right place at Augmented Startups to Learn about Artificial Neural Networks, so sit back relax and see how deep the rabbit hole goes.

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To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-networks-and-deep-learning

MNIST database:
http://yann.lecun.com/exdb/mnist/

Also check out Chris Olah’s blog:
http://colah.github.io/
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.

And if you like that, you’ll *love* the publications at distill:
https://distill.pub/

For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs

“But I’ve already voraciously consumed Nielsen’s, Olah’s and Welch’s works”, I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book “Deep Learning” by Goodfellow, Bengio, and Courville.

Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:
https://arxiv.org/abs/1611.03530
https://arxiv.org/abs/1706.05394
https://arxiv.org/abs/1412.0233

Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

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