Search results for Long Short Term Memory Network Development Programming

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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

    ..., too. But in practice, it does not seem to have much of an effect on the performance of a neural network. Perhaps this is because the number of bias terms in a neural network is much fewer than the weights. The type of neural network we created here is called a “fully-connected feedforward...

    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

  • Deep Learning Research Review: Natural Language Processing">Silver Blog, 2017Deep Learning Research Review: Natural Language Processing

    ...omputing our hidden state vectors in RNNs. This approach will allow us to keep information that capture long distance dependencies. Let’s imagine why long term dependencies would be a problem in the traditional RNN setup. During backpropagation, the error will flow through the RNN, going from the...

    https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

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

    ...Tree Kernels, Recursive neural network, and CNN. It was shown that their model outperforms traditional methods for all used data sets [8].   5. Long short-term memory (LSTM)   A peephole LSTM block with input, output, and forget gates....

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

  • Modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models

    ...n dataset test data the % of term.deposit cases in the selection is 47.2%. ## - When we select ntiles 1 until 20 according to model artificial neural network in dataset test data the % of term.deposit cases in the selection is 40.7%. ## ##   Seems like the algorithm used will not make a big...

    https://www.kdnuggets.com/2019/06/modelplotr-cran-business-value-predictive-models.html

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

    ...input from many time-steps ago, so RNNs have difficulty dealing with long-range dependencies. There are essentially 4 effective ways to learn a RNN: Long Short Term Memory: Make the RNN out of little modules that are designed to remember values for a long time. Hessian Free Optimization: Deal with...

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

  • 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

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

    ...tructures, amazing results can be obtained. In this post we will learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and Long-Short Term Memory Networks. In the next post we will use them on a real project to make a question answering bot. Before we start with all the...

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

  • Attention and Memory in Deep Learning and NLP

    ...gating mechanism that allows for explicit memory deletes and updates. The trend towards more complex memory structures is now continuing. End-to-End Memory Networks allow the network to read same input sequence multiple times before making an output, updating the memory contents at each step. For...

    https://www.kdnuggets.com/2016/01/attention-memory-deep-learning-nlp.html

  • How Bayesian Inference Works">Gold BlogHow Bayesian Inference Works

    ...heir partners company. Half the women still have long hair and half have short hair, but here there are just one of each. The proportions of men with long and short hair are the same too, but since there are 98 of them, there are now 94 with short hair and 4 with long. Since there is 1 woman with...

    https://www.kdnuggets.com/2016/11/how-bayesian-inference-works.html

  • Research Guide for Video Frame Interpolation with Deep Learning

    ...and Structural Similarity Index (SSIM) for analyzing the quality of the interpolated image. Below are the results they achieved. source     Long-Term Video Interpolation with Bidirectional Predictive Network (2017)   This paper addresses the challenge of generating multiple frames...

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

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

    ...cessing, the same approach can be used, given that input (words, sentences, etc.) could be arranged in matrices and processed in similar fashion. 14. Long Short Term Memory Network (LSTM) Credit: Christopher Olah A Long Short Term Memory Network (LSTM) is a recurrent neural network which is...

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

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

    ...dy published the article about using time series analysis for anomaly detection. Today, we’d like to discuss time series prediction with a long short-term memory model (LSTMs). We asked a data scientist, Neelabh Pant, to tell you about his experience of forecasting exchange rates using recurrent...

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

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

    ...xplain the most relevant parts in the following lines. This paper implements an RNN like structure that uses an attention model to compensate for the long term memory issue about RNNs that we discussed in the previous post. Don’t know what an attention model is? Do not worry, I will explain it in...

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

    ...ak it Try different cost functions Use more FC layers Introduce more aggressive dropout If you're interested in having better results with pretrained networks: Use different network architectures Use more FC layers with more hidden units If you want to do discover what the CNN model has learnt:...

    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

  • 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

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

    ...d research workflow approaches and finally suggest my work methodology.   Types of research   We usually encounter three types of research: Long-term, in academia and companies such as IBM or FACEBOOK, i.e., research that advances science or technology. Medium-term, i.e., strategic...

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

  • Sequence Modeling with Neural Networks – Part I

    ...networks can’t do this, and it seems like a major shortcoming. Bag-of-words and bag-of-n-grams as text representations do not allow to keep track of long-term dependencies inside the same sentence or paragraph. Another disadvantage of modeling sequences with traditional Neural Networks (e.g....

    https://www.kdnuggets.com/2018/10/sequence-modeling-neural-networks-part-1.html

  • How AI will transform healthcare (and can it fix the US healthcare system?)">Silver BlogHow AI will transform healthcare (and can it fix the US healthcare system?)

    ...ll see these Machine Learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work." Clinical Trials and remote monitoring Whilst the pre-clinical stage of drug discovery is estimated to account...

    https://www.kdnuggets.com/2019/09/ai-transform-healthcare.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

    …chmark tasks. Check out the Torch implementation here. Conclusion In this post, I have tried to explain the main concepts behind Convolutional Neural Networks in simple terms. There are several details I have oversimplified / skipped, but hopefully this post gave you some intuition around how they…

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

  • Evaluating the Business Value of Predictive Models in Python and R

    ...om forest'], select_dataset_label = ['test data']) No comparison specified! Single evaluation line will be plotted The label with smallest class is ['term deposit'] Target value term deposit plotted for dataset test data and model random forest. What just happened? In the modelplotpy a class is...

    https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html

  • The 10 Deep Learning Methods AI Practitioners Need to Apply

    ...image recognition with convolutions, natural language processing with embeddings and character based text generation with Recurrent Neural Network / Long Short-Term Memory. All the code in Jupiter Notebook can be found on this GitHub repository. Here is an outcome of one of the assignments, a...

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

  • KDnuggets Interview with Jon Kleinberg, revisited

    …who’s a historian as a glimpse into something of that field’s view of time and temporal processes at a relatively abstract level. This is part of my longterm interest in trying to think about the role of time in complex data; since much of the challenge here lies in finding the right structures…

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

  • KDnuggets Interview with Jon Kleinberg, revisited

    ...who's a historian as a glimpse into something of that field's view of time and temporal processes at a relatively abstract level. This is part of my long-term interest in trying to think about the role of time in complex data; since much of the challenge here lies in finding the right structures...

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

  • Deep Learning for NLP: An Overview of Recent Trends">Silver BlogDeep Learning for NLP: An Overview of Recent Trends

    ...’s important to understand that even though both character-level and word-level embeddings have been successfully applied to various NLP tasks, there long-term impact have been questioned. For instance, Lucy and Gauthier recently found that word vectors are limited in how well they capture the...

    https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

  • Recurrent Neural Networks Tutorial, Introduction

    …reat success in many NLP tasks. At this point I should mention that the most commonly used type of RNNs are LSTMs, which are much better at capturing longterm dependencies than vanilla RNNs are. But don’t worry, LSTMs are essentially the same thing as the RNN we will develop in this tutorial, they…

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

  • Knowing Your Neighbours: Machine Learning on Graphs">Gold 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!

    ...ly obtaining his Phd from the University of Oregon, and enjoys writing about AI in his free time Original. Reposted with permission. Related: Deep Learning Research Review: Reinforcement Learning An Intuitive Explanation of Convolutional Neural Networks Shortcomings of Deep Learning...

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

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

    ...es some non-linear function to that result. The output of a layer’s neurons, a new vector, is fed to the next layer, and so on. Source   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....

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

  • Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices

    ...redict the future behavior of stock prices. We assume that the reader is familiar with the concepts of deep learning in Python, especially Long Short-Term Memory. While predicting the actual price of a stock is an uphill climb, we can build a model that will predict whether the price will go up or...

    https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html

  • Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences

    ...ashtags, hashtags only, removing stop-words, etc. you can play around with the different lists. This is the unsurprising list of top 10 most frequent terms (terms_only in Part 3) in the data set. [('ireland', 3163), ('england', 2584), ('wales', 2271), ('', 2068), ('day', 1479), ('france', 1380),...

    https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-4.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

    …ng Gephi to visualize our Facebook network. This graph is nice, as you get an instant view on the list of your friends, but not really interesting in terms of social network analysis. What is lacking is the relationships between your friends. 2nd step: clean your list and export it to html For this…

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

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

    ...y (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 your mind. This simple habit can aid in the...

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

  • Linear Programming and Discrete Optimization with Python using PuLP

    ...efficient solvers. Following are some of the canonical examples to get you started thinking, Solving Sudoku as an LP problem Maximizing return on the long-term investment as an LP problem LP applied to production planning Solving warehouse location problem using ILP Many machine learning algorithms...

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

  • The problem with metrics is a big problem for AI

    ...short-term concerns   It is much easier to measure short-term quantities: click through rates, month-over-month churn, quarterly earnings. Many long-term trends have a complex mix of factors and are tougher to quantify. What is the long-term impact on user trust of having your brand...

    https://www.kdnuggets.com/2019/10/problem-metrics-big-problem-ai.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

  • 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

  • Improving the Performance of a Neural Network

    ...ng a small learning rate can help a neural network converge to the global minima but it takes a huge amount of time. Therefore, you have to train the network for a longer period of time. A small learning rate also makes the network susceptible to getting stuck in local minimum. i.e the network will...

    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

  • Text Processing in R

    ...e additional terms captured by quanteda missing <- doc_term_matrix@Dimnames$features %in% colnames(Doc_Term_Matrix) # We can see that our document term matrix now includes terms with - and ' included. doc_term_matrix@Dimnames$features[which(missing == 0)] This is certainly easier and more...

    https://www.kdnuggets.com/2018/03/text-processing-r.html

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

    ...CarSim, PreScan. Cons: Matlab requires a license. But there are free alternatives available such as Octave. Matlab cannot be used for general-purpose programming. Matlab takes much memory of a computer when processing data. So if you have a large dataset is slows the computational speed.  ...

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

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

    ...at a scale never before possible has also amplified some of our worst qualities. Online hate speech spreads virally across the globe with short- and long-term consequences for individuals and societies. These consequences are often difficult to measure and predict. Online social media websites and...

    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

    comments In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term...

    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

  • How to Become a Data Scientist – Part 1">2016 Silver BlogHow to Become a Data Scientist – Part 1

    ...andbox everything, you have to prioritise, appreciate the incentives of others, compromise the math and technology for short-term vs. medium-term vs. long-term, worry about diminishing returns (80/20 rule) and deal with both deep theory and deep practice, and everything in-between. In short: you...

    https://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html

  • A Neural Network in 11 lines of Python

    ...(syn1.T) * (l1 * (1-l1)) 10.     syn1 += l1.T.dot(l2_delta) 11.     syn0 += X.T.dot(l1_delta)   Part 1: A Tiny Toy Network A neural network trained with backpropagation is attempting to use input to predict output. Consider trying to predict the output column...

    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

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

    ...ted challenges, including limited power and resources, scalability, mobility, dynamic network topologies, heterogeneity (e.g., in terms of CPU-power, networking, memory and storage, operating systems), data aggregation, security, as well as customization to the needs of the specific application...

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

  • Overview and benchmark of traditional and deep learning models in text classification

    ...icity, they are fast to train, and easy to understand. Cons: Even though ngrams bring some context between words, bag of word models fail in modeling long-term dependencies between words in a sequence. Now we're going to dive into deep learning models. The reason deep learning outperform bag of...

    https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.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

  • Attention Craving RNNS: Building Up To Transformer Networks

    ...ne’s too scared to. My goal today is to assume nothing, explain the details with animations, and make math great again (MMGA? ugh…) Here we’ll cover: Short RNN review. Short sequence to sequence model review. Attention in RNN's. Improvements to attention. Transformer network introduction.  ...

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

  • 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

  • Mining Twitter Data with Python Part 6: Sentiment Analysis Basics

    ...n. of tweets p_t = {} p_t_com = defaultdict(lambda : defaultdict(int)) for term, n in count_stop_single.items(): p_t[term] = n / n_docs for t2 in com[term]: p_t_com[term][t2] = com[term][t2] / n_docs Computing the Semantic Orientation   Given two vocabularies for positive and negative terms:...

    https://www.kdnuggets.com/2016/07/mining-twitter-data-python-part-6.html

  • Building, Training, and Improving on Existing Recurrent Neural Networks

    ...d bounds: TensorFlow. Before you get started, if you are brand new to RNNs, we highly recommend you read Christopher Olah’s excellent overview of RNN Long Short-Term Memory (LSTM) networks here. Speech recognition: audio and transcriptions   Until the 2010’s, the state-of-the-art for speech...

    https://www.kdnuggets.com/2017/05/building-training-improving-existing-recurrent-neural-networks.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

  • 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

    ...the location with the higher value function. Makes sense right? A position with a high value function = good to be in this position (with regards to long term reward). Now, this whole RL environment can be described with a Markov Decision Process. For those that haven’t heard the term before, an...

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

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

    ...the mathematical equivalent of a dL/dW where W are the weights at a particular layer. Now, what we want to do is perform a backward pass through the network, which is determining which weights contributed most to the loss and finding ways to adjust them so that the loss decreases. Once we compute...

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

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

    ...Bengio and Aaron Courville Alternatively, you can use (it may be good for an introduction with interactive materials, but I’ve found the style a bit long-winded): Neural Networks and Deep Learning by Michael Nielsen   Other materials   There are many applications of deep learning (it’s...

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

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

    ...ine a regional economic specialty. Link Lee Rainie, director of the Pew Research Internet Project, used NodeXL to map a range of Twitter social media networks. Along with researchers from the Social Media Research Foundation, including myself, the research team documented the existence of six...

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

  • Why the Fast Data world needs a proven and mature In-Memory Data Fabric?

    ...on -- will drive true community-building beyond just open source consumption, and of course will ensure the in-memory data fabric’s viability for the long term. By doing this, we hope to continue to fast-track both the adoption and adaptation of this technology. Ilya Sterin, Sr. Director of...

    https://www.kdnuggets.com/2014/11/gridgain-in-memory-data-fabric-apache.html

  • Attend In-Memory Computing Summit, May 23-24, San Francisco

    ...esident and CEO, GridGain In Memory Computing for Financial Services: Past, Present and Future, by Robert Barr, VP Data Grid Architect, Barclays More Memory for In-Memory? Easy!, by Benzi Galili, COO, ScaleMP NVDIMM - Changes are Here So What's Next?, by Arthur Sainio, Co-Chair SNIA NVDIMM Special...

    https://www.kdnuggets.com/2016/05/gridgain-imcsummit-in-memory-computing-san-francisco.html

  • Mining Twitter Data with Python Part 3: Term Frequencies

    ...ounter fname = 'mytweets.json' with open(fname, 'r') as f: count_all = Counter() for line in f: tweet = json.loads(line) # Create a list with all the terms terms_all = [term for term in preprocess(tweet['text'])] # Update the counter count_all.update(terms_all) # Print the first 5 most frequent...

    https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-3.html

  • 6 areas of AI and Machine Learning to watch closely">Gold Blog6 areas of AI and Machine Learning to watch closely

    …ks. In a typical RL setup, an agent is tasked with observing its current state in a digital environment and taking actions that maximise accrual of a longterm reward it has been set. The agent receives feedback from the environment as a result of each action such that it knows whether the action…

    https://www.kdnuggets.com/2017/01/6-areas-ai-machine-learning.html

  • What Is Machine Intelligence Vs. Machine Learning Vs. Deep Learning Vs. Artificial Intelligence (AI)?

    ...proach, the brain gives us a roadmap of where to direct our work in the future, such as completing our understanding of behavior, attention and short term memory. This roadmap distinguishes HTM from other techniques and makes it the best candidate for creating intelligent machines. An Example Let’s...

    https://www.kdnuggets.com/2016/01/what-is-machine-intelligence-ml-deep-learning-ai.html

  • 2013 Nov: Analytics, Big Data, Data Mining and Data Science Posts

    ...icipants to re-rank URLs of each SERP returned by the search engine according to the personal preferences of the users - personalize search using the long-term (user history based) and short-term (session-based) user context. Data Analysis Consultant at Megaputer, Bloomington, IN - Nov 12, 2013....

    https://www.kdnuggets.com/2013/11/index.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

  • 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

  • Designing Your Neural Networks

    ...network size itself down for you. Again, I’d recommend trying a few combinations and track the performance in your Weights and Biases dashboard to determine the perfect network size for your problem. Andrej Karpathy also recommends the overfit then regularize approach — “first get a model large...

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

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

    ...ut it really helped me to understand Interior and Integral gradient as well as general overview of how can we understand the inner workings of neural network.   Data Set / Network Architecture / Accuracy / Class Numbers   Image from this website Red Rectangle → Input Image (32*32*3) Black...

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

  • 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

  • Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory

    …e is enormous interest in the data world in how to make optimal use of RAM for analytics. The trade-offs being for columnar data are different for in-memory. For data on disk, usually IO dominates latency, which can be addressed with aggressive compression, at the cost of CPU. In memory, access is…

    https://www.kdnuggets.com/2017/02/apache-arrow-parquet-columnar-data.html

  • Three Impactful Machine Learning Topics at ICML 2016

    ...l apps, chatbots, and deep learning would be interested in these topics. Deep Residual Networks: Deep Learning Gets Way Deeper by Kaiming He (slides) Memory Networks for Language Understanding by Jason Weston(slides) Recent Advances in Non-Convex Optimization and its Implications to Learning by...

    https://www.kdnuggets.com/2016/07/impactful-machine-learning-topics-icml-2016.html

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

    ...al approach to a business-driven data approach, focusing on agility in the use of big data analytics capabilities, using it to drive initial and also long-term business value. Looking forward to 2018, will be the year of growth of convergence between big data with others technologies. With machine...

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

  • Unsupervised Investments (II): A Guide to AI Accelerators and Incubators

    ...on of raising a Series A round of capital within 6–12 months; IBM Alphazone (Israel): IBM created this accelerator with the goal in mind of fostering long-term technology and business partnerships with smaller companies in the Cloud, Big Data & Analytics and IoT space. They have another...

    https://www.kdnuggets.com/2017/05/unsupervised-investments-guide-ai-accelerators-incubators.html

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

    ...ture is full of great papers that have discussed and devised ways to go around lack of statistics—for instance in a lot of the literature on European development in the 16th and 17th centuries, population size over short periods of time was considered constant, for simplicity, because there was no...

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

    ...ound Cytoscape.js. Dracula Graph library : a JavaScript library released under the MIT License to display and layout interactive connected graphs and networks, along with various related algorithms from the field of graph theory. Just plain JavaScript and SVG. El Grapho: a JavaScript WebGL graph...

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

  • Applying Data Science to Cybersecurity Network Attacks & Events

    ...he words that popped up are network or security related. We can use this information to better understand the scope of what these events are! Are the network attacks? Are they network warehouse related? Etc. But what if we wanted more information? We can group the descriptions based on the urgency...

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

  • Why Apache Arrow is the future for open source-columnar memory analytics

    …re contributing towards to create Apache Arrow. In the coming years we can expect all the big data platforms adopting Apache Arrow as its columnar in-memory layer. What can we expect from an in-memory system like Apache Arrow: Columnar: Over the past few years, big data is all about columnar. It…

    https://www.kdnuggets.com/2017/08/apache-arrow-open-source-columnar-memory-analytics.html

  • Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications

    ...m the matrix multiplication is the time that it takes to perform a single dot product computation. Once complete, matrix P is then copied from device memory back to host memory, where it can be further used by serial code, if necessary. Typically such a kernel operation would be followed by...

    https://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html

  • The Origins of Big Data

    …rk Times article credits Mr. Mashey with the first time use of the term ‘Big Data’. Even though Michael Cox and David Ellsworth seem to have used the term ‘Big data’ in print, Mr. Mashey supposedly used the term in his various speeches and that’s why he is credited for coming up with Big Data. In…

    https://www.kdnuggets.com/2017/02/origins-big-data.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

  • 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

  • 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

  • Checklist for Debugging Neural Networks

    ...sp; 3. Check intermediate outputs and connections   To debug a neural network, it can often be useful to understand the dynamics inside a neural network and the role played by the individual intermediate layers and how the layers are connected. You may be running into errors around: Incorrect...

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

  • A 2019 Guide for Automatic Speech Recognition

    ...version of the ASR system. The written text corpora used in the model contains over 50 billion words in total. The NLM architecture is made up of two Long Short-Term Memory Projection Recurrent Neural Network(LSTMP) layers, each comprising 1024 hidden units projected down to a dimension of 512....

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

  • The Rise of Generative Adversarial Networks

    ...ery other industry. We will be talking about major technological breakthroughs in the later section 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,...

    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

  • The Math Behind Bayes

    ...this equation has two terms dependent of θ, one of which we have seen before: the derivative of the likelihood function with respect to θ. The other term however, is new to us. This term represents the previous knowledge of the model that we might have, and we will see in just a bit how it can be...

    https://www.kdnuggets.com/2019/11/math-behind-bayes.html

  • Understanding Tensor Processing Units

    ...sorFlow compute clusters that can leverage CPUs, GPUs, and TPUs. High-level Tensorflow APIs help you to get models running on the Cloud TPU hardware. Programming model Transferring data between Cloud TPU and host memory is slow compared to the speed of computation because of the speed of the PCIe...

    https://www.kdnuggets.com/2019/07/understanding-tensor-processing-units.html

  • Generative Adversarial Networks – Key Milestones and State of the Art

    ...such as dropout layers are often used in GANs because the generator can overfit the training set through this entirely indirect learning process. The longer these two neural networks play this game, the more they sharpen each other’s skills. The discriminator becomes very good at detecting fake...

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

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

    ...this line to disable GPU Choose the Loss Function and Optimizer Loss function (criterion) decides how the output can be compared to a class, which determines how good or bad the neural network performs. And the optimizer chooses a way to update the weight in order to converge to find the best...

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

  • Semantic Segmentation Models for Autonomous Vehicles

    .... Such a technique also eliminates the need to learn parameters for upsampling, unlike in FCNs. Architecture 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...

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

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

    ...en throw away all the trained weights. It’s been repeatedly shown in research and practice that transfer learning helps to achieve high accuracy in a short period of time since networks trained for somewhat similar tasks discover similar weights; transfer learning is basically just transfer of...

    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

  • 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

  • Jobs in AI, Analytics, Data Science, Machine Learning

    ...cellent candidates to start as doctoral student for full-funded exciting research at the intersection of visual analytics, topological data analysis, network analysis (e.g. graph distance metrics and qualitative comparison of networks), in new Data Science Institute at UHasselt in Belgium. Research...

    https://www.kdnuggets.com/jobs/index.html

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

    …ed real sales performance (not performance ratings) over time, during and beyond formal and informal training. Both companies had made a significant, long term financial investment in formal training and coaching development programs, to help sales reps to optimize their sales performance Results:…

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

  • A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights

    ...ckground().index[:10])) Figure 26 Following are the terms in review text that are most associated with the Tops department: term_freq_df = corpus.get_term_freq_df() term_freq_df['Tops Score'] = corpus.get_scaled_f_scores('Tops') pprint(list(term_freq_df.sort_values(by='Tops Score',...

    https://www.kdnuggets.com/2019/05/complete-exploratory-data-analysis-visualization-text-data.html

  • Programming Best Practices For Data Science">Silver BlogProgramming Best Practices For Data Science

    ...sbursement_method debt_settlement_flag debt_settlement_flag_date settlement_status settlement_date settlement_amount settlement_percentage settlement_term The warning lets us know that the type inferencing of pandas for each column would be improved if we set the low_memory parameter to False when...

    https://www.kdnuggets.com/2018/08/programming-best-practices-data-science.html

  • Sentiment Analysis & Predictive Analytics for trading. Avoid this systematic mistake

    …ne such method, with regard to market direction, is to only count changes in direction forecast – i.e. to only count when sentiment data changes from LONG to SHORT, or vice versa – as proper sentiment signals to be assessed against the actual aggregate market direction for the entire time period…

    https://www.kdnuggets.com/2016/01/sentiment-analysis-predictive-analytics-trading-mistake.html

  • Resurgence of AI During 1983-2010

    ...on of heart failure onset [98]. In 1997, Hochreiter and Schmidhuber developed a specific kind of deep learning recurrent neural network, called LSTM (long short-term memory) [66]. LSTMs mitigate some problems that occur while training RNNs and they are well suited for predictions related to...

    https://www.kdnuggets.com/2018/02/resurgence-ai-1983-2010.html

  • TensorFlow for Short-Term Stocks Prediction

    ...ived his MS degree cum Laude in Computer Engineering from Politecnico di Milano, after a period at TU Delft working on a thesis about Recommender Systems. Mattia is now working as a Data Scientist in the Cyber Security area for an Italian company. Related Understanding Deep Convolutional Neural...

    https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

  • AI is a Big Fat Lie

    ...strial applications. So, we've even launched a new conference, Deep Learning World, which covers the commercial deployment of deep learning. It runs alongside our long-standing machine learning conference series, Predictive Analytics World. Deep Learning World, a conference on the commercial...

    https://www.kdnuggets.com/2019/01/dr-data-ai-big-fat-lie.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

  • Deep Learning in a Nutshell – what it is, how it works, why care?

    ...his example, there are no connections that lead from a neuron in a higher layer to a neuron in a lower layer (i.e., no directed cycles). These neural networks are called feed-forward neural networks as opposed to their counterparts, which are called recursiveneural networks (again these are much...

    https://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.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

  • Deep Learning Reading Group: Deep Networks with Stochastic Depth

    ...ted by early layers are washed out by the time they reach the final layers by the many weight multiplications in between. Long Training Times: Deeper networks require a longer time to train than shallow networks. Training time scales linearly with the size of the network. There are many solutions...

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

  • Spark with Tungsten Burns Brighter

    …ge of storing and processing data as a vector is getting tackled by the Tungsten project admirably. The Tungsten version of sort that uses chip cache memory is 3 times as fast as RAM in-memory sort. They haven’t gotten the new functionality into all the Spark base algorithms yet, but it’s coming….

    https://www.kdnuggets.com/2016/05/spark-tungsten-burns-brighter.html

  • MetaMind Mastermind Richard Socher: Uncut Interview

    ...e to be conscious of that. For instance, we need to provide on-prem [on-site] solutions. ZL: You presented a new model in the talk, called a "dynamic memory network". For this audience it didn't get super technical, but the way you described it had the feeling of a neural Turing machine approach....

    https://www.kdnuggets.com/2015/10/metamind-mastermind-richard-socher-deep-learning-interview.html

  • Intel’s Investments in Cognitive Tech: Impact and New Opportunities

    ...n and alternative solutions like OpenCL for Altera’s FPGAs and C for Xilinx’s FPGAs do not look optimal[12]. Moreover, in addition to challenges with programming, data movement and memory are the other concerns, when CPUs are accelerated by GPUs or FPGUs[13]. For example, a bottleneck appears at a...

    https://www.kdnuggets.com/2016/05/intel-investment-cognitive-tech-impact-new-opportunities.html

  • Understanding Convolutional Neural Networks for NLP

    …via Region Embedding. [6] Wang, P., Xu, J., Xu, B., Liu, C., Zhang, H., Wang, F., & Hao, H. (2015). Semantic Clustering and Convolutional Neural Network for Short Text Categorization. Proceedings ACL 2015, 352–357. [7] Zhang, Y., & Wallace, B. (2015). A Sensitivity Analysis of (and…

    https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

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