The document summarizes various applications of deep learning that have progressed rapidly since the book was written. It discusses applications in areas such as computer vision, natural language processing, speech recognition and synthesis, robotics, healthcare, and autonomous vehicles. Specific examples mentioned include neural machine translation models from Google, AlphaGo from DeepMind, and autonomous vehicle research from Waymo. It notes that many applications now rely on techniques such as attention mechanisms, generative models, reinforcement learning, and model compression that have developed significantly in recent years.
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
A basic introduction to Generative Adversarial Networks, what they are, how they work, and why study them. This presentation shows what is their contribution to Machine Learning field and for which reason they have been considered one of the major breakthroughts in Machine Learning field.
The document discusses learning to rank, which involves using machine learning techniques to generate ordered rankings based on training data. It describes ranking as a supervised learning problem that deals with ordinal labels rather than categorical or real-valued labels. The document outlines different approaches to learning to rank, including pointwise, pairwise, and listwise methods. It also provides examples of applications like search engine rankings and personalized ad recommendations.
http://imatge-upc.github.io/vqa-2016-cvprw/
This thesis studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62\% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations.
Deep generative models can generate synthetic images, speech, text and other data types. There are three popular types: autoregressive models which generate data step-by-step; variational autoencoders which learn the distribution of latent variables to generate data; and generative adversarial networks which train a generator and discriminator in an adversarial game to generate high quality samples. Generative models have applications in image generation, translation between domains, and simulation.
This document provides an overview of generative adversarial networks (GANs). It explains that GANs use two neural networks, a generator and discriminator, that compete against each other during training. The generator tries to generate fake samples that look real, while the discriminator tries to distinguish real from fake samples. When trained, the generator is able to generate new samples similar to the training data distribution. The document discusses applications of GANs to image generation, editing, and super resolution, as well as recent work on speech generation. It notes challenges in GAN training and evaluating generated samples.
Capitalico / Chart Pattern Matching in Financial Trading Using RNNAlpaca
The document discusses using recurrent neural networks, specifically long short-term memory networks (LSTMs), to perform pattern matching on financial time series data to identify chart patterns. It proposes an approach using multi-dimensional inputs of price and indicator time series without hand-crafted features. The model would be trained on examples collected by experts to output a confidence level for pattern matches. Experiments showed LSTM models can reasonably fit training and testing data. Future work includes improving the base model, incorporating reinforcement learning, and generating trading signals.
The document discusses machine learning models learning when to give up or reject classifying examples. It proposes three main approaches: 1) Using calibrated confidence estimates to account for natural confusability in the training data, 2) Handling input noise with a secondary confidence predictor, and 3) Making classifiers aware of what examples are unknown based on the training data. The focus is on classification problems, where rejecting allows transferring to a human for assistance. The goal is to minimize rejections while maintaining a fixed error rate.
EuroSciPy 2019 - GANs: Theory and ApplicationsEmanuele Ghelfi
EuroSciPy 2019: https://pretalx.com/euroscipy-2019/talk/Q79NND/
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues.
We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0.
The workshop is composed of:
- Theoretical introduction
- GANs from Scratch in TensorFlow 2.0
- High-performance input data pipeline with TensorFlow Datasets
- Introduction to the AshPy API
- Implementing, training, and visualizing DCGAN using AshPy
- Serving TF2 Models with Google Cloud Functions
The materials of the workshop will be openly provided via GitHub (https://github.com/zurutech/gans-from-theory-to-production).
IMAGE GENERATION WITH GANS-BASED TECHNIQUES: A SURVEYijcsit
In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.
Generative Adversarial Networks and Their ApplicationsArtifacia
This is the presentation from our AI Meet Jan 2017 on GANs and its applications.
You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
This document summarizes generative adversarial networks (GANs) and their applications. It begins by introducing GANs and how they work by having a generator and discriminator play an adversarial game. It then discusses several variants of GANs including DCGAN, LSGAN, conditional GAN, and others. It provides examples of applications such as image-to-image translation, text-to-image synthesis, image generation, and more. It concludes by discussing major GAN variants and potential future applications like helping children learn to draw.
Word embeddings are common for NLP tasks, but embeddings can also be used to learn relations among categorical data. Deep learning can be useful also for structured data, and entity embeddings is one reason why it makes sense. These are slides from a seminar held in Sbanken.
Generative Adversarial Networks (GANs) are a type of deep learning model used for unsupervised machine learning tasks like image generation. GANs work by having two neural networks, a generator and discriminator, compete against each other. The generator creates synthetic images and the discriminator tries to distinguish real images from fake ones. This allows the generator to improve over time at creating more realistic images that can fool the discriminator. The document discusses the intuition behind GANs, provides a PyTorch implementation example, and describes variants like DCGAN, LSGAN, and semi-supervised GANs.
Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
Professor Steve Roberts, Machine learning research group and Oxford-Man Institute + Alan Turing Institute. Steve gave this talk on the 24th January at the London Bayes Nets meetup.
A short and naive introduction to using network in prediction modelstuxette
The document provides an introduction to using network information in prediction models. It discusses representing a network as a graph with a Laplacian matrix. The Laplacian captures properties like random walks on the graph and heat diffusion. Eigenvectors of the Laplacian related to small eigenvalues are strongly tied to graph structure. The document discusses using the Laplacian in prediction models by working in the feature space defined by the Laplacian eigenvectors or directly regularizing a linear model with the Laplacian. This introduces network information and encourages similar contributions from connected nodes. The approaches are applied to problems like predicting phenotypes from gene expression using a known gene network.
This document discusses generative adversarial networks (GANs) and the LAPGAN model. It explains that GANs use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate fake images to fool the discriminator, while the discriminator learns to distinguish real from fake images. LAPGAN improves upon GANs by using a Laplacian pyramid to decompose images into multiple scales, with separate generator and discriminator networks for each scale. This allows LAPGAN to generate sharper images by focusing on edges and conditional information at each scale.
A Short Introduction to Generative Adversarial NetworksJong Wook Kim
Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks compete against each other. One network generates new data instances, while the other evaluates them for authenticity. This adversarial process allows the generating network to produce highly realistic samples matching the training data distribution. The document discusses the GAN framework, various algorithm variants like WGAN and BEGAN, training tricks, applications to image generation and translation tasks, and reasons why GANs are a promising area of research.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
The document discusses numerical concerns for implementing deep learning algorithms. It covers topics like:
1) Algorithms specified with real numbers but implemented with finite bits can lead to rounding errors and instability.
2) Gradient descent, curvature, and saddle points which are important for iterative optimization.
3) Conditioning problems can cause gradient descent to be slow and fail to exploit curvature. Learning rates must account for curvature.
Chap 8. Optimization for training deep modelsYoung-Geun Choi
연구실 내부 세미나 자료. Goodfellow et al. (2016), Deep Learning, MIT Press의 Chapter 8을 요약/발췌하였습니다. 깊은 신경망(deep neural network) 모형 훈련시 목적함수 최적화 방법으로 흔히 사용되는 방법들을 소개합니다.
A Decomposition Technique For Solving Integer Programming ProblemsCarrie Romero
This document summarizes a research paper that develops an algorithm for solving large-scale integer programming problems using Dantzig-Wolfe decomposition and column generation. The algorithm is tested on capital budgeting and scheduling problems. Numerical examples are provided to demonstrate the method. Key aspects of the algorithm include generating columns iteratively to solve a pricing problem at each iteration, and using computer software to code the algorithm and output results.
Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
This document discusses natural language inference and summarizes the key points as follows:
1. The document describes the problem of natural language inference, which involves classifying the relationship between a premise and hypothesis sentence as entailment, contradiction, or neutral. This is an important problem in natural language processing.
2. The SNLI dataset is introduced as a collection of half a million natural language inference problems used to train and evaluate models.
3. Several approaches for solving the problem are discussed, including using word embeddings, LSTMs, CNNs, and traditional bag-of-words models. Results show LSTMs and CNNs achieve the best performance.
This document provides an overview of deep feedforward networks. It begins with an example of using a network to solve the XOR problem. It then discusses gradient-based learning and backpropagation. Hidden units with rectified linear activations are commonly used. Deeper networks can more efficiently represent functions and generalize better than shallow networks. Architecture design considerations include width, depth, and number of hidden layers. Backpropagation efficiently computes gradients using the chain rule and dynamic programming.
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
This talk by Lucas Theis from Twitter/Magic Pony on "Compressing Images with Neural Networks" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Beginner's Guide to Diffusion Models..pptxIshaq Khan
1. Diffusion models are a new powerful family of deep generative models inspired by physics that destroy structure in data using a diffusion process and train a model to reverse the process.
2. Denoising Diffusion Probabilistic Models (DDPMs) were introduced to generate high-quality samples using a U-Net to predict noise levels and recover data from pure noise.
3. Improved DDPMs achieved competitive log-likelihoods while maintaining high sample quality through modifications like an improved noise schedule and optimization techniques.
EXTENDING OUTPUT ATTENTIONS IN RECURRENT NEURAL NETWORKS FOR DIALOG GENERATIONijaia
In natural language processing, attention mechanism in neural networks are widely utilized. In this paper, the research team explore a new mechanism of extending output attention in recurrent neural networks for dialog systems. The new attention method was compared with the current method in generating dialog sentence using a real dataset. Our architecture exhibits several attractive properties such as better handle long sequences and, it could generate more reasonable replies in many cases.
The document outlines an agenda for a Tensorflow basics workshop. It includes an opening speech on Tesla AI, an introduction to key concepts like neural networks and machine learning workflows. The bulk of the workshop involves coding sessions where participants will build a classification model in Tensorflow and get help from instructors. It concludes with information on continuing self-directed learning through online resources and a preview of an upcoming computer vision lesson.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
building intelligent systems with large scale deep learningmustafa sarac
The document discusses the work of the Google Brain team in conducting long-term research on machine learning and building systems like TensorFlow to make ML models more widely available. It outlines the team's goals of making machines intelligent to improve people's lives through research areas like computer vision, healthcare, robotics and language understanding. The team aims to build general tools for ML and collaborate within Google and with others to apply their research at large scale.
Seed RL is a scalable and efficient reinforcement learning agent that was designed to efficiently utilize cloud and TPU resources. It implements popular distributed RL algorithms like IMPALA and R2D2 in a way that optimizes for cost and performance. Seed RL achieves faster training times and reduced experiment costs of up to 80% compared to other methods by using a simple centralized inference architecture and optimized communication layer. The implementation and experiments are open-sourced to allow for reproducibility and testing of new ideas.
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
Sudeep Das presented on recommender systems and advances in deep learning approaches. Matrix factorization is still the foundational method for collaborative filtering, but deep learning models are now augmenting these approaches. Deep neural networks can learn hierarchical representations of users and items from raw data like images, text, and sequences of user actions. Models like wide and deep networks combine the strengths of memorization and generalization. Sequence models like recurrent neural networks have also been applied to sessions for next item recommendation.
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples.
GANS
Ian Goodfellow, OpenAI Research Scientist
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-google-keynote
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks.
In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
Distributed Models Over Distributed Data with MLflow, Pyspark, and PandasDatabricks
Does more data always improve ML models? Is it better to use distributed ML instead of single node ML?
In this talk I will show that while more data often improves DL models in high variance problem spaces (with semi or unstructured data) such as NLP, image, video more data does not significantly improve high bias problem spaces where traditional ML is more appropriate. Additionally, even in the deep learning domain, single node models can still outperform distributed models via transfer learning.
Data scientists have pain points running many models in parallel automating the experimental set up. Getting others (especially analysts) within an organization to use their models Databricks solves these problems using pandas udfs, ml runtime and MLflow.
This document provides an overview of structured probabilistic models for deep learning. It discusses using graphs to describe model structure, sampling from graphical models, and the advantages of structured modeling over unstructured approaches. Key topics covered include directed and undirected models, separation properties, converting between graph representations, learning model structure, and using latent variables. The document serves as lecture slides outlining concepts in structured probabilistic modeling.
This document provides an overview of sequence modeling using recurrent and recursive neural networks. It begins with an introduction to classical dynamical systems and unfolding computational graphs for recurrent networks. It then discusses different types of recurrent networks, including those with recurrence through the hidden and output states. Bidirectional and encoder-decoder sequence-to-sequence architectures are also covered. Finally, the document discusses issues like exploding gradients and presents solutions like LSTMs and gradient clipping. Recursive networks and networks with explicit memory components are also introduced.
1. Unsupervised pretraining of deep neural networks (DNNs) on multiple datasets usually provides no benefit and sometimes harms performance compared to DNNs trained on individual datasets.
2. Representation learning aims to learn representations of input data that make a task easier by separating explanatory factors of variations. For example, mixture models can discover separate classes in data and distributed representations can divide the input space into uniquely identifiable regions.
3. Generative adversarial networks (GANs) can learn vector spaces that support semantic operations like representing a woman with glasses by combining vectors for concepts of gender and wearing glasses.
1. Autoencoders are neural networks that are trained to reconstruct their input. They have an internal representation or "code" layer that compresses the input into a lower-dimensional form.
2. There are different types of regularized autoencoders that can learn meaningful representations, including sparse, denoising, contractive, and stochastic autoencoders. Denoising autoencoders in particular are trained to reconstruct clean inputs from corrupted versions, which can learn the manifold of the data.
3. Contractive autoencoders explicitly regularize the hidden layer activations to resist small changes to the input, encouraging a smooth hidden representation that captures the manifold structure of the data.
This document discusses adversarial machine learning attacks. It begins by reminding the reader about evasion attacks, which aim to find model weaknesses at test time, and poisoning attacks, which compromise the training process. It then discusses evasion attacks in more detail, including the use of adversarial examples and real-world attacks. The document outlines different types of evasion attacks and formulations. It also discusses adversarial training as a potential mitigation technique, as well as universal adversarial perturbations. The document presents experimental results showing that perlin noise attacks can outperform other attacks and that adversarial training is not fully effective. It concludes by emphasizing the need to understand vulnerabilities in machine learning systems and develop effective defenses and testing methodologies.
This document discusses adversarial machine learning and data poisoning attacks. It notes that machine learning systems can be compromised through evasion and poisoning attacks. Poisoning attacks aim to degrade the performance of a machine learning system by compromising the training data. Optimal poisoning attacks can be modeled as bi-level optimization problems and efficiently computed using back-gradient optimization, allowing poisoning points to be generated at scale. Poisoning attacks may be transferable across different machine learning algorithms. The document explores different types of poisoning attacks and defenses like anomaly detection and label sanitization, noting that defenses must account for detectability constraints to defend against more sophisticated attacks.
Monte Carlo methods can be used to estimate sums and integrals by approximating them as expectations under a probability distribution. Samples are drawn from the distribution and the average of the function evaluated at each sample is calculated. This provides an unbiased estimate with variance that decreases as more samples are taken. Importance sampling improves upon this by drawing samples from a different distribution that puts more weight on important areas, which can reduce variance. Markov chain Monte Carlo methods like Gibbs sampling are used to draw samples from distributions that cannot be directly sampled, like those represented by undirected graphs, by iteratively updating variables conditioned on others.
This document discusses confronting the partition function in probabilistic models. It explains that many probabilistic models are defined by an unnormalized probability distribution that is normalized by a partition function. The partition function is difficult to compute, making the gradient of the log-likelihood challenging. Basic learning algorithms for undirected models involve generating model samples to estimate the negative phase of the gradient. Estimating the partition function is also important for evaluating trained models.
This document discusses approximate inference techniques for probabilistic models. It begins with an introduction to variational inference and how it can be used to approximate intractable distributions. It then discusses applying variational inference to mixture of Gaussian models and exponential family distributions. Finally, it briefly introduces expectation propagation as another approximate inference method before concluding with a summary.
1) Linear factor models represent observed data vectors as a linear combination of latent factors plus noise. They include probabilistic principal component analysis (PCA) and factor analysis.
2) Independent component analysis learns components that are closer to statistically independent than the raw features, and can separate signals like voices or EEG signals.
3) Sparse coding finds a sparse representation of data by solving an optimization problem that minimizes a factor's value and reconstruction error, producing sparse weights.
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Radiotherapy planning is a multi-step process used to design and optimize a treatment regimen that delivers precise doses of radiation to cancerous tissues while minimizing damage to surrounding healthy tissues.
Genetic Issues and Male Infertility, AzoospermiaSujoy Dasgupta
Dr Sujoy Dasgupta delivered a talk on "Genetic Issues in Male Infertility" in a webinar organised by the North East India Genomic Consortium on 25 June, 2024. Number of experts in the field of Medical Genetics participated in it.
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Health Related Quality of Life (HRQOL) NotesSanthosh
Description:
Health-Related Quality of Life (HRQOL) is a comprehensive measure that evaluates the impact of health conditions on an individual’s overall well-being, focusing on both physical and mental health dimensions. Unlike traditional health metrics that primarily assess clinical or physiological factors, HRQOL captures how health issues affect various aspects of a person’s daily life, including their ability to perform routine activities, experience pain, and maintain psychological well-being.
HRQOL encompasses several key areas:
Physical Health: Includes aspects such as physical functioning, the presence of pain or discomfort, and the ability to perform everyday tasks.
Mental Health: Covers psychological and emotional well-being, including mood, stress, and cognitive function.
Social Functioning: Assesses how health conditions impact social interactions and relationships.
Overall Life Satisfaction: Reflects the individual's overall satisfaction with their quality of life in the context of their health status.
By integrating these dimensions, HRQOL provides a holistic view of how health conditions and treatments influence a person’s quality of life, beyond mere clinical outcomes. This approach helps in understanding the broader effects of health on an individual's daily experiences and overall happiness.
MRE Survival Food Review: A Comprehensive Guide to More Prepared Meals Ready-...Murat Arat
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Formulation of Buccal Drug Delivery SystemKHimani2
Buccal drug delivery system is an advanced type of drug delivery system where the drug is passed into the specific site without must wastage ! It is a novel drug delivery system where the medicament avoids 1st pass metabolism, which increases its bio availability !
* Types include matrix type and reservoir type in which 2nd type is more advanced and shows quick absorption of the drug .
* I have mentioned it's advantages and disadvantages.
* Factors effecting the drug delivery system
*Formulation of the BDDS
* Evaluation parameters
2. (Goodfellow 2018)
Disclaimer
• Details of applications change much faster than the
underlying conceptual ideas
• A printed book is updated on the scale of years, state-
of-the-art results come out constantly
• These slides are somewhat more up to date
• Applications involve much more specific knowledge, the
limitations of my own knowledge will be much more
apparent in these slides than others
3. (Goodfellow 2018)
Large Scale Deep Learning
1950 1985 2000 2015 2056
Year
10 2
10 1
100
101
102
103
104
105
106
107
108
109
1010
1011
Number
of
neurons
(logarithmic
scale)
1 2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Sponge
Roundworm
Leech
Ant
Bee
Frog
Octopus
Human
ure 1.11: Since the introduction of hidden units, artificial neural networks have doub
ize roughly every 2.4 years. Biological neural network sizes from Wikipedia (2015
Figure 1.11
4. (Goodfellow 2018)
Fast Implementations
• CPU
• Exploit fixed point arithmetic in CPU families where this offers a speedup
• Cache-friendly implementations
• GPU
• High memory bandwidth
• No cache
• Warps must be synchronized
• TPU
• Similar to GPU in many respects but faster
• Often requires larger batch size
• Sometimes requires reduced precision
5. (Goodfellow 2018)
Distributed Implementations
• Distributed
• Multi-GPU
• Multi-machine
• Model parallelism
• Data parallelism
• Trivial at test time
• Synchronous or asynchronous SGD at train time
8. (Goodfellow 2018)
Model Compression
• Large models often have lower test error
• Very large model trained with dropout
• Ensemble of many models
• Want small model for low resource use at test time
• Train a small model to mimic the large one
• Obtains better test error than directly training a small
model
13. (Goodfellow 2018)
Training Data Sample Generator
(CelebA) (Karras et al, 2017)
Generative Modeling:
Sample Generation
Covered in Part III Progressed rapidly
after the book was
written
Underlies many
graphics and
speech applications
17. (Goodfellow 2018)
g optimization with a learned predictor model. a) Original experimental
and measured binding scores (horizontal axis); we fit a model to this data
an oracle for scoring generated sequences. Plot shows scores on held-out
elation 0.97). b) Data is restricted to sequences with oracle scores in the
Model-Based Optimization
(Killoran et al, 2017)
19. (Goodfellow 2018)
Attention Mechanisms
translations (Cho et al., 2014a) and for generating translated sentences (Sutskever
et al., 2014). Jean et al. (2014) scaled these models to larger vocabularies.
12.4.5.1 Using an Attention Mechanism and Aligning Pieces of Data
↵(t 1)
↵(t 1)
↵(t)
↵(t)
↵(t+1)
↵(t+1)
h(t 1)
h(t 1)
h(t)
h(t)
h(t+1)
h(t+1)
c
c
⇥
⇥ ⇥
⇥ ⇥
⇥
+
Figure 12.6: A modern attention mechanism, as introduced by Bahdanau et al. (2015), is
essentially a weighted average. A context vector c is formed by taking a weighted average
of feature vectors h(t)
with weights ↵(t)
. In some applications, the feature vectors h are
hidden units of a neural network, but they may also be raw input to the model. The
weights ↵(t)
are produced by the model itself. They are usually values in the interval
(t)
Figure 12.6
Important in many vision, speech, and NLP applications
Improved rapidly after the book was written
23. (Goodfellow 2018)
Natural Language Processing
• An important predecessor to deep NLP is the family
of models based on n-grams:
natural language. Depending on how the model is designed, a token may
word, a character, or even a byte. Tokens are always discrete entities. The
st successful language models were based on models of fixed-length sequences
kens called n-grams. An n-gram is a sequence of n tokens.
Models based on n-grams define the conditional probability of the n-th token
the preceding n 1 tokens. The model uses products of these conditional
butions to define the probability distribution over longer sequences:
P(x1, . . . , x⌧ ) = P(x1, . . . , xn 1)
⌧
Y
t=n
P(xt | xt n+1, . . . , xt 1). (12.5)
461
imply by looking up two stored probabilities. For this to exactly reproduce
nference in Pn, we must omit the final character from each sequence when we
rain Pn 1.
As an example, we demonstrate how a trigram model computes the probability
of the sentence “THE DOG RAN AWAY.” The first words of the sentence cannot be
handled by the default formula based on conditional probability because there is no
ontext at the beginning of the sentence. Instead, we must use the marginal prob-
ability over words at the start of the sentence. We thus evaluate P3(THE DOG RAN).
Finally, the last word may be predicted using the typical case, of using the condi-
ional distribution P(AWAY | DOG RAN). Putting this together with equation 12.6,
we obtain:
P(THE DOG RAN AWAY) = P3(THE DOG RAN)P3(DOG RAN AWAY)/P2(DOG RAN).
(12.7)
A fundamental limitation of maximum likelihood for n-gram models is that Pn
as estimated from training set counts is very likely to be zero in many cases, even
hough the tuple (xt n+1, . . . , xt) may appear in the test set. This can cause two
different kinds of catastrophic outcomes. When Pn 1 is zero, the ratio is undefined,
o the model does not even produce a sensible output. When Pn 1 is non-zero but
Pn is zero, the test log-likelihood is 1. To avoid such catastrophic outcomes,
Improve with:
-Smoothing
-Backoff
-Word categories
24. (Goodfellow 2018)
Word Embeddings in Neural
Language Models
CHAPTER 12. APPLICATIONS
multiple latent variables (Mnih and Hinton, 2007).
34 32 30 28 26
14
13
12
11
10
9
8
7
6
Canada
Europe
Ontario
North
English
Canadian
Union
African
Africa
British
France
Russian
China
Germany
French
Assembly
EU Japan
Iraq
South
European
35.0 35.5 36.0 36.5 37.0 37.5 38.0
17
18
19
20
21
22
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Figure 12.3: Two-dimensional visualizations of word embeddings obtained from a neural
machine translation model (Bahdanau et al., 2015), zooming in on specific areas where
semantically related words have embedding vectors that are close to each other. Countries
Figure 12.3
26. (Goodfellow 2018)
A Hierarchy of Words and
Word Categories
CHAPTER 12. APPLICATIONS
(1)
(0)
(0,0,0) (0,0,1) (0,1,0) (0,1,1) (1,0,0) (1,0,1) (1,1,0) (1,1,1)
(1,1)
(1,0)
(0,1)
(0,0)
w0
w0 w1
w1 w2
w2 w3
w3 w4
w4 w5
w5 w6
w6 w7
w7
Figure 12.4: Illustration of a simple hierarchy of word categories, with 8 words w0, . . . , w7
Figure 12.4
27. (Goodfellow 2018)
Neural Machine Translation
HAPTER 12. APPLICATIONS
Decoder
Output object (English
sentence)
Intermediate, semantic representation
Source object (French sentence or image)
Encoder
igure 12.5: The encoder-decoder architecture to map back and forth between a surfac
epresentation (such as a sequence of words or an image) and a semantic representatio
By using the output of an encoder of data from one modality (such as the encoder mappin
Figure 12.5
31. (Goodfellow 2018)
Deep RL for Atari game playing
(Mnih et al 2013)
Convolutional network estimates the value function (future
rewards) used to guide the game-playing agent.
(Note: deep RL didn’t really exist when we started the book,
became a success while we were writing it, extremely hot topic by the time the book was printed)
32. (Goodfellow 2018)
Superhuman Go Performance
(Silver et al, 2016)
Monte Carlo tree search, with convolutional networks for value
function and policy