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  • Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch">Gold BlogNothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

    ...bypass the feature engineering ourselves and lets the neural network figure out the feature crosses itself! Let’s take a look at the following neural network: Fig 77. Neural network with one hidden layer. So we’ve added a bunch of new nodes in the middle of our neural network architecture from the...

    https://www.kdnuggets.com/2019/08/numpy-neural-networks-computational-graphs.html

  • Top 30 Social Network Analysis and Visualization Tools

    …online portal for researchers, educators, and practitioners interested in the study of biomedical, social and behavioral science, physics, and other networks. NetworKit is a growing open-source toolkit for high-performance network analysis. Its aim is to provide tools for the analysis of large…

    https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html

  • Generalization in Neural Networks

    ...here’s going to be some data that the neural network trains on, and there’s going to be some data reserved for checking the performance of the neural network. If the neural network performs well on the data which it has not trained on, we can say it has generalized well on the given data. Let’s...

    https://www.kdnuggets.com/2019/11/generalization-neural-networks.html

  • The Star Wars social networks – who is the central character?

    …o appear across all of the films but they don’t talk directly with many people in the original trilogy, which moves them off the centre in the social network. Networks in individual films Now let’s look at the networks in individual films. Notice how the number of nodes and complexity of the…

    https://www.kdnuggets.com/2015/12/star-wars-social-network-who-is-central-character.html

  • Data Science Programming: Python vs R

    …for statistical computing. At DeZyre, our career counsellors often get questions from prospective students as to what should they learn first Python programming or R programming. If you are unsure on which programming language to learn first then you are on the right page. Python and R language…

    https://www.kdnuggets.com/2015/10/data-science-programming-python-vs-r.html

  • Research Guide for Video Frame Interpolation with Deep Learning

    ...ve warping layer is adopted to warp the input frames, depth maps, and contextual features. The final frame output is generated from a frame synthesis network. The network takes the warped input frames, warped depth maps, contextual features, projected flows, and interpolation kernels as its input....

    https://www.kdnuggets.com/2019/10/research-guide-video-frame-interpolation-deep-learning.html

  • Interview: Reiner Kappenberger, HP Security Voltage on Data-Centric Security for Big Data

    ...computer science. Here is my interview with him: Anmol Rajpurohit: Q1. What does HP Security Voltage do? How and when did the focus shift from emails security to data security? Reiner Kappenberger: We provide data-centric security and stateless key management solutions that help organizations...

    https://www.kdnuggets.com/2015/07/interview-reiner-kappenberger-hp-security-voltage-big-data.html

  • A Quick Introduction to Neural Networks

    ...n and adjusting weights). Figure 6: backward propagation and weight updation step in a multi layer perceptron If we now input the same example to the network again, the network should perform better than before since the weights have now been adjusted to minimize the error in prediction. As shown...

    https://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html

  • Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Silver BlogUnderstanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

    ...keras-with-gpu-on-amazon-ec2-a-step-by-step-instruction-4f90364e49ac They should get you started to: Set up an EC2 VM and connect to it Configure the network security to access jupyter notebook remotely 4 - Building a cat/dog classifier using Tensorfow and Keras The environment is now set up. We're...

    https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

  • 9 Key Deep Learning Papers, Explained">Gold Blog9 Key Deep Learning Papers, Explained

    ...for the output volume. The way that the authors address this is by adding 1x1 conv operations before the 3x3 and 5x5 layers. The 1x1 convolutions (or network in network layer) provide a method of dimensionality reduction. For example, let’s say you had an input volume of 100x100x60 (This isn’t...

    https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

  • Graph and Social Network Analysis, Link Analysis, and Visualization

    ...alysis. R, includes several packages relevant for social network analysis: igraph: generic network analysis package; sna: for sociometric analysis of networks; network manipulates and displays network objects. Social Networks Visualiser (SocNetV), a flexible and user-friendly tool for the analysis...

    https://www.kdnuggets.com/software/social-network-analysis.html

  • TensorFlow: Building Feed-Forward Neural Networks Step-by-Step">Silver BlogTensorFlow: Building Feed-Forward Neural Networks Step-by-Step

    ...uts: [[1], [1], [0], [0]]}) But for code clarity, the NumPy arrays are created separately from the run() operation.   Testing the Trained Neural Network   After getting out of the training loop, the neural network will be trained and ready for predicting unknown samples. In line 48, two...

    https://www.kdnuggets.com/2017/10/tensorflow-building-feed-forward-neural-networks-step-by-step.html

  • Data Mining for Predictive Social Network Analysis – Brazil Elections Case Study

    …ed networks by default. Two commonly-used examples of this type of network are children in a classroom or workers inside an organization. Open system networks are networks where the boundary lines are not clearly defined, which makes this type of network typically the most difficult to study. The…

    https://www.kdnuggets.com/2015/11/data-mining-predictive-social-network-analysis.html

  • An Intuitive Explanation of Convolutional Neural Networks

    …n References section below. References Clarifai Home Page Shaoqing Ren, et al, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, 2015, arXiv:1506.01497 Neural Network Architectures, Eugenio Culurciello’s blog CS231n Convolutional Neural Networks for Visual…

    https://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html

  • Applying Data Science to Cybersecurity Network Attacks & Events

    ...t the time, I decided I wanted to get into cybersecurity during my undergrad in college. What intrigued me was understanding the concepts of malware, network security, penetration testing & the encryption aspect that really plays a role in what cybersecurity really is. Being able to protect the...

    https://www.kdnuggets.com/2019/09/applying-data-science-cybersecurity-network-attacks-events.html

  • Interview: Reiner Kappenberger, HP Security Voltage on How to Secure Data-in-Motion

    ...s need to be aware that data at rest protection does not secure data in motion, or in use, leaving the potential for major compliance and exploitable security gaps. An organization’s security posture has to include protection for data in-motion and in use in analytics. It’s really important to...

    https://www.kdnuggets.com/2015/07/interview-reiner-kappenberger-hp-security-voltage-data-in-motion.html

  • Deep Learning Key Terms, Explained">Gold BlogDeep Learning Key Terms, Explained

    ...initions. 1. Deep Learning As defined above, deep learning is the process of applying deep neural network technologies to solve problems. Deep neural networks are neural networks with one hidden layer minimum (see below). Like data mining, deep learning refers to a process, which employs deep...

    https://www.kdnuggets.com/2016/10/deep-learning-key-terms-explained.html

  • 7 Types of Artificial Neural Networks for Natural Language Processing">Silver Blog7 Types of Artificial Neural Networks for Natural Language Processing

    ...ural network (RNN)   A simple recursive neural network architecture (https://upload.wikimedia.org/wikipedia/commons/6/60/Simple_recursive_neural_network.svg) A recursive neural network (RNN) is a type of deep neural network formed by applying the same set of weights recursively over a...

    https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

  • Information Management 10 IT Security Books for Big Data Scientists

    …e use of personal data, with one undeniable conclusion: once data’s been collected, we have absolutely no control over who uses it or how it is used. Network Security with NetFlow and IPFIX: Big Data Analytics for Information Security, 1st Edition, by Omar Santos This is the definitive guide to…

    https://www.kdnuggets.com/2015/08/information-management-security-books-big-data-scientists.html

  • Interview: Reiner Kappenberger, HP Security Voltage on Security Checklist for Data Architectures

    ...elcos, credit card processors and issuers, healthcare entities, government agencies and even industry regulators: they are all embracing data-centric security. Data-centric security protects the data across its lifecycle — from capture, in motion, at rest, and even in use. AR: Q10. What approach do...

    https://www.kdnuggets.com/2015/07/interview-reiner-kappenberger-hp-security-voltage-checklist.html

  • Introduction to Functional Programming in Python">Gold BlogIntroduction to Functional Programming in Python

    ...4), ('b', 6), ('a', 10)] comments The Map Function   While the ability to pass in functions as arguments is not unique to Python, it is a recent development in programming languages. Functions that allow for this type of behavior are called first-class functions. Any language that contains...

    https://www.kdnuggets.com/2018/02/introduction-functional-programming-python.html

  • Training a Neural Network to Write Like Lovecraft">Gold BlogTraining a Neural Network to Write Like Lovecraft

    ...sp; A LSTM (Long Short-term Memory) Neural Network is just another kind of Artificial Neural Network, which falls in the category of Recurrent Neural Networks. What makes LSTM Neural Networks different from regular Neural Networks is, they have LSTM cells as neurons in some of their layers. Much...

    https://www.kdnuggets.com/2019/07/training-neural-network-write-like-lovecraft.html

  • Gold BlogKnowing Your Neighbours: Machine Learning on Graphs">Silver BlogGold BlogKnowing Your Neighbours: Machine Learning on Graphs

    ...rithms are adept at automatically learning essential features that maximise the performance of a downstream task. Unfortunately, “traditional” neural network and convolutional neural network algorithms cannot directly exploit relationship data. Despite this, researchers recently proposed graph...

    https://www.kdnuggets.com/2019/08/neighbours-machine-learning-graphs.html

  • ResNets, HighwayNets, and DenseNets, Oh My!

    ...the network updates itself appropriately. With a traditional network this gradient becomes slightly diminished as it passes through each layer of the network. For a network with just a few layers, this isn’t an issue. For a network with more than a couple dozen layers however, the signal...

    https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html

  • Get Network insights in Excel with NodeXL

    ...n have a large group of isolate users in the G1 position in their network. Among these users, ten people and accounts are most in the "center" of the network (in terms of the network metric "betweenness centrality"): These contributors (@kdnuggets, @ronald_vanloon, @alevergara78, @deeplearn007,...

    https://www.kdnuggets.com/2017/12/nodexl-network-insights-excel.html

  • How to Visualize your Facebook Network

    …rk has become hard. Hervé Piedcoq, data analyst and OSINT expert is going to show you a method to 1) collect, 2) store and 3) visualize your Facebook network. Collecting the data to build your Facebook network 1st step : download your friends’ list We will use OutWit Hub, a powerful yet easy to use…

    https://www.kdnuggets.com/2015/06/visualize-facebook-network.html

  • KDnuggets Interview with Jon Kleinberg, revisited

    …mation about the network, but collectively they were able to route the message to a far-awar destination. My work on this problem centered around the development of social network models, building on the Watts-Strogatz framework, in which one could quantify the power of such decentralized…

    https://www.kdnuggets.com/2013/07/kdnuggets-interview-with-jon-kleinberg-revisited.html

  • KDnuggets Interview with Jon Kleinberg, revisited

    ...mation about the network, but collectively they were able to route the message to a far-awar destination. My work on this problem centered around the development of social network models, building on the Watts-Strogatz framework, in which one could quantify the power of such decentralized...

    https://www.kdnuggets.com/2013/07/kdnuggets-interview-with-jon-kleinberg-revisited.html

  • Linear Programming and Discrete Optimization with Python using PuLP

    ...were allowed to assume any real number value. Integer programming forces some or all of the variables to assume only integer values. In fact, integer programming is a harder computational problem than linear programming. Integer variables make an optimization problem non-convex, and therefore far...

    https://www.kdnuggets.com/2019/05/linear-programming-discrete-optimization-python-pulp.html

  • Mathematical programming —  Key Habit to Build Up for Advancing Data Science">Gold BlogMathematical programming —  Key Habit to Build Up for Advancing Data Science

    ...a statistically sound manner.   Summary (and a challenge for the reader)   We demonstrate what it means to develop a habit of mathematical programming. Essentially, it is thinking in terms of programming to test out the mathematical properties or data patterns that you are developing in...

    https://www.kdnuggets.com/2019/05/mathematical-programming-key-habit-advancing-data-science.html

  • The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">Gold BlogThe 8 Neural Network Architectures Machine Learning Researchers Need to Learn

    ...best systems for reading cursive writing. In brief, they used a sequence of small images as input rather than pen coordinates. comments 5 — Hopfield Networks   Recurrent networks of non-linear units are generally very hard to analyze. They can behave in many different ways: settle to a stable...

    https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

  • How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks

    ...or this involves an iterative process of smart training and pruning which can be summarized in the following five steps: Randomly initialize a neural network. Train the network until it converges. Prune a fraction of the network. To extract the winning ticket, reset the weights of the remaining...

    https://www.kdnuggets.com/2019/05/lottery-ticket-hypothesis-neural-networks.html

  • The Most Complete Guide to PyTorch for Data Scientists

    ...ne how those layers would be connected to each other. In the forward pass block, the user defines how data flows from one layer to another inside the network. So, put simply, any network we define will look like: Here we have defined a very simple Network that takes an input of size 784 and passes...

    https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html

  • Research Guide for Depth Estimation with Deep Learning

    ...handle highly dynamic scenes. This is done by introducing a third component to the model that predicts motions of objects in 3D. It utilizes the same network structure as the ego-motion network but trains to separate weights. The motion model predicts the transformation vectors per object in...

    https://www.kdnuggets.com/2019/11/research-guide-depth-estimation-deep-learning.html

  • Big Data and Data Science for Security and Fraud Detection

    …chniques for Security and Fraud Detection Big Data System in Abu Dhabi to prevent Terrorism In Abu Dhabi, top security experts have presented a novel security concept through the development of a big data system to Abu Dhabi Autonomous Systems Investments, Tawazum Company. The big data system would…

    https://www.kdnuggets.com/2015/12/big-data-science-security-fraud-detection.html

  • Improving the Performance of a Neural Network

    ...etwork, leading to reduction in error in the test set.   Hyperparameter Tuning   Hyperparameters are values that you must initialise to the network, these values can’t be learned by the network while training. E.x: In a convolutional neural network, some of the hyperparameters are kernel...

    https://www.kdnuggets.com/2018/05/improving-performance-neural-network.html

  • Research Guide for Neural Architecture Search

    ...The authors search for a computation cell as the building block of the final architecture. The learned cell could be stacked to form a convolutional network or a recurrent network by being recursively connected. A cell is a directed acyclic graph consisting of an ordered sequence of N nodes. Each...

    https://www.kdnuggets.com/2019/10/research-guide-neural-architecture-search.html

  • Deepmind’s Gaming Streak: The Rise of AI Dominance

    ...In fact, simple tasks like balancing a rod on a rail can be accomplished with no more than 7 binary connections in a single hidden layer feed-forward network. The networks used for basic RL experiments may seem paltry next to the 100+ layers used in state-of-the-art image tasks, but this is...

    https://www.kdnuggets.com/2020/05/deepmind-gaming-ai-dominance.html

  • Machine Learning and Cyber Security Resources">Silver BlogMachine Learning and Cyber Security Resources

    ...ed them as well. Using Machine Learning to Support Information Security. Defending Networks with Incomplete Information. Applying Machine Learning to Network Security Monitoring. Measuring the IQ of your Threat Intelligence Feed. Data-Driven Threat Intelligence: Metrics On Indicator Dissemination...

    https://www.kdnuggets.com/2017/01/machine-learning-cyber-security.html

  • Top 7 Data Science Use Cases in Trust and Security

    ...trust is lost. Big data analytics offers an opportunity to monitor and analyze the processes that previously were hidden from the companies. Big data security tools cover the scope of security information and event management technologies, and performance and availability monitoring technologies....

    https://www.kdnuggets.com/2019/12/top-7-data-science-use-cases-trust-security.html

  • Free From MIT: Intro to Computer Science and Programming in Python">Gold BlogFree From MIT: Intro to Computer Science and Programming in Python

    ...yone else learning to program). An understanding of computer science principles, computational approaches to problem solving, and the fundamentals of programming, all independent of implementation programming language, should be the goal of anyone with a true desire to really learn how to code....

    https://www.kdnuggets.com/2020/09/free-mit-intro-computer-science-programming-python.html

  • Deep Learning for NLP: Creating a Chatbot with Keras!">Silver BlogDeep Learning for NLP: Creating a Chatbot with Keras!

    ...SENTENCE: "Mary moved to the bathroom . Sandra journeyed to the bedroom ." Okay, now that we have prepared the data, we are ready to build our Neural Network!   The Neural Network: Building the model   The first step to creating the network is to create what in Keras is known as...

    https://www.kdnuggets.com/2019/08/deep-learning-nlp-creating-chatbot-keras.html

  • Can graph machine learning identify hate speech in online social networks?

    ...rk which is different from Twitter’s network of follower and followee relationships. Followees are hidden from us since users can elect to keep their network private, while the retweet network remains public so long as the original tweets are public. The relative proportions of annotated users in...

    https://www.kdnuggets.com/2019/09/graph-machine-learning-hate-speech-social-networks.html

  • Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step

    ...mber of parameters than FC networks.   4. Neurons Grouping   The problem that makes the number of parameters gets very large even for small networks is that FC networks add a parameter between every two neurons in the successive layers. Rather than assigning a single parameter between...

    https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html

  • Data Science Data Architecture

    …availability in multiple aspects: The daily business of the data scientists takes place on this platform, and it not being available stops any model development. The model development environment, over time, will contain a great deal of (analytical) assets, and in that sense, it cannot be…

    https://www.kdnuggets.com/2015/09/data-science-data-architecture.html

  • A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

    ...f the past output to keep, how much of the current input to keep, and how much of the internal state to send out to the output. In a recurrent neural network, you not only give the network the data, but also the state of the network one moment before. For example, if I say “Hey! Something crazy...

    https://www.kdnuggets.com/2017/10/guide-time-series-prediction-recurrent-neural-networks-lstms.html

  • A Neural Network in 11 lines of Python

    ...distributed, but they'll be randomly distributed in exactly the same way each time you train. This makes it easier to see how your changes affect the network. Line 23: This is our weight matrix for this neural network. It's called "syn0" to imply "synapse zero". Since we only have 2 layers (input...

    https://www.kdnuggets.com/2015/10/neural-network-python-tutorial.html

  • Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas

    ...eses (i.e., null hypothesis, null hypothesis). I am going to use this blog to provide more details and some instructions on the use of the Hypothesis Development Canvas. I will provide an example Hypothesis Development Canvas for our University of San Francisco Big Data MBA in-class Chipotle...

    https://www.kdnuggets.com/2018/11/data-science-activities-business-initiatives-hypothesis-development-canvas.html

  • Gold Mine or Blind Alley? Functional Programming for Big Data & Machine Learning

    ...s of its opposite, imperative programming, and introduces a few new ideas, several of which have been subsequently adopted by many popular imperative programming languages. What You Can Do First-Order Functions Functional programming supports first order functions. These functions can be passed as...

    https://www.kdnuggets.com/2015/04/functional-programming-big-data-machine-learning.html

  • Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills">Silver BlogModern Data Science Skills: 8 Categories, Core Skills, and Hot Skills

    ...% 11.6% 1.5 The most common categories among emerging Data Science skills are: Big Data & Cloud, 8 Deep Learning, 7 Data Science & ML Tools 3 Programming Lang, 3 Software Development, 3 The remaining skills are those where the demand is not growing strongly Want/Have is < 1.2 and the...

    https://www.kdnuggets.com/2020/09/modern-data-science-skills.html

  • How to Create a Simple Neural Network in Python">Gold BlogHow to Create a Simple Neural Network in Python

    ...pe(float) output = self.sigmoid(np.dot(inputs, self.synaptic_weights)) return output if __name__ == "__main__": #initializing the neuron class neural_network = NeuralNetwork() print("Beginning Randomly Generated Weights: ") print(neural_network.synaptic_weights) #training data consisting of 4...

    https://www.kdnuggets.com/2018/10/simple-neural-network-python.html

  • Attention Craving RNNS: Building Up To Transformer Networks

    ...t beam search will give you better results most of the time. Attention is optional! BUT… the impact is huge when you have it… Attention is a separate network… Think about the network as the dictionary, where the key is a collection of things you want the network to use in deciding how relevant each...

    https://www.kdnuggets.com/2019/04/attention-craving-rnn-building-transformer-networks.html

  • Achieving End-to-end Security for Apache Spark with Databricks

    ...flows, application deployments, dashboards, to reports. The Databricks just-in-time data platform takes a holistic approach to solving the enterprise security challenge by building all the facets of security — encryption, identity management, role-based access control, data governance, and...

    https://www.kdnuggets.com/2016/06/achieving-security-apache-spark-databricks.html

  • Singapore Data Analytics, Info Security careers

    ...rategist, APJ Senior Malware Researcher Threat Assessment Manager Consultant, Incident Response and Forensics   Associate Partner - Asia Pacific Security Practice Security Architect (Asia Pacific) Security Consultant Senior Software Test Engineer Security Consultant Cyber Security Consultant...

    https://www.kdnuggets.com/2015/11/singapore-data-analytics-info-security-careers.html

  • Object-oriented programming for data scientists: Build your ML estimator">Gold BlogObject-oriented programming for data scientists: Build your ML estimator

    ...fantastic article, which drills down to the concept of OOP in Python in more detail with a context of machine learning. Understanding Object-Oriented Programming Through Machine Learning Object-Oriented Programming (OOP) is not easy to wrap your head around. You can read tutorial after tutorial and...

    https://www.kdnuggets.com/2019/08/object-oriented-programming-data-scientists-estimator.html

  • Deep Learning Research Review: Reinforcement Learning

    ...s through a series of conv layers to construct a good representation of the current state. So let’s first look at our SL (Supervised Learning) policy network. This network is going to take in the image as input and then output a probability distribution over all of the legal actions the agent can...

    https://www.kdnuggets.com/2016/11/deep-learning-research-review-reinforcement-learning.html

  • Deep Neural Networks

    ...nd algorithms of solutions, and the deep neural network can solve a problem without a significant amount of marked data. What Is Deep Learning Neural Network? The neural network needs to learn all the time to solve tasks in a more qualified manner or even to use various methods to provide a better...

    https://www.kdnuggets.com/2020/02/deep-neural-networks.html

  • Interview: Marc Smith, Chief Social Scientist, Connected Action, on Why We Need Open Tools for Social Networks

    ...L? What are some of the most memorable success stories you have heard so far from NodeXL users? Marc Smith: NodeXL is for anyone who is interested in networks, social networks and particularly social media networks. Our users are often scholars, researchers, students, managers, and analysts who are...

    https://www.kdnuggets.com/2014/07/interview-marc-smith-connected-action-social-networks.html

  • First Steps of Learning Deep Learning: Image Classification in Keras

    ...ithms by Sebastian Ruder Picking an optimizer for Style Transfer by Slav Ivanov Building Autoencoders in Keras by Francois Chollet Understanding LSTM Networks by Chris Olah Recurrent Neural Networks & LSTMs by Rohan Kapur Oxford Deep NLP 2017 course List of resources How to Start Learning Deep...

    https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html

  • Only Numpy: Implementing GANs and Adam Optimizer using Numpy">Silver BlogOnly Numpy: Implementing GANs and Adam Optimizer using Numpy

    ...data before putting them both into the network. Line 128 — Getting the Real Image Data Line 147 — Getting the Fake Image Data (Generated By Generator Network) Line 162 — Cost Function of our Discriminator Network. Also, please take note of the Blue Box Region, that is our cost function. Lets...

    https://www.kdnuggets.com/2018/08/only-numpy-implementing-gans-adam-optimizer.html

  • A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">Gold BlogA Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

    ...231N course taught by Andrej Karpathy and Justin Johnson. Recommend for anyone looking for a deeper understanding of CNNs.) Going Deeper Through the Network   Now in a traditional convolutional neural network architecture, there are other layers that are interspersed between these conv...

    https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html

  • The 10 Deep Learning Methods AI Practitioners Need to Apply

    ...pout Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large...

    https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.html

  • What Do Frameworks Offer Data Scientists that Programming Languages Lack?

    ...algorithms as part of the framework is much safer and more effective than trying to tinker with them as part of the language. Frameworks have been in development for years, meaning they are tried, tested, and true -- a combination of thought and experimentation from the best programming minds....

    https://www.kdnuggets.com/2017/05/frameworks-offer-data-scientists-programming-languages-lack.html

  • The Most Important Fundamentals of PyTorch you Should Know">Silver BlogThe Most Important Fundamentals of PyTorch you Should Know

    ...heart of a neural net and customizing it for your application or trying out bold new ideas with the architecture, optimization, and mechanics of the network. You can easily build complex interconnected networks, try out novel activation functions, mix and match custom loss functions, etc. The core...

    https://www.kdnuggets.com/2020/06/fundamentals-pytorch.html

  • Implementing Neural Networks in Javascript

    ...t hiddenLayer = new Layer( 100 ) ; const outputLayer = new Layer( 10 ) ; inputLayer.project(hiddenLayer) ; hiddenLayer.project(outputLayer) ; const myNetwork = new Network( { input : inputLayer, hidden: [hiddenLayer] , output: outputLayer } ) ; To train the network with our training set, we can use...

    https://www.kdnuggets.com/2016/05/implementing-neural-networks-javascript.html

  • 37 Reasons why your Neural Network is not working">Silver Blog, Aug 201737 Reasons why your Neural Network is not working

    …“frozen” layers or variables Check if you unintentionally disabled gradient updates for some layers/variables that should be learnable. 24. Increase network size Maybe the expressive power of your network is not enough to capture the target function. Try adding more layers or more hidden units in…

    https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html

  • A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

    ...viate the overfitting problem. An important note is that this layer is only used during training, and not during test time. Paper by Geoffrey Hinton. Network in Network Layers   A network in network layer refers to a conv layer where a 1 x 1 size filter is used. Now, at first look, you might...

    https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

  • Neural network AI is simple. So… Stop pretending you are a genius">Platinum BlogNeural network AI is simple. So… Stop pretending you are a genius

    ...lse/8-ai-technologies-aint-neural-networks-brandon-wirtz/   Bio: Brandon Wirtz is CEO and Founder at Recognant. Original. Reposted with permission. Related: Using Genetic Algorithm for Optimizing Recurrent Neural Networks A Simple Starter Guide to Build a Neural Network The 8 Neural Network...

    https://www.kdnuggets.com/2018/02/neural-network-ai-simple-genius.html

  • Designing Your Neural Networks

    ...l of which might be fully connected. For these use cases, there are pre-trained models (YOLO, ResNet, VGG) that allow you to use large parts of their networks, and train your model on top of these networks to learn only the higher-order features. In this case, your model will still have only a few...

    https://www.kdnuggets.com/2019/11/designing-neural-networks.html

  • Inside the Mind of a Neural Network with Interactive Code in Tensorflow

    ...without mean pooling Orange Rectangle → Softmax for classification As usual we are going to use the CIFAR 10 data set to train our All Convolutional Network and try to see why the network have predicted certain image into it’s class. And one thing to note, since this post is more about getting to...

    https://www.kdnuggets.com/2018/06/inside-mind-neural-network-interactive-code-tensorflow.html

  • Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry.

    ...sults on a short-term basis.   Major differences between development and research   Let’s compare the development workflow for both fields. Programming: In software development, you organize the code into functions, classes (i.e., object-oriented-programming), and may use design-patterns...

    https://www.kdnuggets.com/2019/02/data-science-agile-cycles-method-managing-projects-hi-tech-industry.html

  • Connecting Data Systems and DevOps

    ...ay to put into practice some of the same organizational principles espoused by agile methodology (collaboration, enablement, and a focus on iterative development cycles). The goal is to have operations, development, and QA capabilities work closely together (often using the same tools) throughout...

    https://www.kdnuggets.com/2016/06/connecting-data-systems-devops.html

  • Why Learn Python? Here Are 8 Data-Driven Reasons

    ...eas and functions. The major Python Language application areas are: Web development System automation and administration Computer graphics Basic game development Security and penetration testing Data science Finance and trading Scientific and mathematical computing General and application-specific...

    https://www.kdnuggets.com/2020/07/learn-python-8-data-driven-reasons.html

  • Writing Your First Neural Net in Less Than 30 Lines of Code with Keras

    ...ssentropy', metrics=['accuracy']) I know… I know… it might seem like a lot, but let’s break it down together! We initialize a sequential model called network. network = models.Sequential() And we add our NN layers. For this example, we will be using dense layers. A dense layer simply means that...

    https://www.kdnuggets.com/2019/10/writing-first-neural-net-less-30-lines-code-keras.html

  • Generative Adversarial Networks, an overview

    ...low, and output layer in red. A brief review of Deep Learning Let’s begin with a brief overview of deep learning. Above, we have a sketch of a neural network. The neural network is made of up neurons, which are connected to each other using edges. The neurons are organized into layers – we have the...

    https://www.kdnuggets.com/2018/01/generative-adversarial-networks-overview.html

  • 60+ useful graph visualization libraries">Silver Blog60+ useful graph visualization libraries

    ...isualization library released under the Apache 2.0 License. The library enable manipulation of and interaction with large amounts of dynamic data. visNetwork : VisNetwork is a Proprietary R package, using vis.js library for network visualization. VivaGraphJS : a graph drawing library for JavaScript...

    https://www.kdnuggets.com/2019/05/60-useful-graph-visualization-libraries.html

  • Neural Network Software for Classification

    ...Neural networks, comprehensive and user-friendly nn application with many charting options, network architectures and training algorithms. Synapse, a development environment for neural networks and other adaptive systems, supporting the entire development cycle from data import and preprocessing...

    https://www.kdnuggets.com/software/classification-neural.html

  • Data Science Project Flow for Startups

    ...a country-wise model down to a per-region model, or to compose several such models into a per-continent model), though many more exist.   3. The Development Phase   3.1. Model development & experiments framework setup The amount and complexity of setup required for model development...

    https://www.kdnuggets.com/2019/01/data-science-project-flow-startups.html

  • Consulting Companies in AI, Analytics, Data Science, and Machine Learning

    ...ain; and Zurich, Switzerland. Technatomy Corporation, providing data warehousing, knowledge management, decision-support systems, and custom software development. Manassas, VA, USA. The APP Solutions provides full-cycle AI/ML development & consulting services for startups and small- medium-sized...

    https://www.kdnuggets.com/companies/consulting.html

  • 35 Open Source tools for Internet of Things

    ...ral awards, and it has a companion cloud computing service called my.openHAB. 18. The Thing System This project includes both software components and network protocols. It promises to find all the Internet-connected things in your house and bring them together so that you can control them. It...

    https://www.kdnuggets.com/2016/07/open-source-tools-internet-things.html

  • Automating Every Aspect of Your Python Project">Gold BlogAutomating Every Aspect of Your Python Project

    ...he best Docker base image for your Python application Google Distroless Scan Your Docker Images for Vulnerabilities 5 open source tools for container security SonarCloud GitHub Action   Bio: Martin Heinz is a DevOps Engineer at IBM. A software developer, Martin is passionate about computer...

    https://www.kdnuggets.com/2020/09/automating-every-aspect-python-project.html

  • Data Science for Managers: Programming Languages">Silver BlogData Science for Managers: Programming Languages

    ...agement, so R can consume all the available memory. R is slow. However, are developed multiple packages to improve R’s performance. R has no built-in security. R can’t be used as a back-end server to do calculations as it is lacking in security over the Web. Top 20 R Libraries for Data Science in...

    https://www.kdnuggets.com/2019/11/data-science-managers-programming-languages.html

  • How to do Everything in Computer Vision

    ...tains confidence values for each image pixel about whether a keypoint likely exists there or not (3) Again given the features from the classification network, we train a sub-network to predict a set of 2D vector fields, where each vector field encodes the degree of association between the...

    https://www.kdnuggets.com/2019/02/everything-computer-vision.html

  • Build an Artificial Neural Network From Scratch: Part 2

    ...nbsp; In my previous article, Build an Artificial Neural Network(ANN) from scratch: Part-1 we started our discussion about what are artificial neural networks; we saw how to create a simple neural network with one input and one output layer, from scratch in Python. Such a neural network is called a...

    https://www.kdnuggets.com/2020/03/build-artificial-neural-network-scratch-part-2.html

  • Checklist for Debugging Neural Networks

    ...fficult to debug with bugs that are expensive to chase. Even for simple, feedforward neural networks, you often have to make several decisions around network architecture , weight initialization, and network optimization — all of which can lead to insidious bugs in your machine learning code. As...

    https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html

  • How Do Neural Networks Learn?

    ...activation function, and so on until we get to the last function in our sequence. The output of this last function will be the predicted value of our network. We have discussed so far how a neural network gets its output, which we are interested in, it just passes its input vector through a...

    https://www.kdnuggets.com/2020/08/how-neural-networks-learn.html

  • Design by Evolution: How to evolve your neural network with AutoML

    ...he problem we might require different limitations, for example the total number of parameters, or total number of layers or FLOPs per cycle. mutate a network: Each network element has been assigned a probability of mutation. Each mutation will alter the parameter by resampling the parameter space....

    https://www.kdnuggets.com/2017/07/design-evolution-evolve-neural-network-automl.html

  • A 2019 Guide for Automatic Speech Recognition

    ...model to predict future samples from a single context. The model takes a raw audio signal as input and then applies an encoder network and a context network. The encoder network embeds the audio signal in a latent space, and the context network combines multiple time-steps of the encoder to obtain...

    https://www.kdnuggets.com/2019/09/2019-guide-automatic-speech-recognition.html

  • Understanding Transformers, the Data Science Way

    ...k applies itself to each position in the output Z parallelly(Each position can be thought of as a word) and hence the name Position-wise feed-forward network. The feed-forward network also shares weight, so that the length of the source sentence doesn’t matter(Also, if it didn’t share weights, we...

    https://www.kdnuggets.com/2020/10/understanding-transformers-data-science-way.html

  • The Rise of Generative Adversarial Networks

    ...ction of this article. The Birth Generative Adversarial Network or GAN for short is a setup of two networks, a generator network, and a discriminator network. These two networks can be neural networks, ranging from convolutional neural networks, recurrent neural networks to auto-encoders. In this...

    https://www.kdnuggets.com/2019/04/rise-generative-adversarial-networks.html

  • Torus for Docker-First Data Science

    ...ineer (MLE). At a high level, MLEs have the same set of challenges as any software engineer working in a product development team: Standardized local development environments Development vs. production environment parity Standardized packaging and deployment pipelines In addition, certain aspects...

    https://www.kdnuggets.com/2018/05/torus-docker-first-data-science.html

  • Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Silver BlogDeep Learning for NLP: ANNs, RNNs and LSTMs explained!

    ...be stacked on top of each other forming layers of the size that we want, and then these layers can be sequentially put next to each other to make the network deeper. When networks are built in this way, the neurons that don’t belong to the input or output layers are considered part of the hidden...

    https://www.kdnuggets.com/2019/08/deep-learning-nlp-explained.html

  • Deep Learning Reading Group: Deep Residual Learning for Image Recognition

    ...ed these training issues, and yet the networks still perform increasingly poorly as their depth increases. For example, they compare 20- and 56-layer networks and find the 56-layer network performs far worse; see the image below from their paper. Comparison of 20- and 56-layer networks on CIFAR-10....

    https://www.kdnuggets.com/2016/09/deep-learning-reading-group-deep-residual-learning-image-recognition.html

  • A Gentle Introduction to Noise Contrastive Estimation

    ...pi term divides by the same denominator, which itself is a sum over the entire vocabulary. This makes our loss function depend on every output in the network, when means every network parameter will have a non-zero gradient and therefore needs updating for every training example. There has to be a...

    https://www.kdnuggets.com/2019/07/introduction-noise-contrastive-estimation.html

  • A Simple Starter Guide to Build a Neural Network">Silver BlogA Simple Starter Guide to Build a Neural Network

    ...ta order, but the order of test_loader remains to examine whether we can handle unspecified bias order of inputs.   Build the Feedforward Neural Network   Now we have our datasets ready. We will start building the neural network. The conceptual illustration can be viewed as below: FNN...

    https://www.kdnuggets.com/2018/02/simple-starter-guide-build-neural-network.html

  • Semantic Segmentation Models for Autonomous Vehicles

    ...hitecture The SegNet architecture adopts the VGG16 network along with an encoder-decoder framework wherein it drops the fully connected layers of the network. The decoder sub-network is a mirror copy of the encoder sub-network, both containing 13 layers. Figure 3(B) shows how SegNet and FCN carry...

    https://www.kdnuggets.com/2018/03/semantic-segmentation-models-autonomous-vehicles.html

  • Everything You Need to Know About AutoML and Neural Architecture Search

    ...The NAS algorithm It’s a fairly intuitive approach! In simple terms: have an algorithm grab different blocks and put those blocks together to make a network. Train and test out that network. Based on your results, adjust the blocks you used to make the network and how you put them together! Part...

    https://www.kdnuggets.com/2018/09/everything-need-know-about-automl-neural-architecture-search.html

  • Deep Learning Research Review: Generative Adversarial Nets

    ...is passionate about applying his knowledge of machine learning and computer vision to areas in healthcare where better solutions can be engineered for doctors and patients. Original. Reposted with permission. Related: A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1 Deep...

    https://www.kdnuggets.com/2016/10/deep-learning-research-review-generative-adversarial-networks.html

  • A 2019 Guide to Human Pose Estimation

    ...16)   This paper argues that repeated bottom-up and top-down processing with intermediate supervision improves the performance of their proposed network. The network is referred to as a “stacked hourglass” because of the successive processes of polling and upsampling that are performed to...

    https://www.kdnuggets.com/2019/08/2019-guide-human-pose-estimation.html

  • How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1">Gold BlogHow to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1

    ...ltiple images into a large batch (concatenating many PyTorch tensors into one) The network downsamples the image by a factor called the stride of the network. For example, if the stride of the network is 32, then an input image of size 416 x 416 will yield an output of size 13 x 13. Generally,...

    https://www.kdnuggets.com/2018/05/implement-yolo-v3-object-detector-pytorch-part-1.html

  • Workforce Data Science: Does Talent Development Increase Performance Over Time?

    …ts referenced here, analytics results showed that sales rep performance did not measurably increase over time – despite multi-millions being spent on development efforts including: training, coaching, competency development and the like. Figure 1: Performance Level over time for Top 5%, Top 25%,…

    https://www.kdnuggets.com/2015/09/data-science-talent-development-performance.html

  • Does Machine Learning Have a Future Role in Cyber Security?

    ...ing suspicious activity, including the ability to detect attacks that have never been seen before. Amit Mital's opinion is more interesting. Is cyber security broken? I’m not a security expert, so I can’t comment on the technical aspects of security, but I can look at the facts. Every day major...

    https://www.kdnuggets.com/2017/06/machine-learning-future-role-cyber-security.html

  • 5 Best Practices for Big Data Security

    …with the increasing demand. Periodic audits will help you to identify new vulnerabilities as they make their presence felt. Thus you can realign your security compliance with the current security standards. Bio: Olivia Young, a tech enthusiast, geek and writer. She is particularly interested in the…

    https://www.kdnuggets.com/2016/06/5-best-practices-big-data-security.html

  • Machine Learning Security

    ...them. In the same way that we take precautions in our web apps to protect our systems against malicious users, we should also be proactive with model security risk. Just as institutions have Application Security Review groups that do e.g. penetration testing of software, we will need to build Model...

    https://www.kdnuggets.com/2019/01/machine-learning-security.html

  • The Perpetual Quest for Digital Trust

    ...e very core of IoT’s anticipated success. The report concludes with the following three action items towards building Digital Trust: Nominate a Chief Security Officer Evaluate product and service security risks – including those of your business partners Use proactive product testing methods Survey...

    https://www.kdnuggets.com/2015/07/perpetual-quest-digital-trust.html

  • Top KDnuggets tweets, Jan 19-20: 15 programming languages you need to know in 2015; R Programming fun: writing a Twitter bot

    ...Simple Pictures that State-of-the-Art #AI Can't Recognize (yet) #Vision #DeepLearning t.co/OsUgXAjS8C t.co/4EHQybz6Wj Top 10 most engaging Tweets 15 #programming languages you need to know in 2015 - #Java #PHP #C++ #Python #SQL #R t.co/ZcScPzuevS #rstats t.co/E1PCvEUT7G #Facebook open sources its...

    https://www.kdnuggets.com/2015/01/top-tweets-jan19-20.html

  • Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search

    ...process is repeated till termination.   One-Shot Models   We define an architecture search method as one-shot if it trains a single neural network during the search process. This neural network is then used to derive architectures throughout the search space as candidate solutions to the...

    https://www.kdnuggets.com/2019/10/using-neural-networks-design-neural-networks-definitive-guide-understand-neural-architecture-search.html

  • Introduction to Convolutional Neural Networks

    ...at converts the pooled feature map to a single column that is passed to the fully connected layer. Dense adds the fully connected layer to the neural network. Once the network is built, then compile/train the network using Stochastic Gradient Descent(SGD). Gradient Descent works fine when we have a...

    https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html

  • Deep Learning Reading Group: Deep Networks with Stochastic Depth

    ...time. The authors also show that it reduces the problems associated with vanishing gradients and diminishing feature use, as expected for a shallower network. An example training run on a network with stochastic depth. The red and blue bars indicate the probability of dropping a layer, p(l). In...

    https://www.kdnuggets.com/2016/09/deep-learning-reading-group-stochastic-depth-networks.html

  • Internet of Things Tutorial: WSN and RFID – The Forerunners

    ...algorithms for the mobility of the sensor nodes, functionalities for turning sensor nodes on and off, functionalities for monitoing the status of the network (such as the sensor network configuration), as well as the ever importance authentication and secure data communications functionalities. WSN...

    https://www.kdnuggets.com/2017/01/internet-of-things-tutorial-chapter-2-wsn-rfid-forerunners.html

  • The 4 Hottest Trends in Data Science for 2020">Silver BlogThe 4 Hottest Trends in Data Science for 2020

    ...ers to give them more data (by continuing to use their products and services). It also ensures that, should their government enact any laws requiring security protocols for customer data, they are already well-prepared. Many companies are opting for SOC 2 Compliance to have some proof of the...

    https://www.kdnuggets.com/2019/12/4-hottest-trends-data-science-2020.html

  • 10 Steps for Tackling Data Privacy and Security Laws in 2020

    ...s a degree of Computer Science from Iqra University and specializes in Information Security & Data Privacy. Related: Privacy-preserving AI – Why do we need it? Analyzing GDPR Fines – who are largest violators? Applying Data Science to Cybersecurity Network Attacks & Events...

    https://www.kdnuggets.com/2020/07/10-steps-data-privacy-security-laws.html

  • Interview: Anil Gadre, MapR on 3 Keys for Big Data Success: Reliability, Security, & Scalability

    ...ion. That led to creating the Apache open source Project Myriad with the support of community members. AR: Q6. What are your thoughts on the state of security in enterprise implementations of Big Data solutions? Is security getting the appropriate attention? Or are most of the companies still...

    https://www.kdnuggets.com/2015/06/interview-anil-gadre-mapr-big-data-success.html

  • Big Data: Main Developments in 2017 and Key Trends in 2018">Silver BlogBig Data: Main Developments in 2017 and Key Trends in 2018

    ...ely parallel training of the AI models that are pushed down to the edges. What key trends do you see in 2018? In 2018, we'll see the data-science/app-development community converge on an open AI development framework that wraps a common abstraction layer around modeling tools such as TensorFlow,...

    https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

  • Summer School: Constraint Programming Data Mining, Sicily

    ...e hand, and constraint programming and optimization on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution...

    https://www.kdnuggets.com/2014/06/summer-school-constraint-programming-data-mining-sicily.html

  • Platinum Blog10 Great Python Resources for Aspiring Data Scientists">Silver BlogPlatinum Blog10 Great Python Resources for Aspiring Data Scientists

    ...ith. Not to mention it is open-source, interpreted, high-level tool!   10. Why Python is Essential for Data Analysis Python is a general purpose programming language, meaning it can be used in the development of both web and desktop applications. It’s also useful in the development of complex...

    https://www.kdnuggets.com/2019/09/10-great-python-resources-aspiring-data-scientists.html

  • Best Data Science Online Courses

    …b Apps in R with Shiny $119 Data Mining with R: Go from Beginner to Advanced! $99 Applied Multivariate Analysis with R $99 SAS Course Title Price SAS programming for beginners $19 Clinical SAS Programming(CDISC) $250 Certified SAS Base Programmer $299 Advanced SAS $29 Logistic Regression (Credit…

    https://www.kdnuggets.com/2015/10/best-data-science-online-courses.html

  • Dissecting the Big Data Twitter Community through a Big data Lens

    …retweets graph is a good representation of actual connections in the network, their strengths, as well as the propagation of information through the network. The Network This post, therefore, will focus on the retweets graph. The following graph shows a visualization of the retweets graph where…

    https://www.kdnuggets.com/2015/09/dissecting-big-data-twitter-community.html

  • Graph Machine Learning in Genomic Prediction

    ...aph-structured data for trait prediction?   Trait prediction from graph   GraphSAGE [1], belonging to a class of Graph Convolutional Neural Networks, is a neural network that when applied to a graph will learn to produce such latent vector representations — also called “embeddings” — for...

    https://www.kdnuggets.com/2020/06/graph-machine-learning-genomic-prediction.html

  • Artificial Neural Network Implementation using NumPy and Image Classification">Gold BlogArtificial Neural Network Implementation using NumPy and Image Classification

    ...ed. For example, if the size of the "input_HL1_weights" variable is 102x80, then we can deduce that the first hidden layer has 80 neurons. The "train_network" is the core function as it trains the network by looping through all samples. For each sample, the steps discussed in listing 3-6 are...

    https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html

  • Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Silver BlogConvolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

    ...gression). Today we’ll focus on the first item of the list, though each of those deserves an article of its own.   What are Convolutional Neural Networks?   In MultiLayer Perceptrons (MLP), the vanilla Neural Networks, each layer’s neurons connect to all the neurons in the next layer. We...

    https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

  • A 2019 Guide to Object Detection

    ...g a single deep neural network. Our approach, named SSD...   This paper presents a model to predict objects in images using a single deep neural network. The network generates scores for the presence of each object category using small convolutional filters applied to feature maps. source...

    https://www.kdnuggets.com/2019/08/2019-guide-object-detection.html

  • Looking Inside The Blackbox: How To Trick A Neural Network

    ..., with certain types of inputs (images, sound, video, etc…) explainability certainly becomes much harder but not impossible.   Asking the neural network   How would a neural network answer the same questions I posed above? Well, to answer that, we can use gradient ascent to do exactly...

    https://www.kdnuggets.com/2020/09/inside-blackbox-trick-neural-network.html

  • Data Augmentation: How to use Deep Learning when you have Limited Data

    ...do I get more data, if I don’t have “more data”? You don’t need to hunt for novel new images that can be added to your dataset. Why? Because, neural networks aren’t smart to begin with. For instance, a poorly trained neural network would think that these three tennis balls shown below, are...

    https://www.kdnuggets.com/2018/05/data-augmentation-deep-learning-limited-data.html

  • Random Forests® vs Neural Networks: Which is Better, and When?">Silver BlogRandom Forests® vs Neural Networks: Which is Better, and When?

    ...redicts the output label (in case of classification). Decision trees in the ensemble are independent. Each can predict the final response. The Neural Network is a network of connected neurons. The neurons cannot operate without other neurons - they are connected. Usually, they are grouped in layers...

    https://www.kdnuggets.com/2019/06/random-forest-vs-neural-network.html

  • Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks

    ...function - We will use the cross-entropy loss function in our network. Weight initialization - We will randomly set the initial random weights of our network layer neurons. Below is what our network will ultimately look like.   Figure 3. Visualization of network layers. The Code   Here is...

    https://www.kdnuggets.com/2017/09/neural-networks-tic-tac-toe-keras.html

  • Recurrent Neural Networks Tutorial, Introduction

    …ling we simply mean that we write out the network for the complete sequence. For example, if the sequence we care about is a sentence of 5 words, the network would be unrolled into a 5-layer neural network, one layer for each word. The formulas that govern the computation happening in a RNN are as…

    https://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html

  • Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation

    ...ghts Sources:   [1] Vector Calculus: Understanding the Gradient   [2] Gradient Descent (and Beyond)   [3] Find Limits of Functions in Calculus   Related: Neural Network Foundations, Explained: Activation Function Deep Learning and Neural Networks Primer: Basic...

    https://www.kdnuggets.com/2017/10/neural-network-foundations-explained-gradient-descent.html

  • Interview: Emmanuel Letouzé, Data-Pop Alliance on the Role of Big Data in Economic Development

    ...at UC Berkeley, writing his dissertation on Big Data and demographic research. Emmanuel is the author of UN Global Pulse's White Paper "Big Data for Development" (2012), the lead author of the 2013 and 2014 OECD Fragile States reports and a regular contributor on Big Data and development. He...

    https://www.kdnuggets.com/2015/04/interview-emmanuel-letouze-big-data-economic-development.html

  • Bridgepoint Education: Director of Advanced Analytics

    ...out the department strategic plan.   Acquisition & Deployment: Conduct research and make recommendations on data mining products, services, protocols, and standards in support of procurement and development efforts.   Operational Management: Direct and lead the development of...

    https://www.kdnuggets.com/jobs/14/11-22-bpiedu-director-advanced-analytics.html

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