- 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.
- Are New Technologies Killing Their Ancestors? - Apr 6, 2018.
Are automatic feature learning models (e.g. CNN) killing their previous manually engineered models? This is an important question that is to be answered in this article.
- Top KDnuggets tweets, Mar 28 – Apr 03: A “Weird” Intro to Deep Learning; Why so many data scientists are leaving their jobs - Apr 4, 2018.
Also: Don't learn #MachineLearning in 24 hours; Introduction to Functional Programming in #Python
- KDnuggets™ News 18:n14, Apr 4: How Do I Get My First Data Science Job? A “Weird” Intro to Deep Learning; Top 20 DL papers - Apr 4, 2018.
Also: A Day in the Life of a Data Scientist: Part 4; Understanding Feature Engineering: Deep Learning Methods for Text Data.
- Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
- A “Weird” Introduction to Deep Learning - Mar 30, 2018.
There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.
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- Semantic Segmentation Models for Autonomous Vehicles - Mar 29, 2018.
State-of-the-art Semantic Segmentation models need to be tuned for efficient memory consumption and fps output to be used in time-sensitive domains like autonomous vehicles.
- Wharton: Successful Applications of Customer Analytics, May 9-10, Philadelphia - Mar 28, 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.
- Understanding Feature Engineering: Deep Learning Methods for Text Data - Mar 28, 2018.
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.
- Exploring DeepFakes - Mar 27, 2018.
In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.
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- Comparing Deep Learning Frameworks: A Rosetta Stone Approach - Mar 26, 2018.
A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.
- Multiscale Methods and Machine Learning - Mar 19, 2018.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
- Top KDnuggets tweets, Mar 7-13: #MachineLearning Algorithms: Which One to Choose for Your Problem; Train Your ML Models on Google GPUs for Free - Mar 15, 2018.
Also Introduction to Markov Chains; 18 Inspiring Women In #AI, #Analytics #BigData, #DataScience, #MachineLearning.
- Meet experts in AI & Industrial Automation in San Francisco: Save with KDnuggets - Mar 13, 2018.
This June 18-19, RE-WORK will be returning to San Francisco to host the Deep Learning for Robotics Summit and the AI in Industrial Automation Summit. Save 20% with the code KDNUGGETS
- Get ready for smart apps - Mar 8, 2018.
Mobile platforms are set to benefit from Deep Learning this year, with significant improvements in privacy, offline functionality and much more. But which Android phone should you purchase to maximise these benefits?
- Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.
- KDnuggets™ News 18:n10, Mar 7: Functional Programming in Python; Surviving Your Data Science Interview; Easy Image Recognition with Google Tensorflow - Mar 7, 2018.
- The 5th AI+Blockchain NEXTCon, Santa Clara, April 10-13, 2018 - Mar 5, 2018.
The 5th AI+Blockchain NEXTCon brings 50+ tech lead speakers from Microsoft, Google, Facebook, LinkedIn, Uber, other leading firms to share best practices and solutions in machine learning, deep learning, NLP, Data science, Blockchain and more. Save 30% by Mar 9 with code KDNUGGET100.
- Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.
- Deep Misconceptions About Deep Learning - Mar 5, 2018.
I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others.
- Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and #MachineLearning Open Source Projects; Intro to Reinforcement Learning Algorithms - Feb 28, 2018.
Also: #NeuralNetwork #AI is simple. So... Stop pretending; 5 Free Resources for Getting Started with #DeepLearning for Natural Language Pro; Want a Job in #Data? Learn This
- The Current Hype Cycle in Artificial Intelligence - Feb 28, 2018.
Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.
- Age of AI Conference 2018 – Day 2 Highlights - Feb 23, 2018.
Here are some of the highlights from the second day of the Age of AI Conference, February 1, at the Regency Ballroom in San Francisco.
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- 5 Fantastic Practical Natural Language Processing Resources - Feb 22, 2018.
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
- PAW Vegas Early Bird Ends This Friday — Deep Learning and 4 Vertical Events - Feb 20, 2018.
Predictive Analytics World and Deep Learning World conferences are coming to Caesars Palace in Las Vegas, Jun 3-7. It's not too late to save with the Early Bird and attend the biggest PAW mega-event ever.
- Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 20, 2018.
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
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- Deep Learning World Vegas – Talks from Cisco, Cap1, Lyft, Qantas, Uber… - Feb 19, 2018.
The inaugural Deep Learning World heads to Caesar's Palace Las Vegas, Jun 3-7, alongside Predictive Analytics World. Early Bird pricing ends Friday – Register now!
- Neural network AI is simple. So… Stop pretending you are a genius - Feb 15, 2018.
This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time.
- Building a Toy Detector with Tensorflow Object Detection API - Feb 13, 2018.
This project is second phase of my popular project - Is Google Tensorflow Object Detection API the easiest way to implement image recognition? Here I extend the API to train on a new object that is not part of the COCO dataset.
- A Basic Recipe for Machine Learning - Feb 13, 2018.
One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.
- 4 Things You Probably Didn’t Know Machine Learning and AI was used for - Feb 12, 2018.
AI was compared to the discovery of fire, but its impact hinges on how creative we are with the technology—just like it did for early humans employing fire. Here are four diverse examples of applied AI to get your creative juices flowing.
- Join RE•WORK & AI experts in London, Boston and Hong Kong – KDNUGGETS discount - Feb 8, 2018.
Miss RE•WORK in San Francisco? Join AI and deep learning experts in London, Hong Kong or Boston, and save 25% with the code KDNUGGETS.
- Fast.ai Lesson 1 on Google Colab (Free GPU) - Feb 8, 2018.
In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?
- 5 Fantastic Practical Machine Learning Resources - Feb 6, 2018.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.