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Search results for Digital Venture Pre Seed

    Found 384 documents, 11242 searched:

  • Intent Recognition with BERT using Keras and TensorFlow 2

    ..._ids = map( lambda tids: tids +[0]*(data.max_seq_len-len(tids)), pred_token_ids ) pred_token_ids = np.array(list(pred_token_ids)) predictions = model.predict(pred_token_ids).argmax(axis=-1) for text, label in zip(sentences, predictions): print("text:", text, "\nintent:", classes[label]) print()...

    https://www.kdnuggets.com/2020/02/intent-recognition-bert-keras-tensorflow.html

  • Fighting Overfitting in Deep Learning

    ...ould we always start from scratch? Let’s take pre-trained models' weights and just optimize them for our task. The problem is that we haven’t a model pre-trained on our data. But we can use a network, pre-trained on a really huge amount of data. Then, we use our relatively small data to fine-tune...

    https://www.kdnuggets.com/2019/12/fighting-overfitting-deep-learning.html

  • Batch Normalization in Neural Networks

    ...orm layer actually improves ImageNet, which is cool. You can add it to dense layers, and also to convolutional layers. If we insert a batch norm in a pre-trained network, it will change the pre-trained weights, because it will subtract the mean and divide by the standard deviation for the...

    https://www.kdnuggets.com/2018/06/batch-normalization-neural-networks.html

  • Data Science Data Architecture

    …ronments, as shown in Figure 1, after some time, the data scientist has a new idea to improve the model. The current approved model is taken from the pre-production environment, and being worked on. Once ready it is placed back into pre-approval, but as the figure shows, it cannot be approved due…

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

  • Is data mining the new tool for gamers seeking pre-launch secrets?

    ...purposes (beyond the very obvious one - targeted marketing!): Find weak spots in a games' design Figure out how players spend their time when playing Predict when players will stop playing Discover which assets are not getting used Personalize i.e. make games that adapt to the player However, with...

    https://www.kdnuggets.com/2014/02/data-mining-tool-gamers-pre-launch-secrets.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

    ...is what the convolution layer does: given a filter, it scans the input and generates a feature map. But what does this convolution operation really represent? How to interpret the resulting feature map? I started by stating that convolution layers capture visual patterns within an image. Let me...

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

  • U. of Chicago Center for Data Science and Public Policy: Postdoc, Pre-Doctoral, Data Engineer, Data Scientist, and Project Manager

    ...ce? The Center for Data Science & Public Policy at the University of Chicago has openings for Post-Doctoral Data Science for Social Good Fellows, Pre-Doctoral (Non-Phd) fellowships positions for Data Engineers, Data Scientists, and Project Managers with a strong background in machine...

    https://www.kdnuggets.com/jobs/15/07-15-dsapp-postdoc-data-engineer-data-scientist-b.html

  • What I learned from looking at 200 machine learning tools

    ...he most resource-intensive phase of a project, once you’ve had people put their data on your platform, it’s tempting to provide them with a couple of pre-built/pre-trained models. Tools for modeling & training are mostly frameworks. The deep learning frameworks competition cooled down to be...

    https://www.kdnuggets.com/2020/07/200-machine-learning-tools.html

  • A Single Function to Streamline Image Classification with Keras

    ...with a very large dataset, whose contents cannot be fitted into memory at one go. Look at this article to understand it better, Python’s Generator Expressions: Fitting Large Datasets into Memory Generator Expressions are an interesting feature in Python, which allows us to create lazily generated...

    https://www.kdnuggets.com/2019/09/single-function-streamline-image-classification-keras.html

  • A 2019 Guide for Automatic Speech Recognition

    ...gnition We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is…   The approach used in pre-training is optimizing the model to predict future samples from a single context. The model takes a raw audio signal as input and then applies an...

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

  • Three Methods of Data Pre-Processing for Text Classification

    ...e techniques that were beneficial for me when developing this project, described below.   Bag of Words   Modern neural networks cannot interpret labeled text as described above and data must be pre-processed before it can be given to a network for training. One straightforward way to do...

    https://www.kdnuggets.com/2019/11/ibm-data-preprocessing-text-classification.html

  • BERT, RoBERTa, DistilBERT, XLNet: Which one to use?

    ...g of BERT with improved training methodology, 1000% more data and compute power. To improve the training procedure, RoBERTa removes the Next Sentence Prediction (NSP) task from BERT’s pre-training and introduces dynamic masking so that the masked token changes during the training epochs. Larger...

    https://www.kdnuggets.com/2019/09/bert-roberta-distilbert-xlnet-one-use.html

  • This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training

    ...to master two highly dissimilar tasks under the same structure. To achieve that, VLP uses a unified encoder-decoder model for general vision-language pre-training. The pre-trained model is fine-tuned for image captioning and visual question answering. More specifically, The VLP model uses a shared...

    https://www.kdnuggets.com/2019/10/microsoft-neural-network-answer-questions-scenic-images-minimum-training.html

  • Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019

    ...be done, shares Oleksandr Khryplyvenko. “What does it mean to ‘understand’ a sentence?” Google’s response to this question is the BERT technique for pre-training NLP applications. Pre-training entails using a huge volume of unlabeled generic text from the web to train a language representation...

    https://www.kdnuggets.com/2019/02/ai-data-science-advances-trends.html

  • Hadoop is Not Failing, it is the Future of Data

    …op has it’s own technical constraints -the ability to support low latency BI (Business Intelligence) queries for one. However, the sheer inability of pre-Hadoop approaches to scale with exploding data ingest and management of massive data caused two business challenges for Digital Architectures….

    https://www.kdnuggets.com/2017/04/hadoop-not-failing-future-data.html

  • 75 Big Data Terms to Know to Make your Dad Proud

    By Ramesh Dontha, Digital Transformation. My earlier article on ‘25 Big Data terms you must know to impress your date’ had a pretty decent response (at least by my standards) and there were requests to add more. Look, it is fairly easy to impress your date. Depending on the gender, all you may...

    https://www.kdnuggets.com/2017/06/75-big-data-terms.html

  • Blockchain Key Terms, Explained

    ...t address and a private key. For a bitcoin wallet, the wallet address is public but the private key is needed to verify with the whole network that a digital signature matches and the transaction is valid. 10. Smart Contract A smart contract is a digital agreement stored on the blockchain that is...

    https://www.kdnuggets.com/2017/11/blockchain-key-terms-explained.html

  • Building an intelligent Digital Assistant

    ...enable their apps (as is the case with Google Home, Alexa etc). The second was to build a truly natural language interface that understands a user’s precise intent from the commands they speak and seamlessly executes the action within the app on their phone that best meets that intent. Thirdly we...

    https://www.kdnuggets.com/2019/10/there-thing-free-lunch-part-2.html

  • Cisco: Machine Learning Engineer/Support Bot Designer [Raleigh, NC]

    ...(NLU), API/micro services and mobile/web development methodologies that can be leveraged to deploy conversational Bot/VA solutions globally, both on-premise and in the cloud. You will lead our Digital Transformation team to use and implement the tools of the Bot/VA platform to setup the intents...

    https://www.kdnuggets.com/jobs/19/03-29-cisco-machine-learning-engineer-support-bot-b.html

  • UnitedHealth Group: UHC Digital Director of Project Management [Minnetonka, MN]

    ...op business plans and cost benefit analysis for assessment by Regional/Senior Leadership You'll investigate non-standard requests and problems, makes presentations to senior leadership, ensure project documentation is accurate and ensure projects are completed on time and within scope Pertinent...

    https://www.kdnuggets.com/jobs/18/10-04-unitedhealth-group-uhc-digital-director-project-management.html

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

    ...on helping clients use data to make better decisions. Toronto, ON, Canada. Custom Analytics Consulting provides high-level expertise in descriptive, predictive, and prescriptive analytics: data-, text-, and web-mining, forecasting and classification models, optimization and numerical simulations....

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

  • UnitedHealth Group: Director, Data Science [Minnetonka, MN]

    ...on and spearhead initiatives to instill the importance of data driven-decisions as a key component of success Serve as an evangelist for the power of predictive analysis and develop business cases for presentation to key stakeholders to illustrate proven successes. Act in a leadership role in the...

    https://www.kdnuggets.com/jobs/18/12-19-unitedhealth-group-director-data-science.html

  • Cisco: Support Bot Designer/Engineer [Raleigh, NC]

    ...(NLU), API/micro services and mobile/web development methodologies that can be leveraged to deploy conversational Bot/VA solutions globally, both on-premise and in the cloud. You will lead our Digital Transformation team to use and implement the tools of the Bot/VA platform to setup the intents...

    https://www.kdnuggets.com/jobs/19/03-05-cisco-support-bot-designer-engineer.html

  • RE.WORK Connect: Shaping a Hyper-connected World

    ...s using deep learning for Smarter Devices for our Connected Environments; Mike Kuniavsky, Principal Scientist of Innovation Services at PARC, will be presenting User Experience for Predictive Machine Learning in the Consumer IoT; Bryan Mistele, President & CEO of INRIX, will discuss Connecting...

    https://www.kdnuggets.com/2015/10/rework-connect-summit-san-francisco-november.html

  • Yahoo Lecture: Big Data, Global Diplomacy and Digital Heartbeat, by Kalev Leetaru

    ...eering (and controversial) work using "big data" approach to problems in international relations, including Culturonomics, a theory for using data to predict international events, and a landmark database GDELT: Global Data on Events, Location and Tone. Kalev Leetaru has received many honors and...

    https://www.kdnuggets.com/2013/12/yahoo-lecture-big-data-global-diplomacy-digital-heartbeat-by-kalev-leetaru.html

  • NPR: Data Scientist

    ...and work through a technical approach and attack a problem in a systematic way Solid experience with machine learning and data mining Experience with digital metrics systems such as Adobe Site Catalyst or Google Analytics Premium Experience with Google Analytics API Strong analysis and experimental...

    https://www.kdnuggets.com/jobs/13/12-18-npr-data-scientist.html

  • Adobe Research: Econometrics Data Scientist

    ...everaging advanced statistical techniques including econometric modeling. Identify key measures, approach and methodologies measuring traditional and digital (online, interactive) marketing campaign success against business objectives. Design and evaluate measurement systems for data capture and...

    https://www.kdnuggets.com/jobs/13/12-11-adobe-econometrics-data-scientist-adobe-research.html

  • Uber-fication! Uberize Your Business

    …a business, reduce costs to transact and make the operations transparent so that business partners find it less risky to participate with you. Become Digital and Become Mobile The leading digital platform of our day is clearly mobile. The convenience offered by the Uber app in summoning a car is…

    https://www.kdnuggets.com/2017/01/uber-uberize-your-business.html

  • Social Media & Web Analytics Innovation Summit 2014: Day 1 Highlights

    ...ed "actionability" as digital product improvements i.e. existing feature enhancements, adding new features, etc. He discussed how our thinking of the digital space has evolved, as now digital is a medium to engage and delight customers. So, the importance of customer engagement has been accentuated...

    https://www.kdnuggets.com/2014/05/social-media-web-analytics-summit-san-francisco-talks-day-1.html

  • Big Data & Analytics Innovation Summit, Australia: Day 1 Highlights

    ...t-outs, do-not-solicit requests, and negative brand experience, all have a real impact on the bottom line. Opportunities to engage with customers are precious and one needs to ensure these interactions are optimized. Using the power of digital analytics, one can reach a deeper understanding of...

    https://www.kdnuggets.com/2014/10/big-data-analytics-summit-australia-day1.html

  • 3 Ways to Build an Analytics Dream Team

    ...more than savvy social media. Accenture’s Technology Vision 2016 predicts leading companies that develop a people first approach will win in today’s digital economy. “Companies that embrace digital can empower their workforce to continuously learn new skills to do more with technology and generate...

    https://www.kdnuggets.com/2016/04/3-ways-build-analytics-dream-team.html

  • OpenText Data Digest Sep 25: US Maps

    …Philip Bump (@pbump) notes that the upcoming televised Presidential debate in St. Louis, Missouri will be the fourth such time the city has hosted a presidential or vice presidential contest in the last 56 years. Bump suggests the St. Louis bias may come from the familiarity with host Washington…

    https://www.kdnuggets.com/2015/10/opentext-data-digest-sep-25-us-maps.html

  • A Vision for Making Deep Learning Simple">Silver Blog, Sep 2017A Vision for Making Deep Learning Simple

    ...we just loaded. This prediction, of course, is done in parallel with all the benefits that come with Spark: from sparkdl import readImages, DeepImagePredictor predictor = DeepImagePredictor(inputCol="image", outputCol="predicted_labels", modelName="InceptionV3") predictions_df =...

    https://www.kdnuggets.com/2017/09/databricks-vision-making-deep-learning-simple.html

  • Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained

    ...utral, or negative. E.g. “You are awesome” classifies as positive). Well, you can simply add another output! The input is “I like New”, the next word prediction is “York”, and the sentiment prediction is positive. The loss from both outputs is then summed together and averaged, and the final loss...

    https://www.kdnuggets.com/2019/10/multi-task-learning-ernie-sota-nlp-architecture.html

  • The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

    ...d the decoder, the portion of the model that transforms the feature output from the encoder into classifications, pixel segmentations, next-time-step predictions, and so on. Taking a pre-trained model and initializing and re-training a new decoder can achieve state-of-the-art performance in far...

    https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

  • Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree

    ...snippets below to see what we mean: Code Snippets from Source 3: def test(model, criterion, use_cuda): ... #omitted # convert output probabilities to predicted class pred = output.data.max(1, keepdim=True)[1] # compare predictions to true label correct +=...

    https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html

  • Deep Learning Tips and Tricks

    ...rful CNN architectures. Consider domains that may not seem like obvious fits, but share potential latent features. Use a smaller learning rate: Since pre-trained weights are usually better than randomly initialized weights, modify more delicately! Your choice here depends on the learning landscape...

    https://www.kdnuggets.com/2018/07/deep-learning-tips-tricks.html

  • Find Out What Celebrities Tweet About the Most

    …s if needed #Trump <- tm_map(Trump, removeWords, c(‘amp’,’will’)) #Modi <- tm_map(Modi, removeWords, c(‘amp’,’will’)) Fig. 2: R Word Cloud from President Putin’s Tweets Fig. 3: R Word Cloud using President Trump’s most recent Tweets Fig. 4: R Word Cloud from Prime Minister Modi’s most recent…

    https://www.kdnuggets.com/2017/10/what-celebrities-tweet-about-most.html

  • Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science">Gold BlogData Science for Internet of Things (IoT): Ten Differences From Traditional Data Science

    ...Reinforcement learning also has applications for IoT as I discussed in a post by Brandon Rohrer for Reinforcement Learning and Internet of Things 5) Pre-processing for IoT IoT datasets need a different form of Pre-processing. Sibanjan Das and I referred to it in Deep learning - IoT and H2O. Deep...

    https://www.kdnuggets.com/2016/09/data-science-iot-10-differences.html

  • An Introduction to the MXNet Python API 

    ...use what we learned on Symbols and NDArrays to prepare some data and build a neural network. Then, we’ll use the Module API to train the network and predict results. Part 4: Using a pre-trained model for image classification (Inception v3) In part 3, we built and trained our first neural network....

    https://www.kdnuggets.com/2017/05/intro-mxnet-python-api.html

  • Machine Learning & AI Main Developments in 2018 and Key Trends for 2019">Gold BlogMachine Learning & AI Main Developments in 2018 and Key Trends for 2019

    ...second was in November. Google open-sourced BERT (Bidirectional Encoder Representations from Transformers), a bidirectional, unsupervised language representation, pre-trained on Wikipedia. As the authors demonstrated in "BERT: Pre-training of Deep Bidirectional Transformers for Language...

    https://www.kdnuggets.com/2018/12/predictions-machine-learning-ai-2019.html

  • Resurgence of AI During 1983-2010

    ...t 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 time-series. Applications of such networks include those in robotics, time series prediction, speech recognition, grammar...

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

  • Deep learning in Satellite imagery

    ...label your images and store them. Finally, you can build a dashboard that will use them or use the API to request an image, run the model on it, and present results. Although presented architecture is based on R & Shiny, Python is suited for this job as well and we tested it in our commercial...

    https://www.kdnuggets.com/2018/12/deep-learning-satellite-imagery.html

  • How to Automate Tasks on GitHub With Machine Learning for Fun and Profit

    ...our users, which allows us to re-train our model and debug problems very fast. We discuss the explicit feedback mechanism in a later section. Making predictions Below are model predictions on toy examples. The full code is available in this notebook. Link to the notebook.   We wanted to...

    https://www.kdnuggets.com/2019/05/automate-tasks-github-machine-learning-fun-profit.html

  • Spark NLP: Getting Started With The World’s Most Widely Used NLP Library In The Enterprise">Silver Blog Spark NLP: Getting Started With The World’s Most Widely Used NLP Library In The Enterprise

    ...d entity recognition with BERT Python code from the previous section: sparknlp.start() starts a new Spark session if there isn’t one, and returns it. PretrainedPipeline() loads the English language version of the explain_document_dl pipeline, the pre-trained models, and the embeddings it depends...

    https://www.kdnuggets.com/2019/06/spark-nlp-getting-started-with-worlds-most-widely-used-nlp-library-enterprise.html

  • NLP Breakthrough Imagenet Moment has arrived

    ...aining. Indeed, the above tasks (and many others such as sentiment analysis, constituency parsing, skip-thoughts, and autoencoding) have been used to pretrain representations in recent months. While any data contains some bias, human annotators may inadvertently introduce additional signals that a...

    https://www.kdnuggets.com/2018/12/nlp-imagenet-moment.html

  • Nvidia’s New Data Science Workstation — a Review and Benchmark

    ...hrough in this article.   The specs   The workstation I tested was built by Boxx. It comes with all the hardware pre-built and the software pre-installed, plus some extra cabling just in case. Having the machine pre-built was great since it’s something that might take a good few hours....

    https://www.kdnuggets.com/2019/07/nvidia-new-data-science-workstation.html

  • 7 Steps to Understanding Computer Vision

    ...both: differential and integral). A brief introduction to matrix calculus should come in handy. Also, my experience says that if one has some idea of digital signal processing then it should be helpful to grasp concepts easily. On the implementation side, I prefer one to have a background in both...

    https://www.kdnuggets.com/2016/08/seven-steps-understanding-computer-vision.html

  • Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 2

    ...ables and metrics that might be better predictors of performance (remembering our definition of data science). To accomplish this, we will apply the “Predictive Questions” exercise. In the “Predictive Questions” exercise, we will take a descriptive question that the business users are asking to...

    https://www.kdnuggets.com/2016/11/schmarzo-cdo-toolkit-leading-digital-business-transformation-part-2.html

  • Best Masters in Data Science and Analytics – Europe Edition

    ...m prepares its graduates to design and build data-driven systems for decision-making in the private or public sector, offering a thorough training in predictive, descriptive, and prescriptive analytics. (9-month program, $21,245 full tuition) Universitat de Barcelona's Master in Foundations of Data...

    https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-europe.html

  • Help Define the Future of Open Source Data Management, Boston, June 26-28

    ...26-28 in Boston. Digitalization—the integrated use of analytics, big data, the cloud, the Internet of Things (IoT), and mobile—is driving change at unprecedented rates. The diverse nature of digital applications demands that IT managers adopt new data management strategies and solutions to manage a...

    https://www.kdnuggets.com/2017/04/postgres-vision-boston-open-source-data-management.html

  • Age of AI Conference 2018 – Day 2 Highlights

    ...d on Deep Learning in Adversarial context, was split into two parts: Integrity at the interface Privacy The attack surface spans the physical domain, digital representation, Machine Learning (ML) model and again the physical domain. This talk focused on the ML model aspect. Types of adversaries and...

    https://www.kdnuggets.com/2018/02/age-ai-conference-2018-day-2.html

  • Paradoxes of Data Science

    …es, on the other hand, remain hesitant and risk averse, so far downstream in this cascade of innovations that the word almost loses its meaning. They prefer things that are “comprehensible” and that have been widely consecrated, generating little controversy, e.g. in health care, using CMS approved…

    https://www.kdnuggets.com/2015/08/paradoxes-data-science.html

  • 5 Decisive Technology Trends which will Make or Break the Manufacturing Momentum in 2017

    ...r maintenance schedules, to save millions of dollars on part replacements. The U.S. Air Force plans maintenance of jet engines based on data from its Digital Twin. Predict Today for a Better Tomorrow: By staving off failures even before they occur just by using equipment data intelligently,...

    https://www.kdnuggets.com/2017/02/5-decisive-technology-trends-manufacturing-momentum.html

  • UBS Research: Digital/Web Analytics Manager

    Company: UBS Research Location: New York, NY Web: www.ubs.com If you're an experienced campaign analyst, you're probably frustrated by the siloed nature of most campaign reporting capabilities. The web team talks to the e-mail team, who isn't measuring things the same way; clicks get attributed...

    https://www.kdnuggets.com/jobs/14/05-22-ubs-digital-web-analytics-manager.html

  • Additions to KDnuggets Directory in April

    ...7-8, Chief Data Officer Summit 2014. London, UK. May 8, Open Analytics Summit. New York, NY, USA. May 12-14, Big Data Con. Mainz, Germany. May 14-15, Digital & Web Analytics Summit, Gain Actionable & Impactful Digital Insight. London, UK. May 22-23, Chief Data Officer Summit. San Francisco,...

    https://www.kdnuggets.com/2014/05/added-to-kdnuggets-in-april.html

  • Merkle: Data Operations Lead

    ...ransfers from clients and client teams to Data Operations team and back out to our partners and clients Serve as operations subject matter expert for digital and traditional data process Design rigorous processes and workflows to ensure excellent execution and communication with the key team...

    https://www.kdnuggets.com/jobs/15/07-02-merkleinc-data-operations-lead.html

  • Time Series Classification Synthetic vs Real Financial Time Series">Silver BlogTime Series Classification Synthetic vs Real Financial Time Series

    ...ing (9000 training vs 3000 in-sample testing) # I plot the probabilities from the model - the "dashed" line is the average predicted probability. xgb.pred <- predict(xgb.model, dval, type = 'prob') results <- cbind(y_val, xgb.pred) results %>% as.tibble() %>% ggplot(aes(x = xgb.pred)) +...

    https://www.kdnuggets.com/2020/03/time-series-classification-synthetic-real-financial-time-series.html

  • Best practices of orchestrating Python and R code in ML projects

    …f = ft.read_dataframe(test_matrix_file) labels = df.loc[:,’label’] x = df.loc[:, df.columns != ‘label’] predictions_by_class = model.predict_proba(x) predictions = predictions_by_class[:,1] precision, recall, thresholds = precision_recall_curve( labels.ix[:,0], predictions) auc =…

    https://www.kdnuggets.com/2017/10/best-practices-python-r-code-ml-projects.html

  • The Current Hype Cycle in Artificial Intelligence

    ...e censure, with his or her only defense being that the trust on the AI system was misplaced. Moore’s Law Will Likely End Within a Decade As mentioned previously, Moore’s law, which predicts an exponential growth rate for the number of transistors in a circuit, has been the most influential reason...

    https://www.kdnuggets.com/2018/02/current-hype-cycle-artificial-intelligence.html

  • CatBoost vs. Light GBM vs. XGBoost

    ...thm splits all the data points for a feature into discrete bins and uses these bins to find the split value of histogram. While, it is efficient than pre-sorted algorithm in training speed which enumerates all possible split points on the pre-sorted feature values, it is still behind GOSS in terms...

    https://www.kdnuggets.com/2018/03/catboost-vs-light-gbm-vs-xgboost.html

  • Comparing Deep Learning Frameworks: A Rosetta Stone Approach

    ...nchmarking Deep Learning Frameworks In the following sections, we review results for training time for one type of CNN model, feature extraction on a pre-trained ResNet50 model, and training time for one type RNN model. Training Time(s): CNN (VGG-style, 32bit) on CIFAR-10 – Image Recognition The...

    https://www.kdnuggets.com/2018/03/deep-learning-frameworks.html

  • Understanding Convolutional Neural Networks for NLP

    …ld larger gains than using them for long texts. Building a CNN architecture means that there are many hyperparameters to choose from, some of which I presented above: Input represenations (word2vec, GloVe, one-hot), number and sizes of convolution filters, pooling strategies (max, average), and…

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

  • How to Become a Data Scientist – Part 3

    ...applying directly, or you could find out if other, more trusted consultants are able to help. Look – if you are the best person for the job and are represented by a clueless recruiter, it might not matter either way. But given the choice, it is preferable to go with someone you rate every time,...

    https://www.kdnuggets.com/2016/09/become-data-scientist-part-3.html

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

    ...tical part of creating the network, the idea of transfer learning has helped to lessen the data demands. Transfer learning is the process of taking a pre-trained model (the weights and parameters of a network that has been trained on a large dataset by somebody else) and “fine-tuning” the model...

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

  • BERT is changing the NLP landscape">Silver BlogBERT is changing the NLP landscape

    ...er of Greek gods.” BERT enables transfer learning. This is referred to as “NLP’s ImageNet Moment.” Google has pre-trained BERT on Wikipedia, and this pre-trained model can now be used on other more specific datasets like a customer support bot for your company. And remember this pre-training is...

    https://www.kdnuggets.com/2019/09/bert-changing-nlp-landscape.html

  • 3 Viable Ways to Extract Data from the Open Web

    ...This solution, then, is best when you’re dealing with finite sets of sources and required fields – not so much for monitoring or ongoing research. 3. Pre-Packaged Data from Webhose.io The Webhose.io platform provides a different method to access crawled web data. With this data-as-a-service (DaaS)...

    https://www.kdnuggets.com/2016/03/webhose-3-ways-extract-data-open-web.html

  • Detecting Sarcasm with Deep Convolutional Neural Networks">Gold BlogDetecting Sarcasm with Deep Convolutional Neural Networks

    ...t.   Contributions Apply deep learning to sarcasm detection Leverage user profiling, emotion, and sentiment features for sarcasm detection Apply pre-trained models for automatic feature extraction   Model Sentiment shifting is prevalent in sarcasm-related communication; thus, the authors...

    https://www.kdnuggets.com/2018/06/detecting-sarcasm-deep-convolutional-neural-networks.html

  • Improving Nudity Detection and NSFW Image Recognition

    ...Side-note: Illustration2Vec was developed to make a keyword-based search engine to help novice drawers find reference images to base new work on. The pre-trained caffe model is available through Algorithmia, and available under the MIT License. In all, Illustration2Vec can classify 512 tags, across...

    https://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html

  • Content-Based Recommendation System using Word Embeddings

    ...engine. However, it does not capture the semantic meaning and also it gives a sparse matrix. Research and breakthroughs are happening in NLP at an unprecedented pace. Neural network architectures has become famous for understanding the word representations and this is also called word embeddings....

    https://www.kdnuggets.com/2020/08/content-based-recommendation-system-word-embeddings.html

  • PAW Predictive Analytics San Francisco – 1 month before showtime

    ...industry sectors, including marketing, credit scoring, insurance, fraud detection, web optimization, and much more. View the agenda. * Register with pre-event pricing Predictive Analytics World for Workforce April 3-6, 2016 Take full advantage of analytics utilized for the purpose of solving...

    https://www.kdnuggets.com/2016/03/paw-predictive-analytics-san-francisco-1-month.html

  • Top New Features in Orange 3 Data Mining Platform

    ...number of features. We can use feature subset selection and then test SVM or logistic regression through cross-validation. Right? Wrong (of course)! Pre-processing should be a part of the learner. Learners in Orange now accept pre-processors, including feature subset selectors on the input, but...

    https://www.kdnuggets.com/2015/12/top-7-new-features-orange-3.html

  • What Data Scientists Can Learn From Qualitative Research

    ...nd Inductive Coding Styles   What are the two approaches to manual coding of open-ended questions, and which one is best? Deductive Coding Using pre-existing frame   With deductive coding you start with a predefined set of codes. These might come from an existing taxonomy, codes that...

    https://www.kdnuggets.com/2016/07/data-scientists-learn-from-qualitative-research.html

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

    …n’s nature, then align the offer (a coupon, a political candidate, or a job) with their nature and finally nurture – the right nature – to greatness. Predicting Pre-hire Solves Attrition and Performance Problems Our work consistently shows that top and bottom performers in a specific role have…

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

  • Hiring? Approving Mortgages? It’s the Same Thing (Risk …)

    …n it’s too late. Most approaches don’t have highly predictive job candidate data – and never consider augmenting their candidate datasets so they can predict pre-hire. Talent Analytics, Corp. Stands Alone in Predicting Flight Risk and Job Performance Pre-hire – Before It’s Too Late If your business…

    https://www.kdnuggets.com/2015/11/hiring-approving-mortgages-same-risk.html

  • TensorFlow 2.0: Dynamic, Readable, and Highly Extended

    ...eraging the improved capabilities of TF 2.0 include an open-source chatbot library called DeepPavlov, a bug-bite image classifier, and an air quality prediction app that estimates the level of pollution based on cell phone images. Caveats TensorFlow 2.0 remains pre-release as of this writing, and...

    https://www.kdnuggets.com/2019/08/tensorflow-20.html

  • Machine Learning Key Terms, Explained

    ...a form of supervised learning. 3. Regression   Regression is very closely related to classification. While classification is concerned with the prediction of discrete classes, regression is applied when the "class" to be predicted is made up of continuous numerical values. Linear regression...

    https://www.kdnuggets.com/2016/05/machine-learning-key-terms-explained.html

  • Automated Machine Learning in Python

    ...eline Optimization Tool (TPOT) According to its official site: The goal of TPOT is to automate the building of ML pipelines by combining a flexible expression tree representation of pipelines with stochastic search algorithms such as genetic programming. TPOT makes use of the Python-based...

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

  • BERT: State of the Art NLP Model, Explained

    ...of size H, in which each vector corresponds to an input token with the same index. When training language models, there is a challenge of defining a prediction goal. Many models predict the next word in a sequence (e.g. “The child came home from ___”), a directional approach which inherently...

    https://www.kdnuggets.com/2018/12/bert-sota-nlp-model-explained.html

  • Choosing an Error Function

    ...tion error function.   Use case: Absolute deviation with saturation Our temperature forecasts are now being used to make decisions about when to pre-heat or pre-cool an office building for a workday. Pre-heating and pre-cooling during the night allows a lower energy price and saves the company...

    https://www.kdnuggets.com/2019/06/choosing-error-function.html

  • Word Embeddings in NLP and its Applications

    ...2Vec works, here is what ParallelDots’s in-house experts view the subject. Technical Aspect of Word Embeddings A common practice in NLP is the use of pre-trained vector representations of words, also known as embeddings, for all sorts of down-stream tasks. Intuitively, these word embeddings...

    https://www.kdnuggets.com/2019/02/word-embeddings-nlp-applications.html

  • Text Preprocessing in Python: Steps, Tools, and Examples

    ...ign, and develop real-time intelligent software to improve their business with data technologies. Original. Reposted with permission. Related: Data Representation for Natural Language Processing Tasks Self-Service Data Prep Tools vs Enterprise-Level Solutions? 6 Lessons Learned Introduction to...

    https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

  • State of the art in AI and Machine Learning – highlights of papers with code

    ...led BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. Other top papers on NLP: Exploring...

    https://www.kdnuggets.com/2019/02/paperswithcode-ai-machine-learning-highlights.html

  • Deploy your PyTorch model to Production

    ...r.info("Classifying image %s" % (url),) response = requests.get(url) img = open_image(BytesIO(response.content)) t = time.time() # get execution time pred_class, pred_idx, outputs = learn.predict(img) dt = time.time() - t app.logger.info("Execution time: %0.02f seconds" % (dt))...

    https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

  • Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters

    ...ed in advance through two unsupervised tasks: masked language modeling (predicting a missing word given the left and right context) and next sentence prediction (predicting whether one sentence follows another). Thus BERT doesn’t need to be trained from scratch for each new task; rather, its...

    https://www.kdnuggets.com/2019/02/deconstructing-bert-distilling-patterns-100-million-parameters.html

  • 2018’s Top 7 R Packages for Data Science and AI

    ...ay, sometimes a simple visualization with ggplot can help you explain a model. For more on this check the awesome article below by Matthew Mayo) Interpreting Machine Learning Models: An Overview An article on machine learning interpretation appeared on O’Reilly’s blog back in March, written by...

    https://www.kdnuggets.com/2019/01/vazquez-2018-top-7-r-packages.html

  • What my first Silver Medal taught me about Text Classification and Kaggle in general?

    ...of the competition, everyone was trying to get the best possible rank on the public LB. It is just human nature. A lot of discussions was around good seeds and bad seeds for neural network initialization. While it seems okay in the first look, the conversation went a stage further where people...

    https://www.kdnuggets.com/2019/05/silver-medal-text-classification-kaggle.html

  • How to Build Your Own Logistic Regression Model in Python

    ...egression   Logistic regression algorithm is applied in the field of epidemiology to identify risk factors for diseases and plan accordingly for preventive measures. Used to predict whether a candidate will win or lose a political election or to predict whether a voter will vote for a...

    https://www.kdnuggets.com/2019/10/build-logistic-regression-model-python.html

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

    ...commands to perform random crops crop_size = [new_height, new_width, channels] seed = np.random.randint(1234) x = tf.random_crop(x, size = crop_size, seed = seed) output = tf.images.resize_images(x, size = original_size) 5. Translation Translation just involves moving the image along the X or Y...

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

  • H2O Framework for Machine Learning

    ...you can notice that if you subtract the total error for the test set (0.0958) from 1, you will get ~0.9041, which is the accuracy we found manually. (predictions["predict"] == test["y"]).mean()     Other algorithms   H2O provides several different models for training. Let's try some...

    https://www.kdnuggets.com/2020/01/h2o-framework-machine-learning.html

  • How A Data Scientist Can Improve Productivity

    …command reproduction. The previous command will be automatically piped with the next command because of the file data/Posts.tsv is an output for the previous command and the input for the next one: # Split training and testing dataset. Two output files. # 0.33 is the test dataset splitting ratio….

    https://www.kdnuggets.com/2017/05/data-scientist-improve-productivity.html

  • The New Neural Internet is Coming

    ...ique and, that fits you perfectly. CTR is going sky high. By measuring your reactions the network will adapt and make ads targeting you more and more precisely, hitting your soft spots. The Bubble trend So, at the end of the day, we are going to see a fully personalized content everywhere on the...

    https://www.kdnuggets.com/2018/02/new-neural-internet-coming.html

  • A Neural Network in 11 lines of Python

    ...e far left weight, this would multiply 1.0 * the l1_delta. Presumably, this would increment 9.5 ever so slightly. Why only a small ammount? Well, the prediction was already very confident, and the prediction was largely correct. A small error and a small slope means a VERY small update. Consider...

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

  • NIPS 2017 Key Points & Summary Notes

    ...on the exact same task depending on the random seed chosen. That is, A achieved statistically significant superior performance over B with one random seed, while this dominance was flipped with a different random seed. I really like this work, and again take it to be at just the right time....

    https://www.kdnuggets.com/2017/12/nips-2017-key-points-summary-notes.html

  • Feb 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    ...ush Into #BigData, #MachineLearning #BigDataCo t.co/ly3sS2fpdb Feb 23: .@6SenseInc #SF startup led by Amanda Kahlow, raises $20M for sales, marketing predictive #analytics #BigDataCo t.co/R9hHJ1ETRN Feb 05: Algorithm, Bengaluru and US-based data analytics firm, raised $161K seed funding #BigDataCo...

    https://www.kdnuggets.com/2015/03/february-analytics-big-data-science-company-activity.html

  • July 2014 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    ...ytics Corp, makers of software for solving complex power/energy problems buff.ly/1o5eVpm Jul 29: Peel-Works, Mumbai-bases SaaS and #BigData analytics venture, gets $2M from IDG Ventures, Inventus buff.ly/1n06Zko Jul 29: ThetaRay, Tel-Aviv Cyber Security and #BigData Innovator, closes $10M Series B...

    https://www.kdnuggets.com/2014/08/july-analytics-big-data-science-company-activity.html

  • Positioning a Machine Learning Company

    ...as little to competitive friction as possible. About: Zetta Venture Partners invests in companies building software that learns from data to analyze, predict and prescribe outcomes. They lead or co-lead $1-5M funding rounds. Original. Reposted with permission. Related: Best Data Science, Machine...

    https://www.kdnuggets.com/2016/04/positioning-machine-learning-company.html

  • Stacking the Deck: The Next Wave of Opportunity in Big Data

    ...market opportunities will develop over the next 3-5 years, not necessarily where the market is today. Over the past few years, billions of dollars of venture capital funding has flowed into Big Data infrastructure companies that help organizations store, manage and analyze unprecedented levels of...

    https://www.kdnuggets.com/2014/05/stacking-deck-next-wave-opportunity-big-data.html

  • Top Datapreneurs in data science

    …rved as CEO of Omniture, a SaaS-based web analytics company that he co-founded in 1996 and took public in 2006. Omniture was the number one returning venture investment out of 1,008 venture capital investments in 2004, as well as the number two performing technology IPO of 2006. He was named the…

    https://www.kdnuggets.com/2015/09/top-datapreneurs-data-science-analyticsvidhya.html

  • Where are the Opportunities for Machine Learning Startups?

    ...making. However, technologists have largely ignored emotion and created an often frustrating experience for people...” Images used to train a micro-expression detector (thanks JB!) The first task is to train models to recognise emotion in humans. Emotient, RealEyes and Affectiva all use facial...

    https://www.kdnuggets.com/2016/06/opportunites-machine-learning-startups.html

  • Panel Report: A Data Scientist Guide to Startups

    ...unquestionable pedigrees in data science and substantial experience with start-ups from multiple perspectives (founders, employees, chief scientists, venture capitalists). For the casual reader, we next present a brief summary of the experts' opinions on eight of the issues the panel discussed. The...

    https://www.kdnuggets.com/2014/08/panel-report-data-scientist-guide-startups.html

  • Apache Spark Introduction for Beginners">Silver BlogApache Spark Introduction for Beginners

    ...nvironment or the Hadoop stack. It enables different parts to keep running on the top of the stack having an explicit allocation for HDFS. Spark in MapReduce - Spark in MapReduce is utilized to dispatch start work notwithstanding independent arrangement. With SIMR, the client can begin Spark and...

    https://www.kdnuggets.com/2018/10/apache-spark-introduction-beginners.html

  • Will Predictive Hiring Algorithms Replace or Augment your HR Decisions?

    ...ng” you as a person when they review your resume in 6 seconds. There is nothing personal about today’s typical candidate screening process. Candidate Pre-screening – One of HR’s Best “Predictive Analytics Projects” Candidate screening is a process better handled by algorithms that can effortlessly,...

    https://www.kdnuggets.com/2016/05/predictive-hiring-algorithms-replace-augment-your-hr-decisions.html

  • Deep Learning in Neural Networks: An Overview

    ...pact way. These codes become the new inputs for supervised or reinforcement learning. Many methods learn hierarchies of more and more abstract data representations – continuously learning concepts by combining previously learnt concepts. “In the NN case, the Minimum Description Length principle...

    https://www.kdnuggets.com/2016/04/deep-learning-neural-networks-overview.html

  • Open Data in Elections: Using Visualization and Graphical Discovery Analysis for Voter Education and Civic Engagement

    ...#Buhari2015 and more, we analyzed sentiments while charting demographic data. The rest of the data we digitized and converted to interactive visual representations were crunched from the press, nongovernmental organizations and Nigeria's Independent National Electoral Commission (INEC). Some of...

    https://www.kdnuggets.com/2016/04/open-data-elections-using-visualization-graphical-discovery-analysis.html

  • Mousera: Data Scientist

    ...ocus on Data Sciences, or related field. 2+ years of work experience in Data Science.   Bonus points for: Experience with Cassandra, Hadoop or MapReduce Experience with Kairos Previous work experience at a startup or within a small team. Experience with clinical trials or biological data....

    https://www.kdnuggets.com/jobs/15/08-07-mousera-data-scientist.html

  • Interview: Sharmila Mulligan, ClearStory Data on Collaborative StoryBoards for Big Data

    ...aborate in real-time, and each Storyboard allows for very deep data exploration. This is not doable with dashboards that are pre-determined and often pre-wired to present a certain insight, outcome or KPI. Storyboards take data storytelling to a highly interactive, live view of insights. AR: Q4....

    https://www.kdnuggets.com/2015/01/interview-sharmila-mulligan-big-data-collaborative-storyboards.html

  • Anomaly Detection in Predictive Maintenance with Time Series Analysis

    …ical data? This requires a shift in the analytics perspective! If data describing normal functioning is what we have, then normal functioning we will predict! Pre-processing: Standardization and Time Alignment For this project, we worked on FFT pre-processed sensor data from 28 sensors monitoring a…

    https://www.kdnuggets.com/2015/12/anomaly-detection-predictive-maintenance-time-series-analysis.html

  • Update: Google TensorFlow Deep Learning Is Improving

    ...vember 9, 2015, Google released TensorFlow, its "open source software library for machine intelligence," of which I have previously shared my first impressions. On December 7, 2015, Jeff Dean and Oriol Vinyals presented a NIPS tutorial titled "Large-Scale Distributed Systems for Training Neural...

    https://www.kdnuggets.com/2015/12/update-google-tensorflow-deep-learning-is-improving.html

  • How Data Science Predicts and Reduces Adverse Birth Outcomes

    …The first step taken in this project was to expand the definition of an adverse birth from pre-mature births only, to the definition outlined above. Previously, the IDHS used maps of county-level pre-term rates to determine facility locations. To improve these locations, a cluster analysis was…

    https://www.kdnuggets.com/2016/01/data-science-predicts-adverse-birth-outcomes.html

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

    …a slightly different format. The screenplays themselves were in HTML, either within the tags, or within the
    
     tags. To extract the contents of each script, I used the Html Parser from F# Data library which allows accessing individual tags…
     

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

  • TDWI Launches Big Data Maturity Model Assessment Guide, Online Test

    ...Research Director and Krish Krishnan, Founder, Sixth Sense Advisors on Feb 11, 2014. The guide also maps out the stages of maturity: Nascent, which represents a pre-big data environment; most companies here are exploring the concept of analytics but have not yet started to explore advanced...

    https://www.kdnuggets.com/2013/11/tdwi-big-data-maturity-model-assessment-online.html

  • Custom Optimizer in TensorFlow

    ...tep apply_gradients(). This method relies on the (new) Optimizer (class), which we will create, to implement the following methods: _create_slots(), _prepare(), _apply_dense(), and _apply_sparse(). _create_slots() and _prepare() create and initialise additional variables, such as momentum....

    https://www.kdnuggets.com/2018/01/custom-optimizer-tensorflow.html

  • Crafting an Elevator Pitch for your Data Science Startup

    ...y – and there is an ocean of them. Be You but Be Them Too Whether you are writing an elevator pitch to deliver in oral form to potential investors or preparing a script for a video presentation, you need both the elements of a solid pitch and your voice, or you will seem far less than genuine. This...

    https://www.kdnuggets.com/2019/08/elevator-pitch-data-science-startup.html

  • 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

    ...forwards propagation using activations from previous layer Args: A_prev: Activations/Input Data coming into the layer from previous layer """ self.A_prev = A_prev # store the Activations/Training Data coming in self.Z = np.dot(self.params['W'], self.A_prev) + self.params['b'] # compute the linear...

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

  • Feature selection by random search in Python

    ...eep this in mind. Now, we can implement a random search with, for example, 300 iterations. result = [] # Number of iterations N_search = 300 # Random seed initialization np.random.seed(1) for i in range(N_search): # Generate a random number of features N_columns =...

    https://www.kdnuggets.com/2019/08/feature-selection-random-search-python.html

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

    ..."nn"] += [log_loss(y_test, y_predicted_nn['p_1'])] result[dataset_id]["f1"]["nn"] += [f1_score(y_test, y_predicted_nn['label'])] y_predicted_ens = (y_predicted_rf + y_predicted_nn) / 2.0 result[dataset_id]["logloss"]["ensemble"] += [log_loss(y_test, y_predicted_ens['p_1'])]   In the end, I am...

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

  • How to correctly select a sample from a huge dataset in machine learning">Silver BlogHow to correctly select a sample from a huge dataset in machine learning

    ...We know that the first half of the X1 variable of the dataset has a different distribution than the total, so we expect that such a sample can’t be representative of the whole population. If we repeat the tests, these are the p-values: As expected, X1 has a too low p-value due to the bias of the...

    https://www.kdnuggets.com/2019/05/sample-huge-dataset-machine-learning.html

  • Exoplanet Hunting Using Machine Learning

    ...Train.csv').fillna(0)train_data.head() Dataset   Now the target column LABEL consists of two categories 1(Does not represents exoplanet) and 2(represents the presence of exoplanet). So, convert them to binary values for easier processing of data. categ = {2: 1,1: 0} train_data.LABEL =...

    https://www.kdnuggets.com/2020/01/exoplanet-hunting-machine-learning.html

  • Introduction to Geographical Time Series Prediction with Crime Data in R, SQL, and Tableau

    ...These could be re-written to pull directly from reporting stored procedures with date parameters for a more productionized version of the code. # Set seed set.seed(1001) # Read in data dbhandle <- odbcDriverConnect('driver={SQL...

    https://www.kdnuggets.com/2020/02/introduction-geographical-time-series-crime-r-sql-tableau.html

  • R squared Does Not Measure Predictive Capacity or Statistical Adequacy

    ...ion. The following R code produces predictor and response values based on two separate true models – one is linear, and the other is exponential: set.seed(1); # set seed for replicability x <- seq(1,10,length.out = 1000) # predictor values y <- 1 + 1.5*x + rnorm(1000, 0, 0.80) # response...

    https://www.kdnuggets.com/2020/07/r-squared-predictive-capacity-statistical-adequacy.html

  • Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms

    ...g Intel-optimized models. The Intel® Low Precision Inference Toolkit accelerates deep learning inference workloads. This is developed to convert FP32 precision to int8 precision in order to assist customers with deploying low precision inference solutions rapidly. The Intel® Distribution for Python...

    https://www.kdnuggets.com/2020/09/showcasing-benefits-software-optimizations-ai-workloads-intel.html

  • 10 Useful Machine Learning Practices For Python Developers

    ...mes. If many people are working on the same project, everyone starts making different methods. This can create unproductive chaos.   2. Fix your seed at the top   Reproducibility of experiments is a very important thing, and seed is our enemy. Catch hold of it. Otherwise, it leads to...

    https://www.kdnuggets.com/2020/05/10-useful-machine-learning-practices-python-developers.html

  • Build an Artificial Neural Network From Scratch: Part 2

    ...omplex tasks such as image classification and stock market analysis, contain multiple hidden layers in addition to the input and output layer. In the previous article, we concluded that a Perceptron is capable of finding a linear decision boundary. We used the perceptron to predict whether a person...

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

  • Hands on Hyperparameter Tuning with Keras Tuner

    ...sian optimization for hyperparameter tuning. As for Hyperband, its main idea is to optimize Random Search in terms of search time. For every tuner, a seed parameter can be defined for experiments reproducibility: SEED = 1.   Random Search   The most intuitive way to perform hyperparameter...

    https://www.kdnuggets.com/2020/02/hyperparameter-tuning-keras-tuner.html

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

    ...neuron will be optimized for the provided training data. Consequently, if the neuron is made to think about a new situation, which is the same as the previous one, it could make an accurate prediction. This is how back-propagation takes place. Wrapping up Finally, we initialized the NeuralNetwork...

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

  • The Data Science of “Someone Like You” or Sentiment Analysis of Adele’s Songs

    ...aps you could group the tracks by genre and try to see if there is anything unseen or apply topic modeling techniques. Don’t forget to post your interpretation! Bio: Preetish Panda is a Marketing Manager at Prompt Cloud, a pioneer and global leader in Data-as-a-Service solutions. Preetish has a...

    https://www.kdnuggets.com/2018/09/sentiment-analysis-adele-songs.html

  • Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model

    ...ision node may work for this particular training data because there is a specific record that meet that criteria, but it is highly unlikely that it represents any predictive ability and so is unlikely to be accurate if applied to other data. All this discussion of overfitting with decision trees...

    https://www.kdnuggets.com/2016/06/doing-data-science-kaggle-walkthrough-creating-model.html

  • How to Rank 10% in Your First Kaggle Competition

    ...dictive power. However the overlap of product ID between the training set and the testing set is not very high. Would this contribute to overfitting? Preprocessing You can find how I did preprocessing and feature engineering on GitHub. I’ll only give a brief summary here: Use typo dictionary posted...

    https://www.kdnuggets.com/2016/11/rank-ten-precent-first-kaggle-competition.html

  • July 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    ...Jul 22: Web traffic Israeli analytics co SimilarWeb buys Swayy which monitors #socialmedia #BigDataCo t.co/yWUi2R2YOH Jul 28: #BigData + #IoT= Win: @PredixionSW and t.co/tnFTXOfCFs partner to add Advanced #Analytics to #IoT #BigDataCo t.co/Fyxcd3uN02 Jul 16: Workday Launches Workday Ventures To...

    https://www.kdnuggets.com/2015/08/july-analytics-big-data-science-company-activity.html

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