- 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.
- Understanding Learning Rates and How It Improves Performance in Deep Learning - Feb 1, 2018.
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
- The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - Jan 31, 2018.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
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- Automated Text Classification Using Machine Learning - Jan 30, 2018.
In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.
- My Journey into Deep Learning - Jan 30, 2018.
In this post I’ll share how I’ve been studying Deep Learning and using it to solve data science problems. It’s an informal post but with interesting content (I hope).
- Using Deep Learning to Solve Real World Problems - Jan 25, 2018.
Deep learning offers every company with large data new techniques to solve complex analytical problems. Read this ebook to learn more.
- Deep Learning in H2O using R - Jan 22, 2018.
This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.
- Visual Aesthetics: Judging photo quality using AI techniques - Jan 18, 2018.
We built a deep learning system that can automatically analyze and score an image for aesthetic quality with high accuracy. Check the demo and see your photo measures up!
- Generative Adversarial Networks, an overview - Jan 15, 2018.
In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks.
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- AI and Deep Learning in Healthcare – save with code KDnuggets - Jan 11, 2018.
This year, RE-WORK will be continuing the Global Healthcare Series, focusing on the AI and deep learning tools and techniques set to revolutionise healthcare applications, medicine & diagnostics. Save an additional 20% on already discounted passes with the code: KDNUGGETS
- Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions - Jan 11, 2018.
Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
- Custom Optimizer in TensorFlow - Jan 8, 2018.
How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.
- NVIDIA: Sr Deep Learning Software Engineer - Jan 4, 2018.
NVIDIA is seeking a Senior Deep Learning Software Engineer to join their Autonomous Vehicles team to develop state of the art Deep Learning / AI algorithms for our advanced Autonomous driving platform.
- NVIDIA: Deep Learning Inference Software Engineer (TensorRT) - Jan 4, 2018.
NVIDIA is seeking a Senior Deep Learning Inference Software Engineer (TensorRT), and hiring software engineers for its GPU-accelerated Deep learning team.
- NVIDIA: Director – Computer Vision/Deep Learning for Imaging Software - Jan 4, 2018.
Nvidia is seeking an experienced engineering leader to direct our Deep Learning and Computer Vision efforts in Imaging software.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: January and Beyond - Jan 2, 2018.
Coming soon: Deep Learning Summit San Francisco, Data Science Salon Miami, TDWI Las Vegas, BI + Analytics Conference Huntington Beach, Applied AI Summit London, Strata San Jose, and more.
- Computer Vision by Andrew Ng - 11 Lessons Learned - Dec 22, 2017.
I recently completed Andrew Ng’s computer vision course on Coursera. In this article, I will discuss 11 key lessons that I learned in the course.
- DeepSchool.io: Deep Learning Learning - Dec 22, 2017.
What I truly envision for deep school is that this will build a whole lot of Meetup nodes across the world where people will learn, mentor and network around sharing AI knowledge.
- Deep Learning Made Easy with Deep Cognition - Dec 21, 2017.
So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.
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- Deep Learning by Uber – at PAW Vegas 2018 – Best Price Ends Friday - Dec 19, 2017.
Announcing Deep Learning World: The call-for-speakers for the inaugural Deep Learning World, June 3-7, 2018 in Las Vegas is open. Agenda now posted for Predictive Analytics World, Las Vegas – June 3-7, 2018.
- Building an Audio Classifier using Deep Neural Networks - Dec 15, 2017.
Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.
- Learn from Google Brain, DeepMind, Facebook & other AI experts, KDnuggets offer - Dec 14, 2017.
RE•WORK interview leading minds in the field to discuss the impact and progressions of AI on business and in society. The complimentary white paper 'Should You Be Using AI In Your Business?' is now available to download. Save 20% on globally renowned AI and Deep Learning summits with code KDNUGGETS.
- The 10 Deep Learning Methods AI Practitioners Need to Apply - Dec 13, 2017.
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
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- Top Data Science and Machine Learning Methods Used in 2017 - Dec 11, 2017.
The most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests; Deep Learning is used by only 20% of respondents; we also analyze which methods are most "industrial" and most "academic".
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- Top KDnuggets tweets, Nov 29 – Dec 5: Teaching the Data Science Process - Dec 6, 2017.
Also An Introduction to Key Data Science Concepts; Using Deep Learning To Extract Knowledge From Job Descriptions; A General Approach to Preprocessing Text Data; keras-text - A Text Classification Library in #Keras.
- When reinforcement learning should not be used? - Dec 6, 2017.
While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.
- Using Deep Learning to Solve Real World Problems - Dec 4, 2017.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
- InfoGAN - Generative Adversarial Networks Part III - Nov 30, 2017.
In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
- Top KDnuggets tweets, Nov 22-28: Reinforcement Learning: An Introduction by Sutton and Barto – Complete Second Draft - Nov 29, 2017.
Also #DeepLearning Specialization by Andrew Ng - 21 Lessons Learned; How (and Why) to Create a Good Validation Set; Predicting Cryptocurrency Prices With #DeepLearning
- Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras - Nov 29, 2017.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
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- KDnuggets™ News 17:n45, Nov 29: New Poll: Data Science Methods Used? Deep Learning Specialization: 21 Lessons Learned - Nov 29, 2017.
Also The 10 Statistical Techniques Data Scientists Need to Master; Did Spark Really Kill Hadoop? A Framework for Textual Data Science.
- Top KDnuggets tweets, Nov 15-21: DeepLearning is “shallow”: here are underlying concepts you need - Nov 27, 2017.
Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
- Deep Learning Specialization by Andrew Ng – 21 Lessons Learned - Nov 24, 2017.
I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner.
- Understanding Objective Functions in Neural Networks - Nov 23, 2017.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
- Key Takeaways from Open Data Science Conference (ODSC) West 2017 - Nov 21, 2017.
This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.
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- Estimating an Optimal Learning Rate For a Deep Neural Network - Nov 21, 2017.
This post describes a simple and powerful way to find a reasonable learning rate for your neural network.
- NVIDIA DGX Systems – Deep Learning Software Whitepaper - Nov 20, 2017.
Download this whitepaper from NVIDIA DGX Systems, and gain insight into the engineering expertise and innovation found in pre-optimized deep learning frameworks available only on NVIDIA DGX Systems and learn how to dramatically reduce your engineering costs using today’s most popular frameworks.
- Generative Adversarial Networks — Part II - Nov 17, 2017.
Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper.
- Top 10 Videos on Deep Learning in Python - Nov 17, 2017.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
- Deep Learning in Robotics and Healthcare Summits: Join & save with KDnuggets offer - Nov 16, 2017.
RE•WORK are pleased to announce the launch of 'Expo Only Passes' for the upcoming San Francisco events, on January 25 from 14:00 - 18:00. Plus, save 20% on passes to all RE•WORK summits with the code KDNUGGETS.
- Capsule Networks Are Shaking up AI – Here’s How to Use Them - Nov 16, 2017.
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
- Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
A great introductory and high-level summary of Generative Adversarial Networks.
- Real World Deep Learning: Neural Networks for Smart Crops - Nov 7, 2017.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
- Want to know how Deep Learning works? Here’s a quick guide for everyone - Nov 3, 2017.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
- Top KDnuggets tweets, Oct 25-31: 30 Essential Data Science, Machine Learning, Deep Learning Cheat Sheets; Google Brain chief: DL takes at least 100,000 examples - Nov 1, 2017.
Also Applied #AI Summit will give you the tools for your AI journey, 5-7 Feb, London;10 Free Must-Read Books for Machine Learning, Data Science; Ranking Popular #DeepLearning Libraries for #DataScience.
- KDnuggets™ News 17:n42, Nov 1: 7 Steps to Mastering Deep Learning with Keras; 6 Books Every Data Scientist Should Keep Nearby - Nov 1, 2017.
7 Steps to Mastering Deep Learning with Keras; 6 Books Every Data Scientist Should Keep Nearby; Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning; XGBoost: A Concise Technical Overview; AlphaGo Zero: The Most Significant Research Advance in AI
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
- Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning - Oct 28, 2017.
This is a short post for beginners learning neural networks, covering several essential neural networks concepts.
- Hello, World: Building an AI that understands the world through video - Oct 26, 2017.
At TwentyBN, we have created the world’s first AI technology that shows an awareness of its environment and of the actions occurring within it. Our system observes the world through live video and automatically interprets the unfolding visual scene.
- Ranking Popular Deep Learning Libraries for Data Science - Oct 23, 2017.
We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
- TensorFlow: Building Feed-Forward Neural Networks Step-by-Step - Oct 23, 2017.
This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.
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- 5 Free Resources for Furthering Your Understanding of Deep Learning - Oct 20, 2017.
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
- Learn from Tesla, Google Brain, & Facebook – KDnuggets offer - Oct 19, 2017.
Use the code KDNUGGETS to save an additional 20% on our San Francisco events. Sign up before the end of Early Bird registration (tomorrow, October 20) and you will save on top of the RE•WORK discount!
- 7 Types of Artificial Neural Networks for Natural Language Processing - Oct 19, 2017.
What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.
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- Top KDnuggets tweets, Oct 11-17: A Beginners Guide to #DeepLearning - Oct 18, 2017.
Also Collecting #DataScience Cheat Sheets; Luminoth: Open source toolkit for #ComputerVision.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 2: The Pioneers - Oct 18, 2017.
The most anticipated aspect of the RE•WORK Deep Learning Summit Montreal was the assembly of deep learning pioneers Yoshua Bengio, Yann LeCun, and Geoff Hinton on stage separately and together for the first time at such an event.
- KDnuggets™ News 17:n40, Oct 18: Want to Become a Data Scientist? Read This!; Natural Stupidity is more Dangerous than Artificial Intelligence - Oct 18, 2017.
Want to Become a Data Scientist? Read This Interview First; Natural Stupidity is more Dangerous than Artificial Intelligence; Random Forests(r), Explained; Key Trends and Takeaways from RE-WORK Deep Learning Summit Montreal; An Overview of 3 Popular Courses on Deep Learning
- RE•WORK Deep Learning Summit Montreal Panel of Pioneers Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton - Oct 17, 2017.
At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.
- An Overview of 3 Popular Courses on Deep Learning - Oct 13, 2017.
After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.
- KDnuggets™ News 17:n39, Oct 11: Machine Learning to Predict, Explain Attrition; Deep Learning for Object Detection - Oct 11, 2017.
Also How to Choose a Data Science Job; Tidyverse, an opinionated Data Science Toolbox in R; A Quick Guide to Fake News Detection.
- Deep Learning for Object Detection: A Comprehensive Review - Oct 6, 2017.
By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.
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- KDnuggets™ News 17:n38, Oct 4: What Blockchains Mean to Big Data; Keras Deep Learning Cheat Sheet; Machine Learning in Finance - Oct 4, 2017.
Also: XGBoost, a Top Machine Learning Method on Kaggle, Explained; How to win Kaggle competition based on NLP task, if you are not an NLP expert; Fundamental Breakthrough in 2 Decade Old Algorithm Redefines Big Data Benchmarks
- Data Science, AI & Deep Learning Conference – 16 November 2017, London - Oct 2, 2017.
This conference brings together a range of expert practitioners to explore and discuss the new era of AI, Machine Learning and Deep Learning. Participants gain real insights on how to exploit these technological advances for themselves and their organisations in an increasingly ‘data-driven world’.
- Key Takeaways from AI Conference in San Francisco 2017 – Day 2 - Oct 2, 2017.
Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
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- AI Conference in San Francisco, Sep 2017 – highlights and key ideas - Sep 28, 2017.
Highlights from recent AI Conference include the inevitable merger of IQ and EQ in computing, Deep learning to fight cancer, AI as the new electricity and advice from Andrew Ng, Deep reinforcement learning advances and frontiers, and Tim O’Reilly analysis of concerns that AI is the single biggest threat to the survival of humanity.
- Tensorflow Tutorial, Part 2 – Getting Started - Sep 28, 2017.
This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.
- Keras Cheat Sheet: Deep Learning in Python - Sep 27, 2017.
Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models.
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- KDnuggets™ News 17:n37, Sep 27: Essential Data Science & Machine Learning Cheat Sheets; 5 Machine Learning Projects to Check Out Now! - Sep 27, 2017.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; 5 Machine Learning Projects You Can No Longer Overlook - Episode VI; Putting Machine Learning in Production; 5 Ways to Get Started with Reinforcement Learning; Ensemble Learning to Improve Machine Learning Results
- The Search for the Fastest Keras Deep Learning Backend - Sep 26, 2017.
This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.
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- 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
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- Tensorflow Tutorial: Part 1 – Introduction - Sep 21, 2017.
Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.
- 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
- 5 Machine Learning Projects You Can No Longer Overlook – Episode VI - Sep 20, 2017.
Deep learning, data preparation, data visualization, oh my! Check out the latest installation of '5 Machine Learning Projects You Can No Longer Overlook' for insight on... well, what machine learning projects you can no longer overlook.
- Will AI kill us all after taking our jobs? - Sep 15, 2017.
We are now in the middle of an AI hype wave which will decline. This is why I think that AI will take 100 or more years to become sentient, only after completely different AI systems will be created.
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- New-Age Machine Learning Algorithms in Retail Lending - Sep 13, 2017.
We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.
- Object Detection: An Overview in the Age of Deep Learning - Sep 13, 2017.
Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.
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- New Breakthroughs from DeepMind – Relational Networks and Visual Interaction Networks - Sep 12, 2017.
Such relational intelligence separates artificial intelligence systems with human cognition. DeepMind, the creators of AlphaGo, quietly published two groundbreaking research papers into this area, demonstrating a way to train relational reasoning using deep neural networks.
- Top /r/MachineLearning Posts, August: Andrew Ng is back at it; Reinforcement Learning makes a splash; Fixing your ANN - Sep 8, 2017.
Andrew Ng announces new Deep Learning specialization on Coursera; DeepMind and Blizzard open StarCraft II as an AI research environment; OpenAI bot beat best Dota 2 players in 1v1 at The International 2017; My Neural Network isn't working! What should I do?; Deep Learning Neural Networks Play Path of Exile
- Learn from experts at Netflix, Facebook, Tesla, DeepMind & more - Sep 5, 2017.
January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!
- A Vision for Making Deep Learning Simple - Sep 5, 2017.
This post introduces Deep Learning Pipelines from Databricks, a new open-source library aimed at enabling everyone to easily integrate scalable deep learning into their workflows, from machine learning practitioners to business analysts.
- Detecting Facial Features Using Deep Learning - Sep 4, 2017.
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Machine Learning: September 2017 and Beyond - Sep 1, 2017.
Coming soon: Big Data Innovation Summit Boston, O'Reilly AI NYC, Strata NYC, Rework Deep Learning London, The AI Summit San Francisco, and many more.
- Connecting the dots for a Deep Learning App - Aug 31, 2017.
We show how to build a Deep Learning app which does sentiment analysis on movie reviews. Try it yourself!
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- KDnuggets™ News 17:n33, Aug 30: Python Overtakes R in Machine Learning; Data Science in 42 Steps; Deep Learning not AI’s Future - Aug 30, 2017.
Also: KDnuggets part-time, paid internship in Data Science/Machine Learning Journalism; How To Write Better SQL Queries: The Definitive Guide; Understanding overfitting: an inaccurate meme in Machine Learning; How to Become a Data Scientist: The Definitive Guide
- PyTorch or TensorFlow? - Aug 29, 2017.
PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.
- An Intuitive Guide to Deep Network Architectures - Aug 28, 2017.
How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.
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- Deep Learning is not the AI future - Aug 25, 2017.
While Deep Learning had many impressive successes, it is only a small part of Machine Learning, which is a small part of AI. We argue that future AI should explore other ways beyond DL.
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- 42 Steps to Mastering Data Science - Aug 25, 2017.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
- Top KDnuggets tweets, Aug 16-22: Getting Started with #DeepLearning - Aug 23, 2017.
Also: No surprise, Gartner ranks #AI as the top technology to watch in coming years; TOP 100 @medium articles on #AI / #MachineLearning / #DeepLearning.
- PayPal: Applied Research Scientist (AI-ML R&D / NLP / Deep Learning) - Aug 18, 2017.
Seeking an Applied Research Scientist to work on deep learning research for multiple data science applications within the company. There will be access to huge amount of internal data and lots of opportunities to innovate.
- Deep Learning and Neural Networks Primer: Basic Concepts for Beginners - Aug 18, 2017.
This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
- A New Beginning to Deep Learning - Aug 17, 2017.
I won't give you the clichéd line that it's never too late because that's not the point. It is actually because, a term that I loved as soon as I came across it- 'The AI Winter' - doesn't seem to ever be going to return again.
- First Steps of Learning Deep Learning: Image Classification in Keras - Aug 16, 2017.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
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- A Guide to Understanding AI Toolkits - Aug 16, 2017.
This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
- Explore The Future of Deep Learning with @teamrework - Aug 15, 2017.
Until recently, deep learning alluded to the big names in tech such as Amazon, Facebook, and Google as having a clear use for these tools. Whilst these are some of the key players in AI and DL implementation, there are also huge advantages for their applications in businesses and everyday enterprises.
- DeepMind Relational Reasoning Networks Demystified - Aug 15, 2017.
Every time DeepMind publishes a new paper, there is frenzied media coverage around it. We examine what is and is not real in recent work described as “DeepMind Neural Network Can Make Sense of Objects Around It”.
- Deep Learning with Cloudera, Webinar Aug 24 - Aug 14, 2017.
Deep learning expands boundaries of the possible. Detecting fraud. Predicting claims. Diagnosing cancer. Deep learning solves these problems and many others. Find out more with Cloudera, Aug 24.