A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
This document provides an introduction to machine learning. It discusses how children learn through explanations from parents, examples, and reinforcement learning. It then defines machine learning as programs that improve in performance on tasks through experience processing. The document outlines typical machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type of learning and discusses evaluation methods for supervised learning models.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
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.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
This document provides an introduction to machine learning. It discusses how children learn through explanations from parents, examples, and reinforcement learning. It then defines machine learning as programs that improve in performance on tasks through experience processing. The document outlines typical machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type of learning and discusses evaluation methods for supervised learning models.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
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.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
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.
Introduction to Convolutional Neural NetworksHannes Hapke
This document provides an introduction to machine learning using convolutional neural networks (CNNs) for image classification. It discusses how to prepare image data, build and train a simple CNN model using Keras, and optimize training using GPUs. The document outlines steps to normalize image sizes, convert images to matrices, save data formats, assemble a CNN in Keras including layers, compilation, and fitting. It provides resources for learning more about CNNs and deep learning frameworks like Keras and TensorFlow.
The document is a presentation about TensorFlow. It begins with an introduction that defines machine learning and deep learning. It then discusses what TensorFlow is, including that it is an open-source library for deep learning and ML, was developed by Google Brain, and uses data flow graphs to represent computations. The presentation explains benefits of TensorFlow like parallelism, distributed execution, and portability. It provides examples of companies using TensorFlow and demonstrates cool projects that can be built with it, like image classification, object detection, and speech recognition. Finally, it concludes that TensorFlow is helping achieve amazing advancements in machine learning.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Supervised machine learning uses labeled training data to build models that can predict outputs. There are two main types: regression predicts continuous variables, while classification predicts categorical variables. Supervised learning algorithms include linear regression, which finds a linear relationship between variables, and logistic regression or decision trees for classification. The process involves collecting labeled data, training an algorithm on part of the data, and evaluating its accuracy on test data.
The document summarizes the Batch Normalization technique presented in the paper "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". Batch Normalization aims to address the issue of internal covariate shift in deep neural networks by normalizing layer inputs to have zero mean and unit variance. It works by computing normalization statistics for each mini-batch and applying them to the inputs. This helps in faster and more stable training of deep networks by reducing the distribution shift across layers. The paper presented ablation studies on MNIST and ImageNet datasets showing Batch Normalization improves training speed and accuracy compared to prior techniques.
Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...Simplilearn
The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
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/
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
C4.5 enhances ID3 by making it more robust to noise, able to handle continuous attributes, deal with missing data, and convert decision trees to rules. It avoids overfitting through pre-pruning and post-pruning techniques. When dealing with continuous attributes, it evaluates all possible split points and chooses the optimal one. It treats missing data as a separate value but this is not always appropriate. It generates rules from trees in a greedy manner by pruning conditions to reduce estimated error. The next topic will be on instance-based classifiers.
This document discusses techniques for visualizing and interpreting convolutional neural networks (CNNs). It begins by noting that while CNNs achieve high performance on computer vision tasks, their lack of interpretability is a limitation. The document then reviews popular visualization methods including Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Guided Backpropagation, and Guided Grad-CAM. It discusses the properties and procedures of each technique. An example application of Grad-CAM to a binary oral cancer classification task is also presented. In conclusion, the document proposes using visualization tools like Guided Grad-CAM to increase model transparency and investigate more advanced methods such as Grad-CAM++.
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
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.
Introduction to Convolutional Neural NetworksHannes Hapke
This document provides an introduction to machine learning using convolutional neural networks (CNNs) for image classification. It discusses how to prepare image data, build and train a simple CNN model using Keras, and optimize training using GPUs. The document outlines steps to normalize image sizes, convert images to matrices, save data formats, assemble a CNN in Keras including layers, compilation, and fitting. It provides resources for learning more about CNNs and deep learning frameworks like Keras and TensorFlow.
The document is a presentation about TensorFlow. It begins with an introduction that defines machine learning and deep learning. It then discusses what TensorFlow is, including that it is an open-source library for deep learning and ML, was developed by Google Brain, and uses data flow graphs to represent computations. The presentation explains benefits of TensorFlow like parallelism, distributed execution, and portability. It provides examples of companies using TensorFlow and demonstrates cool projects that can be built with it, like image classification, object detection, and speech recognition. Finally, it concludes that TensorFlow is helping achieve amazing advancements in machine learning.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Supervised machine learning uses labeled training data to build models that can predict outputs. There are two main types: regression predicts continuous variables, while classification predicts categorical variables. Supervised learning algorithms include linear regression, which finds a linear relationship between variables, and logistic regression or decision trees for classification. The process involves collecting labeled data, training an algorithm on part of the data, and evaluating its accuracy on test data.
The document summarizes the Batch Normalization technique presented in the paper "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". Batch Normalization aims to address the issue of internal covariate shift in deep neural networks by normalizing layer inputs to have zero mean and unit variance. It works by computing normalization statistics for each mini-batch and applying them to the inputs. This helps in faster and more stable training of deep networks by reducing the distribution shift across layers. The paper presented ablation studies on MNIST and ImageNet datasets showing Batch Normalization improves training speed and accuracy compared to prior techniques.
Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...Simplilearn
The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
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/
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
C4.5 enhances ID3 by making it more robust to noise, able to handle continuous attributes, deal with missing data, and convert decision trees to rules. It avoids overfitting through pre-pruning and post-pruning techniques. When dealing with continuous attributes, it evaluates all possible split points and chooses the optimal one. It treats missing data as a separate value but this is not always appropriate. It generates rules from trees in a greedy manner by pruning conditions to reduce estimated error. The next topic will be on instance-based classifiers.
This document discusses techniques for visualizing and interpreting convolutional neural networks (CNNs). It begins by noting that while CNNs achieve high performance on computer vision tasks, their lack of interpretability is a limitation. The document then reviews popular visualization methods including Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Guided Backpropagation, and Guided Grad-CAM. It discusses the properties and procedures of each technique. An example application of Grad-CAM to a binary oral cancer classification task is also presented. In conclusion, the document proposes using visualization tools like Guided Grad-CAM to increase model transparency and investigate more advanced methods such as Grad-CAM++.
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
An introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and Tensorboard, and then some code samples with Scala and TensorFlow.
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with code samples in Java and TensorFlow.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, and GANs, along with a simple yet complete neural network.
The document provides an overview of deep learning and its applications to Android. It begins with introductions to concepts like linear regression, activation functions, cost functions, and gradient descent. It then discusses neural networks, including convolutional neural networks (CNNs) and their use in image processing. The document outlines several approaches to integrating deep learning models with Android applications, including generating models externally or using pre-trained models. Finally, it discusses future directions for deep learning on Android like TensorFlow Lite.
This document provides an overview and introduction to deep learning. It discusses key concepts such as neural networks, hidden layers, activation functions, cost functions, and gradient descent. Specific deep learning applications are highlighted, including computer vision, speech recognition, and recommendation systems. Deep learning frameworks like TensorFlow and concepts like convolutional neural networks (CNNs) and generative adversarial networks (GANs) are also explained at a high level. The document aims to introduce attendees to the main ideas and terminology within deep learning.
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
This document provides an overview of deep learning, machine learning, and artificial intelligence. It discusses the differences between traditional AI, machine learning, and deep learning. Key deep learning concepts covered include neural networks, activation functions, cost functions, gradient descent, backpropagation, and hyperparameters. Convolutional neural networks and their applications are explained. Recurrent neural networks are also introduced. The document discusses TypeScript and how it can be used for deep learning applications.
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks, followed by a TypeScript-based code sample that replicates the Tensorflow playground. Basic knowledge of matrices is helpful for this session.
This presentation introduces Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, followed by an Angular application that uses TypeScript in order to replicate the Tensorflow playground.
"A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.
An introductory document covered deep learning concepts including neural networks, activation functions, cost functions, gradient descent, TensorFlow, CNNs, RNNs, GANs, and tensorflow.js. Key topics included the use of deep learning for computer vision, speech recognition, and more. Activation functions such as ReLU, sigmoid and tanh were explained. TensorFlow and tensorflow.js were introduced as frameworks for deep learning.
A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.
An introductory presentation covered key concepts in deep learning including neural networks, activation functions, cost functions, and optimization methods. Popular deep learning frameworks TensorFlow and tensorflow.js were discussed. Common deep learning architectures like convolutional neural networks and generative adversarial networks were explained. Examples and code snippets in Python demonstrated fundamental deep learning concepts.
Intro to Deep Learning, TensorFlow, and tensorflow.jsOswald Campesato
This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.
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. Then we'll see a short introduction to TensorFlow and TensorBoard.
Similar to Deep Learning, Keras, and TensorFlow (20)
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth.
Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.
This document provides an overview and introduction to TensorFlow 2. It discusses major changes from TensorFlow 1.x like eager execution and tf.function decorator. It covers working with tensors, arrays, datasets, and loops in TensorFlow 2. It also demonstrates common operations like arithmetic, reshaping and normalization. Finally, it briefly introduces working with Keras and neural networks in TensorFlow 2.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset.
Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.
Introduction to Deep Learning, Keras, and TensorflowOswald Campesato
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. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
An introduction to Kotlin for advanced Android beginners, covering command-line compilation of Kotlin files, conditional logic, val/var, basic functions, higher order functions, recursion.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
7. Linear Classifier: Example #1
Given some red dots and blue dots
Red dots are in the upper half plane
Blue dots in the lower half plane
How to detect if a point is red or blue?
10. Linear Classifier: Example #2
Given some red dots and blue dots
Red dots are inside a unit square
Blue dots are outside the unit square
How to detect if a point is red or blue?
11. Linear Classifier: Example #2
Two input nodes X and Y
One hidden layer with 4 nodes (one per line)
X & Y weights are the (x,y) values of the inward pointing
perpendicular vector of each side
The threshold values are the negative of the y-intercept
(or the x-intercept)
The outbound weights are all equal to 1
The threshold for the output node node is 4
14. Exercises #1
Describe an NN for a triangle
Describe an NN for a pentagon
Describe an NN for an n-gon (convex)
Describe an NN for an n-gon (non-convex)
15. Exercises #2
Create an NN for an OR gate
Create an NN for a NOR gate
Create an NN for an AND gate
Create an NN for a NAND gate
Create an NN for an XOR gate
=> requires TWO hidden layers
17. Linear Classifier: Example #2
A few points to keep in mind:
A “step” activation function (0 or 1)
No back propagation
No cost function
=> no learning involved
18. A Basic Model in Machine Learning
Let’s perform the following steps:
1) Start with a simple model (2 variables)
2) Generalize that model (n variables)
3) See how it might apply to a NN
19. Linear Regression
One of the simplest models in ML
Fits a line (y = m*x + b) to data in 2D
Finds best line by minimizing MSE:
m = average of x values (“mean”)
b also has a closed form solution
22. Linear Regression: example #1
One feature (independent variable):
X = number of square feet
Predicted value (dependent variable):
Y = cost of a house
A very “coarse grained” model
We can devise a much better model
23. Linear Regression: example #2
Multiple features:
X1 = # of square feet
X2 = # of bedrooms
X3 = # of bathrooms (dependency?)
X4 = age of house
X5 = cost of nearby houses
X6 = corner lot (or not): Boolean
a much better model (6 features)
24. Linear Multivariate Analysis
General form of multivariate equation:
Y = w1*x1 + w2*x2 + . . . + wn*xn + b
w1, w2, . . . , wn are numeric values
x1, x2, . . . , xn are variables (features)
Properties of variables:
Can be independent (Naïve Bayes)
weak/strong dependencies can exist
26. Neural Networks: equations
Node “values” in first hidden layer:
N1 = w11*x1+w21*x2+…+wn1*xn
N2 = w12*x1+w22*x2+…+wn2*xn
N3 = w13*x1+w23*x2+…+wn3*xn
. . .
Nn = w1n*x1+w2n*x2+…+wnn*xn
Similar equations for other pairs of layers
27. Neural Networks: Matrices
From inputs to first hidden layer:
Y1 = W1*X + B1 (X/Y1/B1: vectors; W1: matrix)
From first to second hidden layers:
Y2 = W2*X + B2 (X/Y2/B2: vectors; W2: matrix)
From second to third hidden layers:
Y3 = W3*X + B3 (X/Y3/B3: vectors; W3: matrix)
Apply an “activation function” to y values
28. Neural Networks (general)
Multiple hidden layers:
Layer composition is your decision
Activation functions: sigmoid, tanh, RELU
https://en.wikipedia.org/wiki/Activation_function
Back propagation (1980s)
https://en.wikipedia.org/wiki/Backpropagation
=> Initial weights: small random numbers
35. What’s the “Best” Activation Function?
Initially: sigmoid was popular
Then: tanh became popular
Now: RELU is preferred (better results)
Softmax: for FC (fully connected) layers
NB: sigmoid and tanh are used in LSTMs
39. How to Select a Cost Function
mean-squared error:
for a regression problem
binary cross-entropy (or mse):
for a two-class classification problem
categorical cross-entropy:
for a many-class classification problem
40. Setting up Data & the Model
Normalize the data:
Subtract the ‘mean’ and divide by stddev
Initial weight values for NNs:
random(0,1) or N(0,1) or N(0/(1/n))
More details:
http://cs231n.github.io/neural-networks-2/#losses
41. Hyper Parameters (examples)
# of hidden layers in a neural network
the learning rate (in many models)
the dropout rate
# of leaves or depth of a tree
# of latent factors in a matrix factorization
# of clusters in a k-means clustering
42. Hyper Parameter: dropout rate
"dropout" refers to dropping out units (both hidden
and visible) in a neural network
a regularization technique for reducing overfitting in
neural networks
prevents complex co-adaptations on training data
a very efficient way of performing model averaging
with neural networks
43. How Many Hidden Nodes in a DNN?
Based on a relationship between:
# of input and # of output nodes
Amount of training data available
Complexity of the cost function
The training algorithm
TF playground home page:
http://playground.tensorflow.org
44. CNNs versus RNNs
CNNs (Convolutional NNs):
Good for image processing
2000: CNNs processed 10-20% of all checks
=> Approximately 60% of all NNs
RNNs (Recurrent NNs):
Good for NLP and audio
50. What is Keras?
a high-level NN API
written in Python
supports TensorFlow and CNTK
supports CNNs and RNNs
supports CPUs and GPUs
good for prototyping
https://keras.io/
54. What is TensorFlow?
An open source framework for ML and DL
A “computation” graph
Created by Google (released 11/2015)
Evolved from Google Brain
Linux and Mac OS X support (VM for Windows)
TF home page: https://www.tensorflow.org/
55. What is TensorFlow?
Support for Python, Java, C++
Desktop, server, mobile device (TensorFlow Lite)
CPU/GPU/TPU support
Visualization via TensorBoard
Can be embedded in Python scripts
Installation: pip install tensorflow
TensorFlow cluster:
https://www.tensorflow.org/deploy/distributed
57. What is TensorFlow?
Graph: graph of operations (DAG)
Sessions: contains Graph(s)
lazy execution (default)
operations in parallel (default)
Nodes: operators/variables/constants
Edges: tensors
=> graphs are split into subgraphs and
executed in parallel (or multiple CPUs)
58. TensorFlow Graph Execution
Execute statements in a tf.Session() object
Invoke the “run” method of that object
“eager” execution is now possible
Not part of the mainline yet
Installation: pip install tf-nightly
59. What is a Tensor?
TF tensors are n-dimensional arrays
TF tensors are very similar to numpy ndarrays
scalar number: a zeroth-order tensor
vector: a first-order tensor
matrix: a second-order tensor
3-dimensional array: a 3rd order tensor
https://dzone.com/articles/tensorflow-simplified-
examples
60. TensorFlow “primitive types”
tf.constant: initialized immediately
tf.placeholder (a function):
+ initial value is not required
+ assigned value via feed_dict at run time
+ are not modified during training
tf.Variable (a class):
+ initial value is required
+ updated during training
+ in-memory buffer (saved/restored from disk)
+ can be shared between works (distributed env)
66. TF placeholders and feed_dict
import tensorflow as tf # tf-var-multiply.py
a = tf.placeholder("float")
b = tf.placeholder("float")
c = tf.multiply(a,b)
# initialize a and b:
feed_dict = {a:2, b:3}
# multiply a and b:
with tf.Session() as sess:
print(sess.run(c, feed_dict))
67. TensorFlow and Linear Regression
import tensorflow as tf
# W and x are 1d arrays
W = tf.constant([10,20], name=’W’)
x = tf.placeholder(tf.int32, name='x')
b = tf.placeholder(tf.int32, name='b')
Wx = tf.multiply(W, x, name='Wx')
y = tf.add(Wx, b, name=’y’)
68. TensorFlow fetch/feed_dict
with tf.Session() as sess:
print("Result 1: Wx = ",
sess.run(Wx, feed_dict={x:[5,10]}))
print("Result 2: y = ",
sess.run(y, feed_dict={x:[5,10], b:[15,25]}))
Result 1: Wx = [50 200]
Result 2: y = [65 225]
69. Saving Graphs for TensorBoard
import tensorflow as tf # tf-save-data.py
x = tf.constant(5,name="x")
y = tf.constant(8,name="y")
z = tf.Variable(2*x+3*y, name="z”)
model = tf.global_variables_initializer()
with tf.Session() as session:
writer = tf.summary.FileWriter(”./tf_logs",session.graph)
session.run(model)
print 'z = ',session.run(z) # => z = 34
# launch tensorboard: tensorboard –logdir=./tf_logs
70. TensorFlow Eager Execution
An imperative interface to TF (experimental)
Fast debugging & immediate run-time errors
Eager execution is not included in v1.4 of TF
build TF from source or install the nightly build
pip install tf-nightly # CPU
pip install tf-nightly-gpu #GPU
=> requires Python 3.x (not Python 2.x)
71. TensorFlow Eager Execution
integration with Python tools
Supports dynamic models + Python control flow
support for custom and higher-order gradients
Supports most TensorFlow operations
https://research.googleblog.com/2017/10/eager-
execution-imperative-define-by.html
80. Deep Learning and Art
“Convolutional Blending” (19-layer CNN):
www.deepart.io
Bots created their own language:
https://www.recode.net/2017/3/23/14962182/ai-learning-
language-open-ai-research
https://www.fastcodesign.com/90124942/this-google-
engineer-taught-an-algorithm-to-make-train-footage-
and-its-hypnotic
82. About Me: Recent Books
1) HTML5 Canvas and CSS3 Graphics (2013)
2) jQuery, CSS3, and HTML5 for Mobile (2013)
3) HTML5 Pocket Primer (2013)
4) jQuery Pocket Primer (2013)
5) HTML5 Mobile Pocket Primer (2014)
6) D3 Pocket Primer (2015)
7) Python Pocket Primer (2015)
8) SVG Pocket Primer (2016)
9) CSS3 Pocket Primer (2016)
10) Android Pocket Primer (2017)
11) Angular Pocket Primer (2017)
12) Data Cleaning Pocket Primer (2018)
13) RegEx Pocket Primer (2018)
83. About Me: Training
=> Deep Learning. Keras, and TensorFlow:
http://codeavision.io/training/deep-learning-workshop
=> Mobile and TensorFlow Lite (WIP)
=> R and Deep Learning (WIP)
=> Android for Beginners