- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
- Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs - Feb 21, 2019.
We investigate several Natural Language Processing tasks and explain how Deep Learning can help, looking at Language Modeling, Sentiment Analysis, Language Translation, and more.
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
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- Top KDnuggets tweets, Feb 13-19: Intro to Scikit Learn: The Gold Standard of Python ML; The Essential Data Science Venn Diagram - Feb 20, 2019.
Also: Cartoon: #MachineLearning Problems in 2118 #ValentinesDay; A must-read tutorial when you are starting your journey with #DeepLearning.
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
- Deep Learning World Agenda Now Released! - Feb 13, 2019.
The agenda for Deep Learning World Europe has just been released. Industry leaders will gather in Munich to foster progress in the value-driven operationalization of established deep learning methods. Register now and take advantage of Early Bird rates!
- Trending Deep Learning Github Repositories - Feb 1, 2019.
Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.
- What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.
- First sessions confirmed for PAW Industry 4.0 and DLW Munich 2019 – Super Early Bird rates available until Feb 1st - Jan 14, 2019.
Get your ticket now for PAW Industry 4.0 and DLW Munich, 6-7 May 2019, and enter a world full of Predictive Maintenance, Anomaly Detection, Risk Management, Internet of Things, Deep Learning, Machine Learning & many more related topics!
- Biggest Deep Learning Summit – Special KDnuggets Offer - Jan 10, 2019.
At RE•WORK, the team are dedicating 2019 to keep up the high-quality events and to bring you the latest innovations & breakthroughs in AI. RE•WORK are offering a huge saving on all summit passes when you register with the discount code NEWYEAR.
- [Webinar] Accelerating Machine Learning on Databricks - Jan 9, 2019.
In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning.
- NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing - Jan 8, 2019.
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
- Manning Countdown to 2019 – Big Deals on AI, Data Science, Machine Learning books and videos - Dec 28, 2018.
Introducing the Manning countdown to 2019, where each day you’ll be able to get a different one day deal on some of their biggest books and video courses.
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
- World’s Biggest Deep Learning Summit 3 weeks away - Dec 27, 2018.
RE•WORK will be running a New Year's discount next week, but are offering exclusive early access to KDnuggets subscribers - save 25% when you register with the code NEWYEAR before January 11th.
- Deep learning in Satellite imagery - Dec 26, 2018.
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
- Interspeech 2018: Highlights for Data Scientists - Dec 24, 2018.
Key highlights from the Interspeech conference, with topics covering end-to-end models for automatic speech recognition, information theory approach to deep learning, speech processing and education, and more.
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
- How to do Deep Learning with SAS - Dec 18, 2018.
Build a deep learning model using SAS. This paper offers a how-to guide so that you can get up and running.
- NLP Breakthrough Imagenet Moment has arrived - Dec 14, 2018.
A comprehensive review of the current state of Natural Language Processing, covering the process from shallow to deep pre-training, what's in an ImageNet, the case for language modelling, and more.
- State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
- KDnuggets™ News 18:n46, Dec 5: AI, Data Science, Analytics 2018 Main Developments, 2019 Key Trends; Deep Learning Cheat Sheets - Dec 5, 2018.
Also: Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools; How to Build a Machine Learning Team When You Are Not Google or Facebook; A Complete Guide to Choosing the Best Machine Learning Course; Handling Imbalanced Datasets in Deep Learning
- Handling Imbalanced Datasets in Deep Learning - Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: December and Beyond - Dec 3, 2018.
Coming soon: DataX New York, AI-2018 Cambridge UK, AI NEXTCon Seattle, Deep Learning Summit San Francisco, EGC France, H2O San Francisco, Business Of Bots Business of Bots San Francisco, TDWI Las Vegas, WSDM Melbourne, and more.
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
- Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.
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- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
- Deep Learning Cheat Sheets - Nov 28, 2018.
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.
- How to Engineer Your Way Out of Slow Models - Nov 27, 2018.
We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.
- Join the World’s Biggest Deep Learning Summit – KDnuggets Early Cyber Monday - Nov 21, 2018.
RE•WORK are offering an exclusive early discount to KDnuggets subscribers for any of their upcoming AI and Deep Learning Summits when you register before November 30th with the code CYBER25.
- Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU - Nov 15, 2018.
A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.
- Deep Learning Performance Cheat Sheet - Nov 8, 2018.
We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- Introduction to PyTorch for Deep Learning - Nov 7, 2018.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.
- KDnuggets™ News 18:n42, Nov 7: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language: Intro to NLP - Nov 7, 2018.
Also: Machine Learning Classification: A Dataset-based Pictorial; Quantum Machine Learning: A look at myths, realities, and future projections; Multi-Class Text Classification Model Comparison and Selection; Top 13 Python Deep Learning Libraries
- Top 13 Python Deep Learning Libraries - Nov 2, 2018.
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: November and Beyond - Nov 1, 2018.
Coming soon: PASS Summit Seattle, TDWI Orlando, IEEE Conf on Data Mining Singapore, AI & Big Data Innovation Summit 2018 Beijing, Deep Learning World Berlin, PAW Business Berlin, Big Data LDN London, NIPS, and many more.
- Join AI experts from Google Brain, Open AI & Uber AI Labs in San Francisco - Nov 1, 2018.
Join us at the Deep Learning Summit, San Francisco, 24 - 25 Jan 2019. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP, and explore how deep learning will impact all industries.
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
- Introduction to Deep Learning with Keras - Oct 29, 2018.
In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.
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- A Deep Look at Deep Learning: Understanding The Basics of How (and Why) it Works - Oct 23, 2018.
In this illustrated guide by Dataiku you'll learn what exactly deep learning is and why its growing and why it can be more powerful than classical machine learning (ML).
- Google, Microsoft & Fraunhofer at the First European Edition of Deep Learning World – 12 Nov, 2018 - Oct 23, 2018.
You can attend the premier conference covering the commercial deployment of Deep Learning, Nov 12, Berlin, and learn from top industry experts, from our first class conference programme, and from reputable companies to become the best in what you're doing!
- Speak at Mega-PAW Vegas 2019 – on Machine Learning Deployment (Apply by Nov 15) - Oct 22, 2018.
Presenting at PAW is a fulfilling way to engage with the leading members of the machine learning community, offers a chance to share how predictive analytics delivers an impact for your organization, and provides complimentary registration/access to the PAW event.
- Adversarial Examples, Explained - Oct 16, 2018.
Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.
- Preprocessing for Deep Learning: From covariance matrix to image whitening - Oct 10, 2018.
The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!
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- KDnuggets™ News 18:n38, Oct 10: Concise Explanation of Learning Algorithms; Why I Call Myself a Data Scientist; Linear Regression in the Wild - Oct 10, 2018.
This week, KDnuggets brings you a discussion of learning algorithms with a hat tip to Tom Mitchell, discusses why you might call yourself a data scientist, explores machine learning in the wild, checks out some top trends in deep learning, shows you how to learn data science if you are low on finances, and puts forth one person's opinion on the top 8 Python machine learning libraries to help get the job done.
- Big Data Day Camp: Big Data Tools & Techniques (October 25-26) - Oct 4, 2018.
Learn how to use data to make wise, actionable data driven decisions! Our first 2-day camp, Big Data Tools & Techniques, is October 25-26 at Qualcomm Institute, UCSD.
- Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
- Top 3 Trends in Deep Learning - Oct 3, 2018.
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.
- KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R - Oct 3, 2018.
Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning
- Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.
- Join us in Toronto for the Deep Learning Summit, Save with KDnuggets - Sep 28, 2018.
RE•WORK's world-famous Deep Learning Summit is taking place in Toronto this October with the addition of a new AI for Government track. Don't forget to use code KDNUGGETS at the checkout for 20% off your pass. See you there!
- Introduction to Deep Learning - Sep 28, 2018.
I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.
- ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise - Sep 26, 2018.
Key takeaways and highlights from ODSC India 2018 conference about the latest trends, breakthroughs and revolutions in the field of Data Science and Artificial Intelligence
- Power Laws in Deep Learning 2: Universality - Sep 26, 2018.
It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.
- KDnuggets™ News 18:n36, Sep 26: Machine Learning Algorithms From Scratch; Deep Learning Framework Popularity; Data Capture, the Deep Learning Way - Sep 26, 2018.
Also: SQL Case Study: Helping a Startup CEO Manage His Data; Building a Machine Learning Model through Trial and Error; The Whys and Hows of Web Scraping; Unfolding Naive Bayes From Scratch; "Auto-What?" - A Taxonomy of Automated Machine Learning
- “Auto-What?” – A Taxonomy of Automated Machine Learning - Sep 25, 2018.
Automated machine learning is a rapidly developing segment of artificial intelligence - it’s time to define what an AutoML product is so end-users can compare product capabilities intelligently.
- Deep Learning: The Impact of NVIDIA DGX Station - Sep 25, 2018.
Read this IDC report & see how a deep learning workstation may solve IT problems of many researchers, developers, and creative professionals.
- Deep Learning Framework Power Scores 2018 - Sep 24, 2018.
Who’s on top in usage, interest, and popularity?
- Data Capture – the Deep Learning Way - Sep 21, 2018.
An overview of how an information extraction pipeline built from scratch on top of deep learning inspired by computer vision can shakeup the established field of OCR and data capture.
- A Deep (But Jargon Free) Dive Into Deep Learning - Sep 20, 2018.
Learn what exactly deep learning is, how it works, and about its growing and innovative applications in healthcare, finance, retail, and more with this illustrated guide.
- Power Laws in Deep Learning - Sep 20, 2018.
In pretrained, production quality DNNs, the weight matrices for the Fully Connected (FC ) layers display Fat Tailed Power Law behavior.
- Deep Learning on the Edge - Sep 19, 2018.
Detailed analysis into utilizing deep learning on the edge, covering both advantages and disadvantages and comparing this against more traditional cloud computing methods.
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection - Sep 19, 2018.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
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- Top /r/MachineLearning posts, August 2018: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents for sharing enormous datasets - Sep 15, 2018.
A range of interesting posts from the /r/MachineLearning Reddit group for the month of August, including: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents; Getting Alexa to respond to sign language using TensorFlow; PyCharm IDE.
- See NVIDIA Deep Learning In Action [Webinar Series] - Sep 13, 2018.
Hear how three companies benefitted from the performance, simplicity and convenience of NVIDIA DGX Station to supercharge their deep learning development, infusing their products and services with the power of AI.
- KDnuggets™ News 18:n34, Sep 12: Essential Math for Data Science; 100 Days of Machine Learning Code; Drop Dropout - Sep 12, 2018.
Also: Neural Networks and Deep Learning: A Textbook; Don't Use Dropout in Convolutional Networks; Ultimate Guide to Getting Started with TensorFlow.
- Machine Learning Cheat Sheets - Sep 11, 2018.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
- Neural Networks and Deep Learning: A Textbook - Sep 7, 2018.
This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.
- Ultimate Guide to Getting Started with TensorFlow - Sep 6, 2018.
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
- Deep Learning for NLP: An Overview of Recent Trends - Sep 5, 2018.
A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.
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- AI Knowledge Map: How To Classify AI Technologies - Aug 31, 2018.
What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.
- Learn from the experts at Google Brain, UC Berkley, Adobe Research & FAIR - Aug 28, 2018.
The World's Biggest Deep Learning Summit is returning to San Francisco in January 2019. Use code SUMMER for an additional 25% off the Super Early Bird Ticket rate by September 7.
- Nvidia: AI Training for Self-Driving Vehicles [On-demand Webinar] - Aug 27, 2018.
We discuss the key considerations in selecting the optimal AI infrastructure required to train deep neural networks for safe self-driving systems, including data requirements and computing performance needed, and how to use NVIDIA DGX-1 for training autonomous vehicles.
- 9 Things You Should Know About TensorFlow - Aug 22, 2018.
A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.
- Cartoon: Machine Learning takes a vacation - Aug 18, 2018.
August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.
- ebook: Using Deep Learning to Solve Real-World Problems - Aug 14, 2018.
Read this eBook to learn: How deep learning enables image classification, sentiment analysis, and other advanced analysis techniques and get a a starter workflow for building and training deep learning models.
- How GOAT Taught a Machine to Love Sneakers - Aug 7, 2018.
Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.
- Deep Learning Summit returns to Toronto – learn from Geoff Hinton - Jul 31, 2018.
Learn from Geoff Hinton and others at Deep Learning Summit and AI for Government Summit, Oct 25-26 in Toronto. Save 20% with code KDNUGGETS.
- Top KDnuggets tweets, Jul 18-24: Causation in a Nutshell - Jul 25, 2018.
Also fast.ai Deep Learning Part 2 Complete Course Notes; Comparison of Top 6 Python #NLProc Libraries.
- KDnuggets™ News 18:n28, Jul 25: Best (and Free) Resources to Understand Deep Learning; Why Germany did not beat Brazil in the final – Data Science lessons from the World Cup - Jul 25, 2018.
Also 5 Quick and Easy Data Visualizations in Python with Code; Happy 25th Birthday, KDnuggets!
- Best (and Free!!) Resources to Understand Nuts and Bolts of Deep Learning - Jul 19, 2018.
This blog is however not addressing the absolute beginner. Once you have a bit of intuition about how Deep Learning algorithms work, you might want to understand how things work below the hood.
- KDnuggets™ News 18:n27, Jul 18: Data Scientist was the sexiest job until…; Text Mining on the Command Line; Does PCA Really Work? - Jul 18, 2018.
Also: What is Minimum Viable (Data) Product?; Beating the 4-Year Slump: Mid-Career Growth in Data Science; GDPR after 2 months - What does it mean for Machine Learning?; Basic Image Data Analysis Using Numpy and OpenCV; fast.ai Deep Learning Part 2 Complete Course Notes
- fast.ai Deep Learning Part 2 Complete Course Notes - Jul 17, 2018.
This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 2 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
- Key Takeaways from the Strata San Jose 2018 - Jul 16, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
- Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?” - Jul 16, 2018.
By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.
- fast.ai Deep Learning Part 1 Complete Course Notes - Jul 10, 2018.
This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 1 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
- Deep Learning and Challenges of Scale Webinar - Jul 9, 2018.
Join Nvidia for an on-demand webinar to learn how to tackle the challenges of scaling and building complex deep learning systems.
- Deep Learning Tips and Tricks - Jul 4, 2018.
This post is a distilled collection of conversations, messages, and debates on how to optimize deep models. If you have tricks you’ve found impactful, please share them in the comments below!
- Overview and benchmark of traditional and deep learning models in text classification - Jul 3, 2018.
In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.
- Deep Quantile Regression - Jul 3, 2018.
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.
- 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - Jun 25, 2018.
Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
- What is it like to be a machine learning engineer in 2018? - Jun 21, 2018.
A personal account as to why 2018 is going to be a fun year for machine learning engineers.
- Deep Learning Best Practices – Weight Initialization - Jun 21, 2018.
In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward.
- 5 Key Takeaways from Strata London 2018 - Jun 19, 2018.
5 highlights and thoughts from my attendance to Strata London 2018.
- How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning - Jun 13, 2018.
An end-to-end example of how to build a system that can search objects semantically.
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- KDnuggets™ News 18:n23, Jun 13: Did Python declare victory over R?; Master the Netflix Interview; Deep Learning Projects DIY Style - Jun 13, 2018.
Also: Command Line Tricks For Data Scientists; How (dis)similar are my train and test data?; 5 Machine Learning Projects You Should Not Overlook, June 2018; Introduction to Game Theory; Human Interpretable Machine Learning
- DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
- Interview: How Seagate Technology Makes Great Use of Deep Learning – Last Call to Register for Deep Learning World - May 30, 2018.
In anticipation of his upcoming conference co-presentation at Deep Learning World in Las Vegas, June 3-7, we asked Abbas Chokor, Staff Data Scientist at Seagate Technology, a few questions about his work in deep learning.
- Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets - May 29, 2018.
Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS.
Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
- Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM - May 29, 2018.
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
- Deep Learning With Apache Spark: Part 2 - May 23, 2018.
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
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- Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch - May 21, 2018.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
- Find the Right Accelerator for Your Deep Learning Needs - May 17, 2018.
Download the report Find the Right Accelerator for your Deep Learning Needs to learn how I&O leaders must deliver effective machine learning infrastructures that effectively balance performance, cost, and functionality while minimizing complexity.
- An Introduction to Deep Learning for Tabular Data - May 17, 2018.
This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
- KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science - May 16, 2018.
PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data
- Get Hands-On with Deep Learning – New Workshop at Mega-PAW Vegas, June 3-7 - May 15, 2018.
A new full day training workshop has been announced for Predictive Analytics World's Mega-PAW in Las Vegas, Jun 4: Deep Learning in Practice: A Hands-On Introduction. Mega-PAW is Jun 3-7. Register now!
- Deep learning scaling is predictable, empirically - May 10, 2018.
This study starts with a simple question: “how can we improve the state of the art in deep learning?”
- Data Augmentation: How to use Deep Learning when you have Limited Data - May 9, 2018.
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images.
- Detecting Breast Cancer with Deep Learning - May 9, 2018.
Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.
- Can’t-Miss Keynotes at Deep Learning World – June 3-7 in Vegas - May 8, 2018.
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming inaugural Deep Learning World conference in Las Vegas, Jun 3-7.
- Deep Conversations: Lisha Li, Principal at Amplify Partners - May 3, 2018.
Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.
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- How to Make AI More Accessible - Apr 30, 2018.
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
- Ultra-compact workstation for top deep learning frameworks - Apr 27, 2018.
For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We're bringing deep learning to your deskside with the all-new APEXX W3!
- Hear the latest AI advancements in robotics & automation from Uber, Hitachi, Google & more - Apr 26, 2018.
The Summits will bring together 550 experts and 60 speakers using AI and deep learning to improve operations in manufacturing, and creating the next generation of intelligent robots. Save 20% with code KDNUGGETS.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - Apr 25, 2018.
The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
- KDnuggets™ News 18:n17, Apr 25: Python Regular Expressions Cheat Sheet; Deep Learning With Apache Spark; Building a Question Answering Model - Apr 25, 2018.
Also: Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Presto for Data Scientists - SQL on anything; Why Deep Learning is perfect for NLP (Natural Language Processing); Top 16 Open Source Deep Learning Libraries and Platforms
- Low Prices for Mega-PAW End Friday – Predictive Analytics World & Deep Learning World in Vegas - Apr 23, 2018.
This Friday, April 27 is the regular pricing deadline for Mega-PAW 2018, June 3-7 in Las Vegas. Don't miss your chance to save up to $450.00 when you register for the 2018 event.
- Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
- Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
- Wharton: Successful Applications of Customer Analytics – May 9-10, Philadelphia - Apr 18, 2018.
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
- Deep Learning With Apache Spark: Part 1 - Apr 18, 2018.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
- Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
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- KDnuggets™ News 18:n15, Apr 11: Why so many data scientists are leaving their jobs; New Poll: Data Scientist job satisfaction - Apr 11, 2018.
Also Where Analytics, Data Science, Machine Learning Were Applied - Trends and analysis; Top 8 Free Must-Read Books on Deep Learning.
- Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.