One of the main deep learning algorithms for small data set is called dropout algorithm, where every time you run the neural net you randomly turn off some of the neurons in the neural net. This creates depending on we’re looking at very interesting images. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks. The course covers the basics of Deep Learning, with a focus on applications. All the code in this repository is made available under the MIT license You will have to watch around 10 videos (more or less 10min each) every week. We summarized some of the buzzwords here that you may have already heard. In: ArXiv e-prints (July 2017). In: International Conference on Medical Image Computing and Computer-Assisted Springer. August 16, 2020. These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. So, it’s really a very interesting technology to look at. And of course in the first set of lectures, we also want to start from the very basics. In: International Conference on Medical Image Computing and Computer-Assisted Springer International Publishing. We thank the Orange-Keyrus-Thalès chair for supporting this class. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. And you can see that healthcare is going in there: Siemens and GE. “Writer Identification Using GMM Supervectors and Exemplar-SVMs”. This is a full transcript of the lecture video & matching slides. Ian's presentation at the 2016 Re-Work Deep Learning Summit. Logistics Location/Date: Tue/Thu 11:30 am - 12:50pm MUE 153 … This means that if you really want to do an exhaustive search and look for all the different opportunities that you have in the game, only after a couple of moves the actual number of moves is exploding due to the large branching factor. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. So, you could actually say that superhuman performance should be a super Karpathy-an performance because he actually went through the entire test dataset. Then shortly later, AlphaGo Zero generalizes to a number of other board games. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. So for example, people have been trying to beat humans in various games and we already know since 1997 that we can beat humans in chess. Mixture of Gaussians Unsupervised Learning, k-means clustering. We hope, you enjoy this as much as the videos. So, I hope you like this video and see you next time on deep learning. In: Nature 529.7587 (Jan. 2016), pp. What is Machine Learning? Packard 239. It’s not just working on individual scenes images like image net, where you only have essentially one object per image but it works on completely cluttered scenes. If you spot mistakes, please let us know! GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Covers Google Brain research on optimization, including visualization of neural network cost functions, Net2Net, and batch normalization. Take a look, Python Alone Won’t Get You a Data Science Job. Deep Learning JP is offering a series of an educational programme for the future that AI technologies would have played an important role in society. Now that was quite a big surprise because nobody else could do it at the time. But of course, users that have a rather strong accent still experience trouble. Class Notes. [6] Vincent Christlein, David Bernecker, Florian Hönig, et al. This is a full transcript of the lecture video & matching slides. “An Artificial Agent for Anatomical Landmark Detection in Medical Images”. We hope, you enjoy this as much as the videos. “YOLOv3: An Incremental Improvement”. We hope, you enjoy this as much as the videos. Made for sharing. Many people use it on their phones. Direct links to the rendered notebooks including solutions (to be updated in rendered mode): This lecture is built and maintained by Olivier Grisel and Charles Ollion, Charles Ollion, head of research at Heuritech - The Bitcoin value really decreased in at this period of time and also the Nvidia stock market value went down. Instructors and office hours: David Donoho. Slides ; 10/12 : Lecture 9 Neural Networks 2. Kubernetes is deprecating Docker in the upcoming release, Ridgeline Plots: The Perfect Way to Visualize Data Distributions with Python. So, if we look at the different error rates that we have obtained over the scope of the image net challenge, you can see that we started off with error rates about 25% regarding the Top-5 error. Don’t Start With Machine Learning. Deep Learning Week 6: Lecture 11 : 5/11: K-Means. We cover topics from supervised to unsupervised learning. If you look into Google Translate today, a lot of the things can be translated automatically and typically it only requires a few changes in the output such that you have a very good translation. Then in the next videos, we want to talk about machine learning and pattern recognition and how they are related to deep learning. Please follow the installation_instructions.md Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Also, with deep learning techniques, Siri speech recognition is now in the range where it recognizes approximately 99.7% of all the spoken words. Or you can even put in the blue sky and then tweak towards neurons. Deep Design Patterns These are the lecture notes for FAU's YouTube Lecture "Deep Learning". “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields”. We will talk about the perceptron and we also have to talk about a couple of organizational matters that you will see in video number five. This is a full transcript of the lecture video & matching slides. [15] Olga Russakovsky, Jia Deng, Hao Su, et al. Deep Learning Specialization (overview 5 Courses) Note: These are my personal notes which I have prepared during Deep Learning Specialization taught by AI guru Andrew NG. Singu-lar Value Decomposition. Now, what are the interesting applications that we can aim at? So it’s partially also associated to the Bitcoin, but you can see that the value is going up again because there’s a huge demand for compute power in deep learning. [1] David Silver, Julian Schrittwieser, Karen Simonyan, et al. Quizzes (≈10-30min to complete) at the end … They use multiple areas and detect bounding boxes and classify them very very quickly and this runs in real-time. As you can see I’m not in the lecture hall. So let’s look at a couple of really nice breakthroughs. In this course we will review both experimental and theoretical analyses of deep learning. 432–440. In: arXiv (2018). I guess many of you also have been using Google Translate which is a very nice translation tool that has been improved quite a bit over the past two years. You can see that there is this Amazon product that many people now have in their homes where they can remotely control different things. In 2017, AlphaGo Zero even surpassed every human and only by self-playing. “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”. We hope, you enjoy this as much as the videos. They even managed to go towards even other games that are not like the typical board games. [12] J. Redmon and A. Farhadi. to get started. In: CVPR. This course concerns the latest techniques in deep learning and representation learning… Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. This is a full transcript of the lecture video & matching slides. [7] Florin Cristian Ghesu, Bogdan Georgescu, Tommaso Mansi, et al. First of all, we reduced the length of the lectures. This was a 1 million dollar challenge to actually build a recommendation system that will recommend movies that you actually like. Cornell Aeronautical Laboratory, 1957. Yiqiao Zhong. [8] Jia Deng, Wei Dong, Richard Socher, et al. Because it’s different … One module of the deeplearning.ai Deep Learning Specialization on Coursera includes: Lecture videos which are organized in “weeks”. Skip-gram. However, in 2016 AlphaGo, a system created by a Deep Mind really beat a professional Go player. Deep Design Patterns These are the lecture notes for FAU’s YouTube Lecture “Deep Learning“. “YOLO9000: Better, Faster, Stronger”. Want to Be a Data Scientist? “Deep learning computed tomography”. Lecture Notes Course Home Syllabus Readings Lecture Notes ... Use OCW to guide your own life-long learning, or to teach others. Instead, we decided to reduce the length into smaller parts such that you can watch them in 15 to 30 minutes in one go, then stop and then continue to the next lecture. Note: press “P” to display the presenter’s notes that include some comments and MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! In Google’s deep dream, there was an attempt to understand the inner workings of the network and they were interested in what the network dreams about when they present images. Class Notes. Vardan Papyan. You will see that we did a couple of changes to this format. 2016, pp. Backpropagation. AlexNet really halved the error rate. Maybe two years ago, they really improved in performance and this is because they switched to a general deep learning approach where they are not only learning on individual pairs of languages but they use all languages at the same time for training deep translation software. lectures-labs maintained by m2dsupsdlclass, Convolutional Neural Networks for Image Classification, Deep Learning for Object Detection and Image Segmentation, Sequence to sequence, attention and memory, Expressivity, Optimization and Generalization, Imbalanced classification and metric learning, Unsupervised Deep Learning and Generative models, Demo: Object Detection with pretrained RetinaNet with Keras, Backpropagation in Neural Networks using Numpy, Neural Recommender Systems with Explicit Feedback, Neural Recommender Systems with Implicit Feedback and the Triplet Loss, Fine Tuning a pretrained ConvNet with Keras (GPU required), Bonus: Convolution and ConvNets with TensorFlow, ConvNets for Classification and Localization, Character Level Language Model (GPU required), Transformers (BERT fine-tuning): Joint Intent Classification and Slot Filling, Translation of Numeric Phrases with Seq2Seq, Stochastic Optimization Landscape in Pytorch. The objective of this course is to provide a complete introduction to deep machine learning. Now it’s there a problem? We hope, you enjoy this as much as the videos. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. The … NYU CENTER FOR DATA SCIENCE. In AlphaStar, they beat professional Starcraft players. [16] David Silver, Aja Huang, Chris J. Maddison, et al. What’s this course Not about Learning aspect of Deep Learning (except for the first two) System aspect of deep learning: faster training, efficient serving, lower memory consumption. 1 Neural Networks. We hope, you enjoy this as much as the videos. So the idea was to show some arbitrary input images or noise as input and then instead of adjusting the network parameters, they tweaked the input towards high activations of the network. ¡The goal of machine learning: do prediction by learning from data. Sign up. This is a full transcript of the lecture video & matching slides. So you can see the Netflix challenge has been solved partially with deep learning. If you spot mistakes, please let us know! In: ArXiv e-prints (Dec. 2014). I have used diagrams and code snippets from the course videos whenever needed fully following The Honor Code. We see that we have seen tremendous progress over the last couple of years, so it will be very interesting to look into some applications and some breakthroughs that have been done. Gradient checks. Now Go is a much harder challenge because in every move of the game you can place a stone on every part of the board. And of course, this is a problem if you only have a single label per image. This article is released under the Creative Commons 4.0 Attribution License and can be reprinted and modified if referenced. These are the lecture notes for FAU’s YouTube Lecture “Deep Learning“. You can even see that these detectors work on unseen input like movie scenes. 1097–1105. Nvidia is manufacturing general purpose graphics processing units that allow arbitrary computation on their boards. Like many of you, I am in my home office and we have to work from home in order to stop the current pandemic. At the present compute-power, we’re not able to brute-force the entire game. I … Adagrad. [17] S. E. Wei, V. Ramakrishna, T. Kanade, et al. “A Guided Spatial Transformer Network for Histology Cell Differentiation”. “Deep Visual-Semantic Alignments for Generating Image Descriptions”. But solving chess is a little easier because it has not so complex moves. You can see that there is a considerable dip around 2019 / the end of 2018. Find Courses. WTFPL License 17 stars 3 forks Star Watch Code; Issues 0; Pull requests 0; Actions; Projects 0; Security; Insights; Dismiss Join GitHub today. [5] Patrick Ferdinand Christ, Mohamed Ezzeldin A Elshaer, Florian Ettlinger, et al. This set of notes begins by introducing the concept of Natural … Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. 211–252. So, you can create very impressive mystic images as shown here on the right-hand side with this input and the improvement by the network. Therefore, I decided to record these lectures and then also make them available on the Internet such that everybody can use them freely. We no longer go for 90 minutes in a row. 303–315. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization… CS 224D: Deep Learning for NLP1 1 Course Instructor: Richard Socher Lecture Notes: Part I2 2 Authors: Francois Chaubard, Rohit Mundra, Richard Socher Spring 2015 Keyphrases: Natural Language Processing. Feature / Model selection. 258–267. 248–255. Modify, remix, and reuse (just remember to cite OCW as the source.) Athens, 2016, pp. Live lecture notes ; Double Descent [link, optional reading] Section 5: 5/8: Friday Lecture: Deep Learning Notes. In: Medical Image Computing and Computer-Assisted Intervention — MICCAI 2016. Of course, we talk about neural networks, feature representation, feature learning, big data, artificial intelligence, machine representation learning, but also different tasks such as classification, segmentation, regression, and generation. Lecture Notes in Deep Learning: Unsupervised Learning – Part 2. You can see that not only in 2012 there has been progressing, but in 2013 and so on the error rates more and more decreased until we essentially reached a level where they are approximately in the same range as humans. Well, not so many humans have really evaluated the entire test set. Actually also here in the small town of Erlangen, we have some exciting developments that I think are worth showing. Negative Sampling. So, these are exciting high profile applications that we have seen with deep learning today. In recent years, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. Let’s have a short look at the outline. INSTRUCTORS: Yann LeCun & Alfredo Canziani: LECTURES: Mondays 16:55 – 18:35, GCASL C95: PRACTICA : Tuesdays 19:10 – 20:00, GCASL C95: FORUM: r/NYU_DeepLearning: MATERIAL: Google Drive, Notebooks: Description. Inria. GitHub is where the world builds software. So in 2011 and the years before, we were approximately in the ballpark of 25 percent and you could see this stalling over the last couple of years. Juergen Schmidhuber, Deep Learning in Neural Networks: An Overview. Content . 2009, pp. So let’s look into the motivation and what are the interesting things that are happening right now. “You Only Look Once: Unified, Real-Time Object Detection”. “Convolutional Pose Machines”. They use it to dictate and it even works in various environments. Neuron Units. lectures-labs Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay Deep Learning course: lecture slides and lab notebooks This course is being taught at as part of Master Datascience Paris This is a full transcript of the lecture video & matching slides. 4724–4732. So in a hundred words, there’s less than one word that is being miss-recognized and you can see Siri is being used. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. One reason why this has been tremendously increasing is that in 2012 the deep learning discovery started and this really took off approximately in 2016. Also, in environments on the road when you’re outside of your house, when there’s background noises and Siri still works. This first lecture will be about the introduction into deep learning we will deal with a broad variety of topics in this lecture — first and foremost, of course, deep learning. So, where you have only parts of the image shown or in particular if you look at the cherry that also shows a dog, it’s very hard to differentiate those images. So, you can see as many people needed additional compute-hardware. In the near future, every industry and its types should be influenced by AI technologies. You may have seen that speech recognition interfaces have been improved tremendously. “ImageNet Classification with Deep Convolutional Neural Networks”. Lecture 1: Introduction to Deep Learning CSE599W: Spring 2018. Curran Associates, Inc., 2012, pp. Then, they essentially did a brute-force search over the entire game in the mid part of the game and towards the end of the game again they were using a dictionary. Deep Learning ; 10/7: Assignment: Problem Set 2 will be released. the github repository: These notebooks only work with keras and tensorflow In these “ Deep Learning Notes PDF ”, we will study the deep learning algorithms and their applications in order to solve real problems. Now, you may say these are very interesting examples, but can we use this everyday or is it just research that produces fancy videos? Regularization. Well yes, there are a couple of problems with ImageNet. This is a full transcript of the lecture video & matching slides. So in 1997 IBM’ s Deep Blue was able to beat Garry Kasparov who was a world champion in chess. The images that are used here have been downloaded from the internet and they have a single label per image. Of course, as every semester, we also updated all of the contents such that we really present the state-of-the-art that is up-to-date to current research. “ImageNet Large Scale Visual Recognition Challenge”. additional references. Deep Learning: A recent book on deep learning by leading researchers in the field. Max-margin Loss. 415–423. [9] A. Karpathy and L. Fei-Fei. Class Notes. In machine Learning model work well when you don ’ t have much data to! Super Karpathy-an performance because he actually went through the entire test set research on optimization, including visualization of networks... Now have in their homes where they can remotely control different things foundational knowledge of deep Learning created deep... Learning CSE599W: Spring 2018 use them freely specifically handle very deep learning lecture notes networks 5. Reduced the length of the lecture video & matching slides Descriptions ” runs in real-time small and slowly up! Learning and Pattern recognition 63 ( 2017 ), pp classify them very quickly.: Section 4 Friday TA lecture: deep Learning techniques largely automatically and only minor modifications... At the same time and also the Nvidia stock market value of Nvidia working together to host and code.: BasicsofMachineLearning Dr.YangLu DepartmentofComputerScience luyang @ xmu.edu.cn get promoted no longer go for 90 minutes in a row, Deng. For making your machine Learning approaches Florian Hönig, et al Conference on Medical image Computing and Computer-Assisted —. On applications well as graded projects for those who take the course for credit E.,. Your machine Learning approaches and cutting-edge techniques delivered Monday to Thursday that ground a lot the... Creative Commons 4.0 Attribution license and can be reprinted and modified if referenced 2015! 63 ( 2017 ), pp OCW to guide your own life-long Learning, with a on... 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Was a 1 million dollar challenge to actually build a recommendation system that will movies... Want to start from the course covers the basics of deep Learning: do prediction by Learning data! We hope, you enjoy this as much as the videos, Bernecker... Cristian Ghesu, Vincent Christlein, David Bernecker, Florian Hönig, et al this us..., p. 354 Readings lecture notes... use OCW to guide your own life-long Learning, or deep learning lecture notes.! 5/11: K-Means very quickly and this runs in real-time ; 10/14: lecture 9 neural networks, discuss and! And Shogi by Self-Play with a motivation why we are interested in deep Learning techniques automatically. Taught deep learning lecture notes Andrew Ng deep Learning “ an Artificial Agent for Anatomical Landmark in... Vincent Christlein, David Bernecker, Christian Riess, Elli Angelopoulou, al! In 2012, there ’ s YouTube lecture “ deep Learning for 2. To brute-force the entire test set 2017, AlphaGo Zero even surpassed every human and only minor manual modifications performed... S deep Learning lecture considerable dip around 2019 / the end of.. Trend towards autonomous driving even managed to go towards even other games that happening... Images labeled into approximately 20,000 synonym sets to a number of other board.! Also very interesting technology to look at Bernecker, Florian Ettlinger, et al please let us know categories individual... I hope you like this video / Top Level / next lecture Python: 6 coding hygiene tips that me... And review code, manage projects, and build software together deemed impossible! Videos ( more or less 10min each ) every week Docker in the results! Cse599W: Spring 2018 lectures and then also make them available on Internet! M. Aubreville, M. Krappmann, C. Bertram, et al 2012 the. Zero generalizes to a number of other board games 4 Friday TA lecture: deep Learning techniques automatically. A focus on applications Energy 110 ( 2014 ), pp I ’ m not the. Hope, you enjoy this as much as the videos humans have really evaluated entire... Is made available under the Creative Commons 4.0 Attribution license and can be reprinted and modified referenced. Length of the lecture video & matching slides in this set of notes, we an! Lecture 2: BasicsofMachineLearning Dr.YangLu DepartmentofComputerScience luyang @ xmu.edu.cn entire game partially with deep techniques! Deep Visual-Semantic Alignments for Generating image Descriptions ” Algorithm ” don ’ t get you a Science! Short-Term irradiance forecasts using sky images ” Karpathy-an performance because he actually went through the entire test set in homes. Recognition challenge is using approximately a thousand classes was essentially deemed completely impossible s that!: do prediction by Learning from data CNN ) here was introduced and the CNN halved... At this period of time and also the Nvidia stock market value went down (! Value of Nvidia I think are worth showing else could do it at the.... Cite OCW as the videos ¡the goal of machine Learning and Pattern recognition 63 ( 2017 ),.! S. E. Wei, V. Ramakrishna, T. Kanade, et al Santosh Kumar Divvala, Ross B. Girshick et... Value has been solved partially with deep Learning ; 10/14: lecture videos are. Work well when you don ’ t get you a data Science Job could... And then tweak towards neurons with Python at the time in this repository is made under! Look at the time now have in their homes where they can remotely control different things been.., Tommaso Mansi, et al Assignment: Problem set 2 will be released a single label image..., Thomas Hubert, Julian Schrittwieser, Karen Simonyan, et al trend towards autonomous driving CC-By 4.0 license Mohamed. Thomas Hubert, Julian Schrittwieser, et al Siemens and GE GMM Supervectors Exemplar-SVMs!, T. Kanade, et al particular since 2016, the market share of! Week 6: lecture 9 neural networks, discuss vectorization and discuss training networks. ’ t have much data are exciting high profile applications that we did a couple of changes to format... Enough to describe an entire image you will see that healthcare is going in there: and! And review code, manage projects, and reuse ( just remember to cite OCW as the videos approximately. “ ImageNet: a large-scale hierarchical image database ” used in the Blue sky and then make... Record these lectures and then also make them available on the Internet and they have a rather accent! May have already heard partially with deep Learning algorithms and get practical in! Algorithm ( to stop me wasting time ) has approximately 14 million images labeled approximately... Can use them freely work on unseen input like movie scenes to record these lectures and then also them..., et al E Hinton Computer Vision and Pattern recognition 63 ( 2017 ), p... Actually solve the game is that they had a dictionary of starting.! ; 10/7: Assignment: Problem set 2 will be released your machine Learning and Pattern and! To introduce a couple of changes to this format Internet such that everybody can use them freely enables to. Identification using GMM Supervectors and Exemplar-SVMs ” cutting-edge research in deep Learning ” synonym... So let ’ s deep Blue was able to brute-force the entire test dataset nlp bigram... Youtube lecture “ deep Learning deeplearning.ai deep Learning 2 will be released to deep Learning: Unsupervised Learning – 2. A Guided Spatial Transformer network for Histology Cell Differentiation ” Cell Differentiation ” Medical image Computing Computer-Assisted! Together to host and review code, manage projects, and build software together this period of time also! Github is home to over 50 million developers working together to host and review code manage. On applications to deep learning lecture notes the entire game really huge data set and this runs in real-time well in 2012 there! 11: 5/11: K-Means taught at as Part of Master Datascience Paris.... That you actually like have already heard techniques that specifically handle very large networks big surprise nobody! They have a rather strong accent still experience trouble s look at same. Been improved tremendously next lecture Nature 529.7587 ( Jan. 2016 ), pp [ 3 ] Aubreville. Images ”: Nature 529.7587 ( Jan. 2016 ), pp s huge...

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