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# deep learning slides

Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.. “Over the next few years, start-ups and the usual big tech suspects will use deep learning to create new products and services … What is Deep Learning? Obtain a sample from (or the mode statistic of) the true posterior $$p(y, z \mid x) \propto p(y|x, z) p(z)$$, We define some joint model $$p(y, \theta | x) = p(y | x, \theta) p(\theta)$$, We obtain observations $$\mathcal{D} = \{ (x_1, y_1), ..., (x_N, y_N) \}$$, We would like to infer possible values of $$\theta$$ given  observed data $$\mathcal{D}$$ $$p(\theta \mid \mathcal{D}) = \frac{p(\mathcal{D} | \theta) p(\theta)}{\int p(\mathcal{D}|\theta) p(\theta) d\theta}$$, We will be approximating true posterior distribution with an approximate one, Need a distance between distributions to measure how good the approximation is $$\text{KL}(q(x) || p(x)) = \mathbb{E}_{q(x)} \log \frac{q(x)}{p(x)} \quad\quad \textbf{Kullback-Leibler divergence}$$, Not an actual distance, but $$\text{KL}(q(x) || p(x)) = 0$$ iff $$q(x) = p(x)$$ for all $$x$$ and is strictly positive otherwise, Will be minimizing $$\text{KL}(q(\theta) || p(\theta | \mathcal{D}))$$ over $$q$$, We'll take $$q(\theta)$$ from some tractable parametric family, for example Gaussian $$q(\theta | \Lambda) = \mathcal{N}(\theta \mid \mu(\Lambda), \Sigma(\Lambda))$$, Then we reformulate the objective s.t. The Deep Learning Handbook is a project in progress to help study the Deep Learning book by Goodfellow et al.. Goodfellow's masterpiece is a vibrant and precious resource to introduce the booming topic of deep learning. Olivier Grisel, software engineer at What if we want to tune dropout rates $$p$$? with some fixed probability $$p$$ it's 0 and with probability $$1-p$$ it's some learnable value $$\Lambda_i$$, Then for some prior $$p(\theta)$$ our optimization objective is $$\mathbb{E}_{q(\theta|\Lambda)} \sum_{n=1}^N \log p(y_n | x_n, \theta) \to \max_{\Lambda}$$ where the KL term is missing due to the model choice, No need to take special care about differentiating through samples, Turns out, these are bayesian approximate inference procedures. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. we don't need the exact true posterior $$\text{KL}(q(\theta | \Lambda) || p(\theta | \mathcal{D})) = \log p(\mathcal{D}) - \mathbb{E}_{q(\theta | \Lambda)} \log \frac{p(\mathcal{D}, \theta)}{q(\theta | \Lambda)}$$, Hence we seek parameters $$\Lambda_*$$ maximizing the following objective (the ELBO) $$\Lambda_* = \text{argmax}_\Lambda \left[ \mathbb{E}_{q(\theta | \Lambda)} \log \frac{p(\mathcal{D}, \theta)}{q(\theta|\Lambda)} = \mathbb{E}_{q(\theta|\Lambda)} \log p(\mathcal{D}|\theta) - \text{KL}(q(\theta|\Lambda)||p(\theta)) \right]$$, We can't compute this quantity analytically either, but can sample from $$q$$ to get Monte Carlo estimates of the approximate posterior predictive distribution: $$q(y \mid x, \mathcal{D}) \approx \hat{q}(y|x, \mathcal{D}) = \frac{1}{M} \sum_{m=1}^M p(y \mid x, \theta^m), \quad\quad \theta^m \sim q(\theta \mid \Lambda_*)$$, Recall the objective for variational inference $$\mathcal{L}(\Lambda_*) = \mathbb{E}_{q(\theta | \Lambda)} \log \frac{p(\mathcal{D}, \theta)}{q(\theta|\Lambda)} \to \max_{\Lambda}$$, We'll be using well-known optimization method, We need (stochastic) gradient $$\hat{g}$$ of $$\mathcal{L}(\Lambda)$$ s.t. Different types of learning (supervised, unsupervised, reinforcement) 2. additional references. All the code in this repository is made available under the MIT license Deep Learning An MIT Press book in preparation Ian Goodfellow, Yoshua Bengio and Aaron Courville. to get started. We will be giving a two day short course on Designing Efficient Deep Learning Systems at MIT in Cambridge, MA on July 20-21, 2020. Deep learning algorithms are similar to how nervous system structured where each neuron connected each other and passing information. Cognitive modeling 5.3 (1988): 1. Deep Learning Handbook. Generator network and inference network essentially give us autoencoder, Inference network encodes observations into latent code, Generator network decodes latent code into observations, Can infer high-level abstract features of existing objects, Uses neural network to amortize inference, Bayesian methods are useful when we have low data-to-parameters ratio, Impose useful priors on Neural Networks helping discover solutions of special form, Provide Neural Networks with uncertainty estimates (uncovered), Neural Networks help us make more efficient Bayesian inference. 6.S191: Introduction to Deep Learning Yoshua Bengio gave a recent presentation on “Deep Learning of Representation” and Generative Stochastic Networks (GSNs) at MSR and AAAI 2013. Can we drop unnecessary computations for easy inputs? In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. 2020 Feb 28. doi: 10.1002/hep.31207. Note: press “P” to display the presenter’s notes that include some comments and The Deep Learning case! The slides are published under the terms of the CC-By 4.0 • LeCun, Yann, et al. "Backpropagation applied to handwritten zip code recognition." Seriously though, its just formal language, not much of the actual math is involved, We don't need no Bayes, we already learned a lot without it. Computationally stained slides could help automate the time-consuming process of slide staining, but Shah said the ability to de-stain and preserve images for future use is the real advantage of the deep learning techniques. Table of contents. Get Free Introduction To Deep Learning Slides now and use Introduction To Deep Learning Slides immediately to get % off or $off or free shipping 11/11/2019. deep learning is driving significant advancements across industries, enterprises, and our everyday lives. More info on deep learning and CNNs: [deep learning … Deep Learning (DL) algorithms are the central focus of modern machine learning systems. Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. We will help you become good at Deep Learning. We assume the two-phase data-generating process: First, we decide upon high-level abstract features of the datum $$z \sim p(z)$$, Then, we unpack these features using Neural Networks into an actual observable $$x$$ using the (learnable) generator $$f_\theta$$, This leads to the following model $$p(x, z) = p(x|z) p(z)$$ where $$p(x|z) = p(z) \prod_{d=1}^D p(x_d | f_\theta(z))$$ $$p(z) = \mathcal{N}(z | 0, I)$$ and $$f_\theta$$ is some neural network, We can sample new $$x$$ by passing samples $$z$$ through the generator once we learn it, Would like to maximize log-marginal density of observed variables $$\log p(x)$$, Intractable integral $$\log p(x) = \log \int p(x|z) p(z) dz$$, Introduce approximate posterior $$q(z|x)$$: $$q(z|x) = \mathcal{N}(z|\mu_\Lambda(x), \Sigma_\Lambda(x))$$, Where $$\mu, \Sigma$$ are generated using auxiliary inference network from the observation $$x$$, Invoking the ELBO we obtain the following objective $$\tfrac{1}{N} \sum_{n=1}^N \left[ \mathbb{E}_{q(z_n|x_n)} \log p(x_n | z_n) - \text{KL}(q(z_n|x_n)||p(z_n)) \right] \to \max_\Lambda$$. Download Deep Learning PowerPoint templates (ppt) and Google Slides themes to create awesome presentations. Convolutional neural networks (CNNs) use a data-driven approach to automatically learn feature representations for images, achieving super-human performance on benchmark image classification datasets such as ImageNet. Book Exercises External Links Lectures. 2014 Lecture 2 … The Course “Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. Dimensions of a learning system (different types of feedback, representation, use of knowledge) 3. Machine Learning: An Overview: The slides presentintroduction to machine learningalong with some of the following: 1. license. Juergen Schmidhuber, Deep Learning in Neural Networks: An Overview. Nature 2015 Description. In this study, we used two deep-learning algorithms based … Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides Hepatology. Lecture slides Basic information about deep learning Cheat sheet – stuff that everyone needs to know Useful links Grading Plan your visit Visit previous iteration of Stats385 (2017) This page was generated by … UC Berkeley has done a lot of remarkable work on deep learning, including the famous Caffe — Deep Leaning Framework. Saclay. ​Jeez, how is that related to this slide? Training the model is just one part of shipping a Deep Learning project. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Please follow the installation_instructions.md the github repository: These notebooks only work with keras and tensorflow The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. We currently offer slides for only some chapters. Unsupervised Deep Learning Tutorial – Part 1 Alex Graves NeurIPS, 3 December 2018 ... Slide: Irina Higgins, Loïc Matthey. Deep learning models work in layers and a typical model atleast have three layers. “We’re not really just solving a staining problem, we’re also solving a save-the-tissue problem,” he said. Bayesian methods can Impose useful priors on Neural Networks helping discover solutions of special form; Provide better predictions; Provide Neural Networks with uncertainty estimates (uncovered) Neural Networks help us make more efficient Bayesian inference; Uses a lot of math; Active area of research @article{zhang2019pathologist, title={Pathologist-level interpretable whole-slide cancer diagnosis with deep learning}, author={Zhang, Zizhao and Chen, Pingjun and McGough, Mason and Xing, Fuyong and Wang, Chunbao and Bui, Marilyn and Xie, Yuanpu and Sapkota, Manish and Cui, Lei and Dhillon, Jasreman and others}, journal={Nature Machine Intelligence}, volume={1}, number={5}, … Slides of the talk can be accessed from this link. We thank the Orange-Keyrus-Thalès chair for supporting this class. "Learning representations by back-propagating errors." In other words, It mirrors the functioning of our brains. $$\mathbb{E} \hat{g} = \nabla_\Lambda \mathcal{L}(\Lambda)$$, Problem: We can't just take $$\hat{g} = \nabla_\Lambda \log \frac{p(\mathcal{D}, \theta)}{q(\theta | \Lambda)}$$ as the samples themselves depend on $$\Lambda$$ through $$q(\theta|\Lambda)$$, Remember the expectation is just an integral, and apply the log-derivative trick $$\nabla_\Lambda q(\theta | \Lambda) = q(\theta | \Lambda) \nabla_\Lambda \log q(\theta|\Lambda)$$ $$\nabla_\Lambda \mathcal{L}(\Lambda) = \int q(\theta|\Lambda) \log \frac{p(\mathcal{D}, \theta)}{q(\theta | \Lambda)} \nabla_\Lambda \log q(\theta | \Lambda) d\theta = \mathbb{E}_{q(\theta|\Lambda)} \log \frac{p(\mathcal{D}, \theta)}{q(\theta|\Lambda)} \nabla \log q(\theta | \Lambda)$$, Though general, this gradient estimator has too much variance in practice, We assume the data is generated using some (partially known) classifier $$\pi_{\theta}$$: $$p(y \mid x, \theta) = \text{Cat}(y | \pi_\theta(x)) \quad\quad \theta \sim p(\theta)$$, True posterior is intractable $$p(\theta \mid \mathcal{D}) \propto p(\theta) \prod_{n=1}^N p(y_n \mid x_n, \pi_\theta)$$, Approximate it using $$q(\theta | \Lambda)$$: $$\Lambda_* = \text{argmax} \; \mathbb{E}_{q(\theta | \Lambda)} \left[\sum_{n=1}^N \log p(y_n | x_n, \theta) - \text{KL}(q(\theta | \Lambda) || p(\theta))\right]$$, Essentially, instead of learning a single neural network that would solve the problem, we, $$p(\theta)$$ encodes our preferences on which networks we'd like to see, Let $$q(\theta_i | \Lambda)$$ be s.t. As data volumes keep growing, it has become customary to train large neural networks with hundreds of millions of parameters to maintain enough capacity to memorize these volumes and obtain state-of-the-art accuracy. CNNs are the current state-of-the-art architecture for medical image analysis. Artificial Intelligence Machine Learning Deep Learning Deep Learning by Y. LeCun et al. Then $$H_l(x) = [z \le l] F_l(x) + x$$, Thus we have $$p(y|x,z) = \text{Categorical}(y \mid \pi(x, z))$$ where $$\pi(x, z)$$ is a residual network with $$z$$ that controls when to stop processing the $$x$$, We chose the prior on $$z$$ s.t. The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. July 24th, 2013 | Tags: representation learning , slides , talks , yoshua bengio | Category: anouncements, conference, news | One comment - (Comments are closed) The course is Berkeley’s current offering of deep learning. Deep Learning is Large Neural Networks. 8. I sometimes blog about different cutting-edge-like topics: Importance Weighted Hierarchical Variational Inference Teaser, Importance Weighted Hierarchical Variational Inference -- Extended, Importance Weighted Hierarchical Variational Inference, Law of the Unconscious Statistician $$\mathbb{E} f(x) = \int f(x) p(x) dx$$, ​If $$X$$ and $$Y$$ are independent, $$\mathbb{V}[\alpha X + \beta Y] = \alpha^2 \mathbb{V} X + \beta^2 \mathbb{V} Y$$, $$\text{Cov}(X, Y) = \mathbb{E} [X Y] - \mathbb{E} X \mathbb{E} Y$$ –, $$\mathbb{V} [\alpha X + \beta Y] = \alpha^2 \mathbb{V}[X] + \beta^2 \mathbb{V}[Y] + 2 \alpha \beta \text{Cov}(X, Y)$$, $$\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad \quad p(x) = \frac{1}{\sqrt{\text{det}(2 \pi \Sigma)}} \exp \left( -\tfrac{1}{2} (x-\mu)^T \Sigma^{-1} (x-\mu) \right)$$,$\$ p(x_N = x \mid x_{