What is a Neural Network | Edureka
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YouTube Link: https://youtu.be/vpOLiDyhNUA ** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training ** This Edureka PPT on 'What is a Neural Network' will help you understand how Neural Networks can be used to solve complex, data-driven problems along with their real-world applications. Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Castbox: https://castbox.fm/networks/505?country=in
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Machine Learning
Active learning is a machine learning technique where the learner is able to interactively query the oracle (e.g. a human) to obtain labels for new data points in an effort to learn more accurately from fewer labeled examples. The learner selects the most informative samples to be labeled by the oracle, such as samples closest to the decision boundary or where models disagree most. This allows the learner to minimize the number of labeled samples needed, thus reducing the cost of training an accurate model. Suggested improvements include querying batches of samples instead of single samples and accounting for varying labeling costs.
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Machine Learning
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
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Neural networks are a type of data mining technique inspired by biological neural systems. They are composed of interconnected nodes similar to neurons in the brain. Neural networks can learn patterns from complex data through supervised or unsupervised learning methods. They are widely used for applications like fraud detection, risk assessment, image recognition, and stock market prediction due to their ability to learn from examples without being explicitly programmed.
Deep learning
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Deep neural networks
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
Introduction to Deep Learning
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
MACHINE LEARNING - GENETIC ALGORITHM
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Neural networks...
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networ...
This Edureka "Deep Learning Tutorial" (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. Single Layer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Mc Culloch Pitts Neuron
The document discusses various aspects of artificial intelligence including machine learning algorithms like Naive Bayes, K-Means clustering, and neural networks. It focuses on deep learning and artificial neural networks, explaining the basic biological structure of neurons and how artificial neural networks are modeled after this with layers of nodes that can learn from data. Specific neural network models are examined like McCulloch-Pitts neurons, perceptrons, and sigmoid neurons.
Classification using perceptron.pptx
This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
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Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edu...
This Edureka "Neural Network Tutorial" tutorial will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. Motivation Behind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Machine Learning Course | Edureka
The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
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Machine Learning
Active learning is a machine learning technique where the learner is able to interactively query the oracle (e.g. a human) to obtain labels for new data points in an effort to learn more accurately from fewer labeled examples. The learner selects the most informative samples to be labeled by the oracle, such as samples closest to the decision boundary or where models disagree most. This allows the learner to minimize the number of labeled samples needed, thus reducing the cost of training an accurate model. Suggested improvements include querying batches of samples instead of single samples and accounting for varying labeling costs.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
Machine Learning
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
Introduction to Machine Learning Classifiers
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Data Science Full Course | Edureka
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
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Deep Learning With Neural Networks
Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.
2.mathematics for machine learning
This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.
Neural networks
Neural networks are a type of data mining technique inspired by biological neural systems. They are composed of interconnected nodes similar to neurons in the brain. Neural networks can learn patterns from complex data through supervised or unsupervised learning methods. They are widely used for applications like fraud detection, risk assessment, image recognition, and stock market prediction due to their ability to learn from examples without being explicitly programmed.
Deep learning
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Deep neural networks
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
Introduction to Deep Learning
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
MACHINE LEARNING - GENETIC ALGORITHM
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Neural networks...
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networ...
This Edureka "Deep Learning Tutorial" (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. Single Layer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
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Mc Culloch Pitts Neuron
The document discusses various aspects of artificial intelligence including machine learning algorithms like Naive Bayes, K-Means clustering, and neural networks. It focuses on deep learning and artificial neural networks, explaining the basic biological structure of neurons and how artificial neural networks are modeled after this with layers of nodes that can learn from data. Specific neural network models are examined like McCulloch-Pitts neurons, perceptrons, and sigmoid neurons.
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This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
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This document describes an artificial neural network project presented by Rm.Sumanth, P.Ganga Bashkar, and Habeeb Khan to Madina Engineering College. It provides an overview of artificial neural networks and supervised learning techniques. Specifically, it discusses the biological structure of neurons and how artificial neural networks emulate this structure. It then describes the perceptron model and learning rule, and how multilayer feedforward networks using backpropagation can learn more complex patterns through multiple layers of neurons.
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)
Convolutional Neural Networks (D1L3 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.Adv.TopicsAICNN.ppt
Convolutional layer:
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2) Number of filters equals the number of channels in the output volume.
3) Stride is usually 1 to preserve spatial information.
Pooling layer:
1) Pooling filter width and height is usually 2x2.
2) Stride is usually the same as the filter size (2x2) to halve the spatial dimensions at each downsampling.
3)
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http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
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https://telecombcn-dl.github.io/2018-dlai/
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This document provides an introduction to machine learning applications using deep learning techniques. It discusses how deep learning can be applied to computer vision, text generation, reinforcement learning, and more. The document then explains key concepts in deep learning including neural networks, convolutional neural networks, pooling layers, dropout, and techniques for training neural networks like forward and backpropagation.
2017 07 03_meetup_d
This document provides an overview of deep learning and its applications in medical image analysis. It begins with an introduction to the speaker and their background in biomedical image analysis. It then discusses machine learning and how deep learning uses neural networks with many layers to automatically determine useful features from data. Convolutional neural networks are described as being well-suited for image analysis. Several examples of deep learning applications in medical images are given, including brain MRI segmentation, detection of prostate cancer in ultrasound images, and the speaker's own work on neonatal brain injury assessment from MRI scans. Resources for getting started with deep learning are also listed.
2017 07 03_meetup_d
A 3 sentence summary of the document:
The document discusses deep learning for medical image analysis, focusing on applications in neonatal medical imaging. It provides an overview of deep learning and convolutional neural networks, including examples of their use for tasks like brain tissue segmentation in MRI scans of newborns. The presenter describes their research using deep learning for segmentation and diagnosis of hypoxic ischemic encephalopathy in MRI scans of newborns.
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- 6. Artificial Intelligence Machine Learning Deep Learning
- 7. Artificial Intelligence Machine Learning Deep Learning Artificial Intelligence, Machine Learning and Deep Learning are interconnected fields. Machine Learning and Deep learning aids Artificial Intelligence by providing a set of algorithms and Neural Networks to solve data driven problems.
- 8. Deep learning makes use of Artificial Neural Networks that behave similar to the Neural Networks in our brain.
- 9. A Neural Network functions when some input data is fed to it. This data is then processed via layers of Perceptrons to produce a desired output.
- 10. Problem Statement: Classify leaf images as either diseased or non-diseased.
- 11. Non-diseased leaves Diseased leaves Problem Statement: Classify leaf images as either diseased or non-diseased.
- 12. How do you create a Neural Network that classifies the leaves into diseased and non-diseased crops?
- 13. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer
- 14. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 𝑖=1 𝑛 𝑥𝑖 𝑤𝑖 𝑥1 𝑤1 + 𝑥2 𝑤2 + ⋯ ⋅ 𝑥𝑖 𝑤𝑖 = w2 w3 w4 wi b b b b b
- 15. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 𝑖=1 𝑛 𝑥𝑖 𝑤𝑖 𝑥1 𝑤1 + 𝑥2 𝑤2 + ⋯ ⋅ 𝑥𝑖 𝑤𝑖 = w2 w3 w4 wi b b b b b
- 16. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 𝑖=1 𝑛 𝑥𝑖 𝑤𝑖 𝑥1 𝑤1 + 𝑥2 𝑤2 + ⋯ ⋅ 𝑥𝑖 𝑤𝑖 = w2 w3 w4 wi b b b b b
- 17. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 𝑖=1 𝑛 𝑥𝑖 𝑤𝑖 𝑥1 𝑤1 + 𝑥2 𝑤2 + ⋯ ⋅ 𝑥𝑖 𝑤𝑖 = w2 w3 w4 wi b b b b b P = 0.7 P = 0.3
- 18. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 𝑖=1 𝑛 𝑥𝑖 𝑤𝑖 𝑥1 𝑤1 + 𝑥2 𝑤2 + ⋯ ⋅ 𝑥𝑖 𝑤𝑖 = w2 w3 w4 wi b b b b b P = 0.7
- 19. 30 x 30 pixels 30x30pixels Now let’s assume a case where the predicted output is wrong. In such a situation, we train the Neural Network by using the Backpropagation method.
- 20. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 w2 b b b b b w3 w4 wi
- 21. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 w1 w2 b b b b b w3 w4 wi Actual output Predicted output
- 22. 30 x 30 pixels 30x30pixels Input layer Hidden layer Output layer x1 x2 x3 x4 b b b b b Δw1 Δw2 Δw3 Δw4 Δwi Correct
- 23. With the help of Deep Learning techniques Google can instantly translate between more than 100 different human languages.
- 24. From Tesla to Google owned WAYMO, self driving cars are being perfected with the help of Neural Networks.
- 25. Virtual assistants are purely based on technologies including Deep Learning, Machine Learning and Natural Language Processing.
- 26. “AI bots just beat humans at the video game DOTA 2. That’s a big deal, because their victory required teamwork and collaboration – a huge milestone in advancing Artificial Intelligence.” - Bill Gates
- 27. MuseNet, a Deep Neural Network that can generate 4- minute musical compositions with 10 different instruments.
- 28. Wordsmith is an AI-powered tool that produces written analytics by transforming the given data.