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TensorFlow Tutorial #15 Style Transfer
 
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How to implement the Style Transfer algorithm in TensorFlow for combining the style and content of two images. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 29089 Hvass Laboratories
How to Do Style Transfer with Tensorflow (LIVE)
 
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We're going to learn about all the details of style transfer (especially the math) using just Tensorflow. The goal of this session is for you to understand the details behind how style+content loss is calculated and minimized. We'll also talk about future discoveries. Code for this video: https://github.com/llSourcell/How_to_do_style_transfer_in_tensorflow Learning resources: http://www.makeuseof.com/tag/create-neural-paintings-deepstyle-ubuntu/ https://blog.paperspace.com/art-with-neural-networks/ https://www.tensorflow.org/versions/r0.11/how_tos/ https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/ https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/ http://kawahara.ca/deep-dreams-and-a-neural-algorithm-of-artistic-style-slides-and-explanations/ http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style Please subscribe! And like. And comment. That's what keeps me goin And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 25649 Siraj Raval
How to Generate Art - Intro to Deep Learning #8
 
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We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well. Coding challenge for this video: https://github.com/llSourcell/How-to-Generate-Art-Demo Itai's winning code: https://github.com/etai83/lstm_stock_prediction Andreas' runner up code: https://github.com/AndysDeepAbstractions/How-to-Predict-Stock-Prices-Easily-Demo/blob/master/stockdemo.ipynb More learning resources: https://harishnarayanan.org/writing/artistic-style-transfer/ https://ml4a.github.io/ml4a/style_transfer/ http://genekogan.com/works/style-transfer/ https://arxiv.org/abs/1508.06576 https://jvns.ca/blog/2017/02/12/neural-style/ Style transfer apps: http://www.pikazoapp.com/ http://deepart.io/ https://artisto.my.com/ https://prisma-ai.com/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Song at the beginning is called Everyday by Carly Comando jurassic park inception visualization is from http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/ Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 65471 Siraj Raval
How to Make an Amazing Tensorflow Chatbot Easily
 
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We'll go over how chatbots have evolved over the years and how Deep Learning has made them way better. Then we'll build our own chatbot using the Tensorflow machine learning library in Python. The code & coding challenge for this video are here: https://github.com/llSourcell/tensorflow_chatbot Georgi's winning code for this week: https://github.com/petkofff/p_vs_np_challenge Mick's Runner up code for this week: https://github.com/mickvanhulst/travSalesman Join other Wizards on our Slack room: https://wizards.herokuapp.com Live sequence to sequence chatbot demo: http://neuralconvo.huggingface.co/ Some more useful resources on chatbots: http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/ http://venturebeat.com/2016/08/01/how-deep-reinforcement-learning-can-help-chatbots/ http://web.stanford.edu/class/cs124/lec/chatbot.pdf More resources on Tensorflow: http://lauragelston.ghost.io/speakeasy-pt2/ https://speakerdeck.com/inureyes/building-ai-chat-bot-using-python-3-and-tensorflow Please Subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 367247 Siraj Raval
TensorFlow Tutorial #06 CIFAR-10
 
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How to make a Convolutional Neural Network for the CIFAR-10 data-set. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 31209 Hvass Laboratories
Neural Network Voices
 
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5 days left to enroll, learn more and signup here - https://www.theschool.ai/courses/decentralized-applications Baidu has released some really impressive research that enables them to generate a voice in the style of anyone after having been trained on only a few examples. Few-shot generative learning is something i'm particularly interested in, and in this video I'll go over what their progress has looked like in this field over the past 2 years. We'll go over a web demo of audio generation, try and understand how DeepMind's WaveNet (similar) works, and then look at some Tensorflow code to get a deeper understanding of how this model plays out programmatically. Code for this video: https://github.com/llSourcell/Neural_Network_Voices Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://github.com/baidu-research/deep-voice https://thenextweb.com/artificial-intelligence/2018/02/26/baidus-ai-can-clone-your-voice-and-give-it-a-different-gender-or-accent/ http://research.baidu.com/deep-voice-3-2000-speaker-neural-text-speech/ http://research.baidu.com/neural-voice-cloning-samples/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 34712 Siraj Raval
Pramit Choudhary - Learn to be a painter using Neural Style Painting
 
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Description Vincent Van Gogh was noticeably one of the most influential artistic figures of the Western art. Won't it be great, if one is able to teach machines to paint in a similar manner to create visually appealing images. In this talk, we will learn how to paint images with the help of Convolutional Neural Network - VGG-19 using TensorFlow, SparkMagic and Livy to imitate renowned artists. Abstract Humans have continuously mastered the art of painting images that are visually appealing. They are able mix multiple different styles to produce new styles which instantly catches our attention. Generating such high quality images using algorithms have been less explored in the past. With the advancement in computer vision and object recognition coupled by maturity of Deep Learning frameworks, recently it has become more convenient to generate high quality emotional intuitive artistic images. In 2015, Leon A. Gatys et al, published paper "Image Style Transfer Using Convolutional Neural Network" explaining how to generate artistic images using neural representation to separate and combine random input images using a very deep Convolutional Neural Network - VGG-VD. In this talk, we take a fun dive into learning to be a painter by extracting relevant feature representation from high performing Neural Network using TensoFlow, SparkMagic and Livy in a scalable manner. Take away for the audience: 1. Develop basic understanding of Convolutional Neural Network 2. Realize benefits of using TensforFlow in building Deep Neural Networks 3. Learn how to use SparkMagic and Livy to build scalable solutions 4. Learn how to make machines paint like experts 5. Learn how to apply this style of painting to create poster thumbnails which might have wide variety of applications www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 343 PyData
Neural Networks and TensorFlow - 34 - Neural Style Transfer with VGG19 - 1
 
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In this series we're going to look into concepts of deep learning and neural networks with TensorFlow. In this lesson we begin exploring neural style transfer, a very interesting deep learning topic. Over the course of the next couple of lessons, we are going to teach a neural network how to paint, by inferring the style from one painting and drawing it onto another painting. Machine Learning App (Android): https://play.google.com/store/apps/details?id=com.cristivlad.machinelearning Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad The code: https://github.com/CristiVlad25/nnt-python/blob/master/Neural%20Networks%20and%20TensorFlow%20-%2034%20-%20Neural%20Style%20Transfer%20with%20VGG19%20-%201.ipynb Pretrained VGG19: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat Recommended readings: 1. Antonio Gulli and Amita Kapoor - TensorFlow Deep Learning Cookbook - https://www.amazon.com/gp/product/B0753KP6S4/ 2. Gatys et al. (2015) - A Neural Algorithm of Artistic Style - https://arxiv.org/pdf/1508.06576.pdf Images: 1. By Vincent van Gogh [Public domain] via Wikimedia Commons - https://en.wikipedia.org/wiki/File:VanGogh-starry_night_ballance1.jpg 2. By English: New York Sunday News [Public domain], via Wikimedia Commons - https://upload.wikimedia.org/wikipedia/commons/0/0a/Marilyn_Monroe_in_1952.jpg
Views: 255 Cristi Vlad
TensorFlow Tutorial #02 Convolutional Neural Network
 
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How to make a Convolutional Neural Network in TensorFlow for recognizing handwritten digits from the MNIST data-set. https://github.com/Hvass-Labs/TensorFlow-Tutorials The 2nd convolutional layer can be hard to understand because of the multiple input and output channels. Here is the math formula but it is probably even more confusing: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d
Views: 141996 Hvass Laboratories
TensorFlow Tutorial #11 Adversarial Examples
 
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How to fool a neural network into mis-classifying images by adding a little 'specialized' noise. Demonstrated on the Inception model. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 16184 Hvass Laboratories
Neural Networks and TensorFlow - 36 - Neural Style Transfer with VGG19 - 3
 
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In this series we're going to look into concepts of deep learning and neural networks with TensorFlow. In this lesson, we're defining the load_vgg_model() function that's going to take care of loading our model. This includes the weights and bias, the conv2d, and the average pooling. We're going with average pooling over max_pool as it's been shown to lead to better result in this scenario. Machine Learning App (Android): https://play.google.com/store/apps/details?id=com.cristivlad.machinelearning Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad The code: https://github.com/CristiVlad25/nnt-python/blob/master/Neural%20Networks%20and%20TensorFlow%20-%2036%20-%20Neural%20Style%20Transfer%20with%20VGG19%20-%203.ipynb Pretrained VGG19: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat Recommended readings: 1. Antonio Gulli and Amita Kapoor - TensorFlow Deep Learning Cookbook - https://www.amazon.com/gp/product/B0753KP6S4/ 2. Gatys et al. (2015) - A Neural Algorithm of Artistic Style - https://arxiv.org/pdf/1508.06576.pdf Images: 1. By Vincent van Gogh [Public domain] via Wikimedia Commons - https://en.wikipedia.org/wiki/File:VanGogh-starry_night_ballance1.jpg 2. By English: New York Sunday News [Public domain], via Wikimedia Commons - https://upload.wikimedia.org/wikipedia/commons/0/0a/Marilyn_Monroe_in_1952.jpg 3. Image courtesy of (and adapted from) Simonyan and Zisserman - https://arxiv.org/pdf/1409.1556.pdf
Views: 204 Cristi Vlad
How to Make a Tensorflow Neural Network (LIVE)
 
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In this live stream, we're going to use Tensorflow to build a convolutional neural network capable of classifying images. You'll need 'tensorflow' and the 'future' python libraries installed. The connection was laggy for the live stream and that won't happen again. 4:09-5:50 (The connection drops out) The code for this video is here: https://github.com/llSourcell/tensorflow_neural_net_live_demo/blob/master/README.md More learning resources: https://www.tensorflow.org/versions/r0.10/tutorials/mnist/beginners/index.html http://deeplearning.net/tutorial/gettingstarted.html http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/ https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow?log-in Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 39575 Siraj Raval
Pong Neural Network (LIVE)
 
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In this video we're going to build the popular game Pong from scratch in Python, then train a neural network to become an unbeatable 2nd player! We use Tensorflow to build our neural net and pygame to build our Pong game. The full, working code for this video is here: https://github.com/llSourcell/pong_neural_network_live Unlike my previous 2 live sessions where i did less than 60 lines of code each, I tried to do about 400 lines of code in this one. So I didn't have time to get to everything! I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Please share this video, like, comment and subscribe! And please support me on Patreon!: https://www.patreon.com/user?u=3191693 That's what keeps me going. I love you all. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 45703 Siraj Raval
고흐가 문재인 대통령을 그린다면? Neural Style Transfer - Python, Deep Learning
 
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인공지능이 예술작품을 그리는 딥러닝 모델인 Neural style을 소개해드립니다! 정말 신기하군요~ Source code(Github): https://github.com/anishathalye/neural-style Pretrained model: http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat Dependencies: - Python - Tensorflow - numpy - scipy - pillow Neural Style Transfer 따라하기(Blog): https://www.popit.kr/neural-style-transfer-%EB%94%B0%EB%9D%BC%ED%95%98%EA%B8%B0/ An AI That Can Mimic Any Artist(Blog): https://www.anishathalye.com/2015/12/19/an-ai-that-can-mimic-any-artist/ A Learned Representation for Artistic Style(Paper): https://arxiv.org/pdf/1610.07629.pdf
Neural Network Post Processing - Fast Neural Style Tranfer
 
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Post processing with fast neural style transfer in unity https://github.com/maajor/NeuralNetworkPostProcessing
Views: 221 YD Ma
Code a Neural Network from Scratch with pure Python - Part 1
 
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Part 1 of a tutorial where I show you how to code a neural network from scratch using pure Python code and no special machine learning libraries. In this part we will start writing our helper functions, namely sigmoid and feed forward functions. Link to code in GitHub: https://github.com/JamieMicro/PureNeuralNetwork
Views: 172 James Oliver
Recurrent Neural Network - The Math of Intelligence (Week 5)
 
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Recurrent neural networks let us learn from sequential data (time series, music, audio, video frames, etc ). We're going to build one from scratch in numpy (including backpropagation) to generate a sequence of words in the style of Franz Kafka. Code for this video: https://github.com/llSourcell/recurrent_neural_network Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://www.youtube.com/watch?v=hWgGJeAvLws https://www.youtube.com/watch?v=cdLUzrjnlr4 https://medium.freecodecamp.org/dive-into-deep-learning-with-these-23-online-courses-bf247d289cc0 http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ https://deeplearning4j.org/lstm.html http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 82018 Siraj Raval
Neural Voice Cloning
 
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In this video, we take a look at a paper released by Baidu on Neural Voice Cloning with a few samples. The idea is to “clone” an unseen speaker’s voice with only a few sound clips. If you like the video, hit that like button. Ring the bell to stay notified of my videos on Machine Learning, Deep Learning, Data Sciences and AI. main paper: https://arxiv.org/abs/1802.06006 Check out the audio demos: https://audiodemos.github.io/ MY EQUIPMENT (on a $350 budget) Camera (GoPro Hero 5 Black + 32 GB Memory + Kit): https://goo.gl/V4542j Microphone: https://goo.gl/BxBRcW Pop filter: https://goo.gl/oQTQ8W FOLLOW ME https://www.quora.com/profile/Ajay-Halthor
Views: 8145 CodeEmporium
Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka
 
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( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow. Below are the topics covered in this tutorial: 1. How a Computer Reads an Image? 2. Why can't we use Fully Connected Networks for Image Recognition? 3. What is Convolutional Neural Network? 4. How Convolutional Neural Networks Work? 5. Use-Case (dog and cat classifier) Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - 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. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 71191 edureka!
Neural Networks - YOLO Algorithm
 
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Convolutional Neural Networks About this course: This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization. Who is this class for: - Learners that took the first two courses of the specialization. The third course is recommended. - Anyone that already has a solid understanding of densely connected neural networks, and wants to learn convolutional neural networks or work with image data. Object detection Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection. Learning Objectives • Understand the challenges of Object Localization, Object Detection and Landmark Finding • Understand and implement non-max suppression • Understand and implement intersection over union • Understand how we label a dataset for an object detection application • Remember the vocabulary of object detection (landmark, anchor, bounding box, grid, ...) Subscribe at: https://www.coursera.org
Views: 5072 intrigano
Artistic style transfer for videos
 
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Style transfer for videos, as described in the paper "Artistic style transfer for videos" by Manuel Ruder, Alexey Dosovitskiy and Thomas Brox http://arxiv.org/abs/1604.08610 Another video with more examples and more technical comparisons: https://youtu.be/vQk_Sfl7kSc Code https://github.com/manuelruder/artistic-videos
Views: 180360 Computer Vision Freiburg
Generating Pythonic code with Neural Network - Unconventional Neural Networks p.2
 
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Hello and welcome to part 2 of our series of just poking around with neural networks. In the previous tutorial, we played with a generative model, and now have already set our sights and hopes on getting a neural network to write our Python code for us. Text tutorials and sample code: https://pythonprogramming.net/generating-python-playing-neural-network-tensorflow/ Chat with us on Discord: https://goo.gl/Q9euv3 Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 13711 sentdex
Deep Dream in TensorFlow - Learn Python for Data Science #5
 
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In this video, we replicate Google's Deep Dream code in 80 lines of Python using the Tensorflow machine learning library. Then we visualize it at the end. The challenge for this video is here: https://github.com/llSourcell/deep_dream_challenge Avhirup's winning stock prediction code: https://github.com/Avhirup/Stock-Market-Prediction-Challenge Victor's runner-up code: https://github.com/ciurana2016/predict_stock_py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More Deep Dream tutorials: http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/ https://github.com/awanninger/deepdream http://ryankennedy.io/running-the-deep-dream/ Generate Deep Dream's online: http://deepdreamgenerator.com/generator-style Still my favorite intro to neuroscience class: https://www.mcb80x.org/ Please subscribe! And share this video, like + comment. That's what keeps me going. Please support me on Patreon if you like my videos: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 97747 Siraj Raval
Neural Networks and TensorFlow - 35 - Neural Style Transfer with VGG19 - 2
 
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In this series we're going to look into concepts of deep learning and neural networks with TensorFlow. In this lesson, we're preprocessing some of the data that we're going to use in training the model for neural style transfer. This is a necessary step in most machine learning and deep learning projects. Machine Learning App (Android): https://play.google.com/store/apps/details?id=com.cristivlad.machinelearning Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad The code: https://github.com/CristiVlad25/nnt-python/blob/master/Neural%20Networks%20and%20TensorFlow%20-%2034%20-%20Neural%20Style%20Transfer%20with%20VGG19%20-%201.ipynb Pretrained VGG19: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat Recommended readings: 1. Antonio Gulli and Amita Kapoor - TensorFlow Deep Learning Cookbook - https://www.amazon.com/gp/product/B0753KP6S4/ 2. Gatys et al. (2015) - A Neural Algorithm of Artistic Style - https://arxiv.org/pdf/1508.06576.pdf Images: 1. By Vincent van Gogh [Public domain] via Wikimedia Commons - https://en.wikipedia.org/wiki/File:VanGogh-starry_night_ballance1.jpg 2. By English: New York Sunday News [Public domain], via Wikimedia Commons - https://upload.wikimedia.org/wikipedia/commons/0/0a/Marilyn_Monroe_in_1952.jpg
Views: 302 Cristi Vlad
MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
 
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I trained a recurrent neural network to play Mario Kart human-style. MariFlow Manual & Download: https://docs.google.com/document/d/1p4ZOtziLmhf0jPbZTTaFxSKdYqE91dYcTNqTVdd6es4/edit?usp=sharing Mushroom Cup: https://www.twitch.tv/videos/183296063 Flower Cup: https://www.twitch.tv/videos/183296268 Star Cup: https://www.twitch.tv/videos/183296400 SethBling Twitter: http://twitter.com/sethbling SethBling Twitch: http://twitch.tv/sethbling SethBling Facebook: http://facebook.com/sethbling SethBling Website: http://sethbling.com SethBling Shirts: http://sethbling.spreadshirt.com Suggest Ideas: http://reddit.com/r/SethBlingSuggestions Music at the end is Cipher by Kevin MacLeod
Views: 766915 SethBling
How to visualize neural network parameters and activity - Justin Shenk
 
23:28
Description Visualizing neural network parameters and activity using open source software such as Yosinski's Deep Convolutional Toolbox, Karpathy's RNNs, and TensorFlow's tools. Abstract Learn how to visualize anything the network sees using handy open source tools - and join the discussion of how to "open the black box" of deep learning. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1283 PyData
How to Do Sentiment Analysis - Intro to Deep Learning #3
 
09:21
In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. Please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 141021 Siraj Raval
Python client-server for neural style painting
 
07:05
Link to download files: https://www.dropbox.com/sh/l4dib25ncwhafuj/AACcuzygMzGtajyQUaybliHTa?dl=0
Views: 92 Svitlana Lesiv
How to Generate Images - Intro to Deep Learning #14
 
08:48
We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :) Code for this video: https://github.com/llSourcell/how_to_generate_images Mike's Winning Code: https://github.com/xkortex/how_to_win_slot_machines/blob/master/WallStBandits.ipynb SG's Runner up Code: https://github.com/esha-sg/Intro-DeepLearning-Siraj-Week13 Please subscribe! And like. And comment. That's what keeps me going. 2 things -The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5. -The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that. More Learning resources: https://jaan.io/what-is-variational-autoencoder-vae-tutorial/ http://kvfrans.com/variational-autoencoders-explained/ http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html http://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in.html http://blog.evjang.com/2016/11/tutorial-categorical-variational.html https://jmetzen.github.io/2015-11-27/vae.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 41946 Siraj Raval
Generative Adversarial Networks for Style Transfer (LIVE)
 
01:03:42
Generative Adversarial Nets are such a rich topic for exploration, we're going to build one that was released just 2 months ago called the "DiscoGAN" that lets us transfer the style between 2 datasets. And I'll be building this using Tensorflow. Code for this video: https://github.com/llSourcell/GANS-for-style-transfer Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://arxiv.org/abs/1703.05192 https://github.com/SKTBrain/DiscoGAN https://www.reddit.com/r/MachineLearning/comments/5zp0eu/r_170305192_learning_to_discover_crossdomain/ https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 52532 Siraj Raval
Convolutional Neural Networks with TensorFlow - Deep Learning with Neural Networks 13
 
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In this tutorial, we cover how to create a Convolutional Neural Network (CNN) model within TensorFlow, using our multilayer perceptron model: https://pythonprogramming.net/tensorflow-neural-network-session-machine-learning-tutorial/ Deep MNIST for experts: https://www.tensorflow.org/versions/r0.10/tutorials/mnist/pros/index.html https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 80601 sentdex
How to Predict Stock Prices Easily - Intro to Deep Learning #7
 
09:58
We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power. Coding challenge for this video: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo Vishal's winning code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier Jie's runner up code: https://github.com/jiexunsee/Simple-Inception-Transfer-Learning More Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://deeplearning.net/tutorial/lstm.html https://deeplearning4j.org/lstm.html https://www.tensorflow.org/tutorials/recurrent http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/ Please subscribe! And like. And comment. That's what keeps me going. Join other Wizards in our Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 music in the intro is chambermaid swing by parov stelar Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 480330 Siraj Raval
Generating Songs With Neural Networks (Neural Composer)
 
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I generate new music with Autoencoders and Principal Component Analysis. WATCH FIRST: https://www.youtube.com/watch?v=4VAkrUNLKSo Download: Standalone (Windows 64-bit): https://github.com/HackerPoet/Composer/blob/master/NeuralComposer.zip Source Code: https://github.com/HackerPoet/Composer Carykh's jazz generator: https://www.youtube.com/watch?v=nA3YOFUCn4U Music Credits: "Chill Tune" by Nicolai Heidlas
Views: 100218 CodeParade
Eben Olson: Neural networks with Theano and Lasagne
 
01:25:54
PyData NYC 2015 An introduction to neural networks using the Theano computational library and the Lasagne framework. After introducing the basics of Theano, we will learn how to create, train and apply convolutional and recurrent neural networks using Lasagne. Several applications will be explored including image classification, language modeling, image captioning and art style transfer. Lasagne, a lightweight framework built on Theano, makes it simple to define, train, and apply neural networks. The workshop will provide an introduction to these libraries, and a guided exploration of several interesting applications of convolutional and recurrent networks. Familiarity with Python and basic neural network terminology will be assumed (introductory materials will be available prior to the conference). An AWS instance with the necessary software and data will be provided for each participant. Syllabus: Theano basics Symbolic variables/tensors, expressions Functions, shared variables and updates Overview of Lasagne Layer classes and building a network Objectives, optimizers, and training Convolutional neural networks Image classification Fine-tuning a pretrained network Style transfer ("Neural Art") Recurrent neural networks Language model (text generation) CNN + RNN (image captioning) Extending Lasagne Defining custom Layers Q&A / Audience topics
Views: 10719 PyData
Leaf Recognition Using Convolutional Neural Network by Yuan Liu and Jianing Zhao
 
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Machine Learning 2017 final project: Leaf Recognition Using Convolutional Neural Network by Yuan Liu and Jianing Zhao
TensorFlow Tutorial #05 Ensemble Learning
 
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How to make an ensemble of multiple Neural Networks in TensorFlow. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 13478 Hvass Laboratories
Generative Adversarial Networks (LIVE)
 
01:04:41
We're going to build a GAN to generate some images using Tensorflow. This will help you grasp the architecture and intuition behind adversarial approaches to machine learning. We're building a Deep Convolutional GAN to generate MNIST digits. Code for this video: https://github.com/llSourcell/Generative_Adversarial_networks_LIVE/blob/master/EZGAN.ipynb Please Subscribe! And like. And comment. That's what keeps me going. More Learning resources: http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them http://www.cs.toronto.edu/~dtarlow/pos14/talks/goodfellow.pdf https://datawarrior.wordpress.com/2017/02/03/generative-adversarial-networks/ https://www.quora.com/What-are-Generative-Adversarial-Networks http://nuit-blanche.blogspot.com/2017/01/nips-2016-tutorial-generative.html http://www.paddlepaddle.org/develop/doc/tutorials/gan/index_en.html http://gkalliatakis.com/blog/delving-deep-into-gans Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 47207 Siraj Raval
"How to run Neural Nets on GPUs' by Melanie Warrick
 
37:36
This talk is just what the title says. I will demonstrate how to run a neural net on a GPU because neural nets are solving some interesting problems and GPUs are a good tool to use. Neural networks have regained popularity in the last decade plus because there are real world applications we are finally able to apply them to (e.g. Siri, self-driving​ cars, facial recognition). This is due to significant improvements in computational power and the amount of data that is available for building the models. However, neural nets still have a barrier to entry as a useful tool in companies because they can be computationally expensive to obtain value and implement. GPUs are popular processors in gaming and research due to their computational speed. Deep Neural Net's parallel structures (millions of identical nodes that perform the same operation on different data), are ideal for GPU's. Depending on the neural net, you can use a single server with GPUs vs. a CPU cluster and improve communication latency as well as reduces size and power consumption. Running an optimization method (training algorithm) like Stochastic Gradient Descent on a CPU vs. a GPU can be up to 40 times faster. This talk will briefly explain what neural nets are and why they're important, as well as give context about GPUs. Then I will walk through the code and actually launch a neural net on a GPU. I will cover key pitfalls you may hit and techniques to diagnose and troubleshoot. You will walk away understanding how to approach using GPUs on your own and have some resources to dive into for further understanding. Melanie Warrick SKYMIND @nyghtowl Deep Learning Engineer at Skymind. Previous experience included data science and engineering at Change.org and a comprehensive consulting career. I have a passion for working on machine learning problems at scale and AI.
Views: 7862 Strange Loop
How to learn Neural Networks
 
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Neural Networks are key to deep learning. In this short video I show you how to learn Neural Networks. Links from Video If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer 3 Blue 1 Brown https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi Neural Networks and Deep Learning http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons Matrix Calculus you need for Deep Learning http://explained.ai/matrix-calculus/index.html A beginner’s guide to the mathematics of Neural Networks http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.3556&rep=rep1&type=pdf Mathematics for Machine Learning http://bit.ly/2ndt89b Python Neural Network Code http://iamtrask.github.io/2015/07/12/basic-python-network/ How to build a Neural Network https://stevenmiller888.github.io/mind-how-to-build-a-neural-network/ Here are some other python learning resources Udacity Introduction to Python - http://bit.ly/2GuhXzU Programming Foundations With Python - http://bit.ly/2GrjZB4 Python Websites Github - http://bit.ly/2GoUuQN Python 3 Module of the Week - http://bit.ly/2IpZIle Introduction to Python - Quantitative Economics - http://bit.ly/2wQ5fw2 Books Automate the Boring Stuff with Python - https://amzn.to/2wRjGjd Learn Python the Hard Way - https://amzn.to/2wPYWIz Python Programming - https://amzn.to/2rPYANo Python Crash Course - https://amzn.to/2IRXWZD YouTube Channels Sirar Raval - http://bit.ly/2rOYWDX MIT Python Course (Introduction to Computer Science and Programming) - http://bit.ly/2k4TbOb My Python Course - http://bit.ly/2rWq9nB
Views: 1029 Python Programmer
Style Transfer in Keras (Part 1)
 
10:54
This is part 1 in a tutorial that walks you through the neural style transfer algorithm in Keras. If you have any feedback or questions, let me know! If you find some cool addition/fix/change to my code, let me know! Part 2: https://www.youtube.com/watch?v=8D5x9dRQ5cM Here is a link to the full code: https://github.com/hunter-heidenreich/ML-Open-Source-Implementations/tree/master/Style-Transfer Here is a link to the python notebook: https://github.com/hunter-heidenreich/ML-Open-Source-Implementations/blob/master/Style-Transfer/Style%20Transfer.ipynb This tutorial, but on my website: http://hunterheidenreich.com/ML/style_transfer_tutorial.html Website: https://hunterheidenreich.com Twitter: https://twitter.com/hunter_heiden
Views: 1268 Hunter Heidenreich
Neural Networks Demystified [Part 1: Data and Architecture]
 
03:08
Neural Networks Demystified Part 1: Data and Architecture @stephencwelch Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part 6: Training Part 7: Overfitting, Testing, and Regularization
Views: 594221 Welch Labs
Generating Pokemon with a Generative Adversarial Network
 
25:29
Gotta train 'em all! Let's generate some new pokemon using the power of Generative Adversarial Networks. This is a newer deep learning technique invented by a researcher & friend of mine named Ian Goodfellow. Yann LeCunn called it the coolest idea in the past 2 decades. I'll explain how it works, some more recent improvements, then we'll go through the code. Code for this video: https://github.com/llSourcell/Pokemon_GAN Please Subscribe! And like. And comment. That's what keeps me going. Want more inspiration & education? Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners https://www.analyticsvidhya.com/blog/2017/06/introductory-generative-adversarial-networks-gans/ https://github.com/uclaacmai/Generative-Adversarial-Network-Tutorial http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428 https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 82214 Siraj Raval
Keras Explained
 
09:20
Whats the best way to get started with deep learning? Keras! It's a high level deep learning library that makes it really easy to write deep neural network models of all sorts. It can use several popular backends like Tensorflow and CNTK. I'll show you how it works and explain how it compares to the other deep learning libraries. Code for this video: https://github.com/llSourcell/keras_explained Alberto's Winning Code: https://github.com/alberduris/Reinforcement_Learning_AI_Video_Games/tree/master/Week%206 Sven's Runner-up Code: https://github.com/EmbersArc/PPO Please Subscribe! And like. And comment. That's what keeps me going. Connect with me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval More learning resource: https://elitedatascience.com/keras-tutorial-deep-learning-in-python https://keras.io/ https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ https://github.com/fchollet/keras-resources https://www.datacamp.com/community/tutorials/deep-learning-python https://dashee87.github.io/data%20science/deep%20learning/python/another-keras-tutorial-for-neural-network-beginners/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/sirajraval Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 137693 Siraj Raval
3.2: Introduction to TensorFlow and Keras with Anaconda Python (Module 3, Part 2)
 
25:01
Overview of deep learning tools. How to construct a neural network with TensorFlow and Keras for simple classification and regression. This video is part of a course that is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, at Washington University in St. Louis Please subscribe and comment! Follow me: YouTube: https://www.youtube.com/user/HeatonResearch Twitter: https://twitter.com/jeffheaton GitHub: https://github.com/jeffheaton More links: Complete course: https://sites.wustl.edu/jeffheaton/t81-558/ Complete playlist: https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN
Views: 5168 Jeff Heaton
Generate Music in TensorFlow
 
05:48
In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. Congrats to Rohan Verma (Winner) and Chih-Cheng Liang (runner-up) for their classifiers for scientists. The challenge for this video is to generate a happy/upbeat song using the RBM Script. The code for this video is here: https://github.com/llSourcell/Music_Generator_Demo I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The WaveNet blogpost with audio samples: https://deepmind.com/blog/wavenet-generative-model-raw-audio/ More on RBMs: http://deeplearning4j.org/restrictedboltzmannmachine.html Another write up on music generation with Neural Networks: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/ Interesting Machine Music Generation Project by Google: https://magenta.tensorflow.org/welcome-to-magenta TensorFlow course on Udacity: https://www.udacity.com/course/deep-learning--ud730 Rohan's Classifier (Winner): https://github.com/rhnvrm/galaxy-image-classifier-tensorflow Chih-Cheng's Classifier (Runner-up): https://github.com/ChihChengLiang/tensorflow-night-heron-classifier Please subscribe, like, and comment! You guys are the reason I do this. Thanks so much for watching my videos! If you enjoy my videos, I'd appreciate your support on Patreon :) https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 123395 Siraj Raval
My Chess AI Plays Itself!
 
09:28
After about 5-6 failed attempts of writing a chess engine over the past few years, I finally got one working. I wouldn't call this a "success" by any means since the quality of the moves isn't great. However, it's not a lost cause and I can definitely improve in the future to increase its accuracy. This is the first game it ever produced and I was happy to see how sharp it got. Part of me expected pseudo-random moves leading into a simplified, aimless endgame. White managed to deliver checkmate after 33 moves. The program was written in python and took about 2 weeks of effort on and off. Half of the time was spent just writing the rules of chess. The result was a command line interface, so I used the lichess.org analysis board to display the game and compare it to the Stockfish suggestions (a reliable chess AI). If you have any questions about the specifics of the engine, feel free to leave a comment. It's a pretty simple combination of static, positional analysis combined with dynamic branching to deliver "minimax" style evaluations. Let me know in the comments if you want to see more videos like this in the future. After some further improvements to the engine, I plan to play against it to see how I do! I won't consider this engine to be successful until it beats me. Hopefully I'm a better programmer than chess player XD
Views: 19546 Dan Cubing
Genetic Algorithm in Artificial Intelligence - The Math of Intelligence (Week 9)
 
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Evolutionary/genetic algorithms are somewhat of a mystery to many in the machine learning discipline. You don't see papers regularly published using them but they are a really fascinating subfield and in this video, we're going to use a genetic algorithm to improve invaders in a space invaders game! Code for this video: https://github.com/llSourcell/Evolutionary_Space_Invaders Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.ai-junkie.com/ga/intro/gat1.html http://www.tutorialspoint.com/genetic_algorithms/ http://www.theprojectspot.com/tutorial-post/creating-a-genetic-algorithm-for-beginners/3 http://www.obitko.com/tutorials/genetic-algorithms/ http://www-cs-students.stanford.edu/~jl/Essays/ga.html http://www.alanzucconi.com/2016/04/06/evolutionary-coputation-1/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 27339 Siraj Raval
Neural Networks Demystified [Part 3: Gradient Descent]
 
06:56
Neural Networks Demystified @stephencwelch Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified Link to Yann's Talk: http://videolectures.net/eml07_lecun_wia/ In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part 6: Training Part 7: Overfitting, Testing, and Regularization
Views: 321425 Welch Labs
Graph neural networks: Variations and applications
 
18:07
Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data. See more at https://www.microsoft.com/en-us/research/video/graph-neural-networks-variations-applications/
Views: 16727 Microsoft Research
Computer evolves to generate baroque music!
 
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I put the word "evolve" in there because you guys like "evolution" videos, but this computer is actually learning with gradient descent! All music in this video is either by Bach, Mozart, or Computery. GizmoDude8128 wins a prize for figuring out that 100101 in base 2 is 37 in base 10 the fastest! (Question inspired by fixylol) Andrej Karpathy's blog post on RNNs: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Views: 1968249 carykh