- The Best NLP with Deep Learning Course is Free - May 22, 2020.
Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.
- DeepMind’s Suggestions for Learning #AtHomeWithAI - May 13, 2020.
DeepMind has been sharing resources for learning AI at home on their Twitter account. Check out a few of these suggestions here, and keep your eye on the #AtHomeWithAI hashtag for more.
- KDnuggets™ News 20:n19, May 13: Start Your Machine Learning Career in Quarantine; Will Machine Learning Engineers Exist in 10 Years? - May 13, 2020.
Also: The Elements of Statistical Learning: The Free eBook; Explaining "Blackbox" Machine Learning Models: Practical Application of SHAP; What You Need to Know About Deep Reinforcement Learning; 5 Concepts You Should Know About Gradient Descent and Cost Function; Hyperparameter Optimization for Machine Learning Models
- What You Need to Know About Deep Reinforcement Learning - May 12, 2020.
How does deep learning solve the challenges of scale and complexity in reinforcement learning? Learn how combining these approaches will make more progress toward the notion of Artificial General Intelligence.
- KDnuggets™ News 20:n18, May 6: Five Cool Python Libraries for Data Science; NLP Recipes: Best Practices - May 6, 2020.
5 cool Python libraries for Data Science; NLP Recipes: Best Practices and Examples; Deep Learning: The Free eBook; Demystifying the AI Infrastructure Stack; and more.
- Deep Learning: The Free eBook - May 4, 2020.
"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.
- LSTM for time series prediction - Apr 27, 2020.
Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.
- Fighting Coronavirus With AI: Improving Testing with Deep Learning and Computer Vision - Apr 22, 2020.
This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.
- Math and Architectures of Deep Learning - Apr 22, 2020.
This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. You can save 40% off Math and Architectures of Deep Learning until May 13! Just enter the code nlkdarch40 at checkout when you buy from manning.com.
- KDnuggets™ News 20:n16, Apr 22: Scaling Pandas with Dask for Big Data; Dive Into Deep Learning: The Free eBook - Apr 22, 2020.
4 Steps to ensure your AI/Machine Learning system survives COVID-19; State of the Machine Learning and AI Industry; A Key Missing Part of the Machine Learning Stack; 5 Papers on CNNs Every Data Scientist Should Read
- Livestream Deep Learning World from your Home Office! - Apr 20, 2020.
Livestream Deep Learning World Munich 2020 from the comfort and safety of your home on 11-12 May 2020.
- The Double Descent Hypothesis: How Bigger Models and More Data Can Hurt Performance - Apr 20, 2020.
OpenAI research shows a phenomenon that challenges both traditional statistical learning theory and conventional wisdom in machine learning practitioners.
- Dive Into Deep Learning: The Free eBook - Apr 16, 2020.
This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills.
- How Deep Learning is Accelerating Drug Discovery in Pharmaceuticals - Apr 13, 2020.
The goal of this essay is to discuss meaningful machine learning progress in the real-world application of drug discovery. There’s even a solid chance of the deep learning approach to drug discovery changing lives for the better doing meaningful good in the world.
- KDnuggets™ News 20:n13, Apr 1: Effective visualizations for pandemic storytelling; Machine learning for time series forecasting - Apr 1, 2020.
This week, read about the power of effective visualizations for pandemic storytelling; see how (not) to use machine learning for time series forecasting; learn about a deep learning breakthrough: a sub-linear deep learning algorithm that does not need a GPU?; familiarize yourself with how to painlessly analyze your time series; check out what can we learn from the latest coronavirus trends; and... KDnuggets topics?!? Also, much more.
- Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU? - Mar 26, 2020.
Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.
- The 4 Best Jupyter Notebook Environments for Deep Learning - Mar 19, 2020.
Many cloud providers, and other third-party services, see the value of a Jupyter notebook environment which is why many companies now offer cloud hosted notebooks that are hosted on the cloud. Let's have a look at 3 such environments.
- Francois Chollet on the Future of Keras and Reinforce Conference - Feb 25, 2020.
Ahead of Reinforce Conference in Budapest, we asked Francois Chollet, the creator of Keras, about Keras future, proposed developments, PyTorch, energy efficiency, and more. Listen to him in person in Budapest, April 6-7, and use code KDNuggets to save 15% on conference tickets.
- Audio Data Analysis Using Deep Learning with Python (Part 2) - Feb 25, 2020.
This is a followup to the first article in this series. Once you are comfortable with the concepts explained in that article, you can come back and continue with this.
- Audio Data Analysis Using Deep Learning with Python (Part 1) - Feb 19, 2020.
A brief introduction to audio data processing and genre classification using Neural Networks and python.
- Deep Neural Networks - Feb 14, 2020.
We examine the features and applications of a deep neural network.
- Practical Hyperparameter Optimization - Feb 13, 2020.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
- Top KDnuggets tweets, Feb 05-11: #SciPy 1.0: fundamental algorithms for scientific computing in #Python; Why is Data Science so popular? - Feb 12, 2020.
Why is Data Science so Popular?; Visual Paper Summary: ALBERT (A Lite BERT); Uber Has Assembled One of the Most Impressive Open Source DL Stacks; Top #AI Influencers To Follow in 2020
- Sharing your machine learning models through a common API - Feb 12, 2020.
DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.
- The Data Science Puzzle — 2020 Edition - Feb 7, 2020.
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here.
- OpenAI is Adopting PyTorch… They Aren’t Alone - Jan 31, 2020.
OpenAI is moving to PyTorch for the bulk of their research work. This might be a high-profile adoption, but it is far from the only such example.
- Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About - Jan 30, 2020.
Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.
- Uber Has Been Quietly Assembling One of the Most Impressive Open Source Deep Learning Stacks in the Market - Jan 27, 2020.
Many of the technologies used by Uber teams have been open sourced and received accolades from the machine learning community. Let’s look at some of my favorites.
- Artificial Intelligence Books to Read in 2020 - Jan 21, 2020.
Here are some AI-related books that I’ve read and recommend for you to add to your 2020 reading list!
- Explaining Black Box Models: Ensemble and Deep Learning Using LIME and SHAP - Jan 21, 2020.
This article will demonstrate explainability on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence, using two state of the art open source explainability techniques, LIME and SHAP.
- Learn from Industry Leaders Facebook, Walmart, Lyft, eBay & More - Jan 20, 2020.
The agenda for Deep Learning World 2020 in Las Vegas, May 31-Jun 4, has been released. Use code KDNUGGETS for a 15% discount on your ticket.
- Methods, challenges & applications of Deep Learning | Munich 11-12 May - Jan 16, 2020.
Visit Deep Learning World, 11-12 May in Munich, to broaden your knowledge, deepen your understanding and discuss your questions with other Deep Learning experts!
- Disentangling disentanglement: Ideas from NeurIPS 2019 - Jan 15, 2020.
This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next.
- Applying Occam’s razor to Deep Learning - Jan 10, 2020.
Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.
- Top 5 must-have Data Science skills for 2020 - Jan 8, 2020.
The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.
- Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
- 10 Best and Free Machine Learning Courses, Online - Dec 26, 2019.
Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.
- Xavier Amatriain’s Machine Learning and Artificial Intelligence 2019 Year-end Roundup - Dec 16, 2019.
It is an annual tradition for Xavier Amatriain to write a year-end retrospective of advances in AI/ML, and this year is no different. Gain an understanding of the important developments of the past year, as well as insights into what expect in 2020.
- What just happened in the world of AI? - Dec 12, 2019.
The speed at which AI made advancements and news during 2019 makes it imperative now to step back and place these events into order and perspective. It's important to separate the interest that any one advancement initially attracts, from its actual gravity and its consequential influence on the field. This review unfolds the parallel threads of these AI stories over this year and isolates their significance.
- Scalable graph machine learning: a mountain we can climb? - Dec 10, 2019.
Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
- 10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- Enabling the Deep Learning Revolution - Dec 5, 2019.
Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.
- Popular Deep Learning Courses of 2019 - Dec 3, 2019.
With deep learning and AI on the forefront of the latest applications and demands for new business directions, additional education is paramount for current machine learning engineers and data scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills.
- Two Years In The Life of AI, Machine Learning, Deep Learning and Java - Nov 29, 2019.
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.
- Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
- Deep Learning for Image Classification with Less Data - Nov 20, 2019.
In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.
- Generalization in Neural Networks - Nov 18, 2019.
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.
- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
- KDnuggets™ News 19:n43, Nov 13: Dynamic Reports in Python and R; Creating NLP Vocabularies; What is Data Science? - Nov 13, 2019.
On KDnuggets this week: Orchestrating Dynamic Reports in Python and R with Rmd Files; How to Create a Vocabulary for NLP Tasks in Python; What is Data Science?; The Complete Data Science LinkedIn Profile Guide; Set Operations Applied to Pandas DataFrames; and much, much more.
- Research Guide for Depth Estimation with Deep Learning - Nov 12, 2019.
In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning.
- Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch - Nov 4, 2019.
The new release of PyTorch includes some impressive open source projects for deep learning researchers and developers.
- Harnessing Semiotics and Discourse Communities to Understand User Intent - Oct 25, 2019.
Semiotics helps us understand the importance of context to determining the meaning of a term and discourse communities provide us with the background context (mental model) by which to correctly interpret its meaning correctly.
- Convolutional Neural Network for Breast Cancer Classification - Oct 24, 2019.
See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women.
- Building an intelligent Digital Assistant - Oct 18, 2019.
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
- KDnuggets™ News 19:n39, Oct 16: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI - Oct 16, 2019.
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
- Research Guide for Video Frame Interpolation with Deep Learning - Oct 15, 2019.
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
- There is No Such Thing as a Free Lunch - Oct 11, 2019.
You have heard the expression “there is no such thing as a free lunch” – well in machine learning the same principle holds. In fact there is even a theorem with the same name.
- Activation maps for deep learning models in a few lines of code - Oct 10, 2019.
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.
- Overcoming Deep Learning Stumbling Blocks - Oct 4, 2019.
Find out what was presented at the 6th annual Deep Learning Summit in London where industry leaders, academics, researchers, and innovative startups presenting the latest technological advancements and industry application methods in the field of deep learning.
- 6 Must See Deep Learning Experts at ODSC West 2019 – 20% Off Ends Friday - Oct 3, 2019.
You won’t want to miss the opportunity to learn about the future of deep learning first-hand at ODSC West in San Francisco, Oct 29 - Nov 1. So don’t forget to register soon for 20% off.
- 5 Famous Deep Learning Courses/Schools of 2019 - Sep 24, 2019.
Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills.
- 12 Deep Learning Researchers and Leaders - Sep 23, 2019.
Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.
- Which Data Science Skills are core and which are hot/emerging ones? - Sep 17, 2019.
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
- 5 Step Guide to Scalable Deep Learning Pipelines with d6tflow - Sep 16, 2019.
How to turn a typical pytorch script into a scalable d6tflow DAG for faster research & development.
- A 2019 Guide to Speech Synthesis with Deep Learning - Sep 9, 2019.
In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.
- KDnuggets™ News 19:n33, Sep 4: Data Science Skills Poll; Object-oriented Programming for Data Scientists - Sep 4, 2019.
This week: Object-oriented programming for data scientists; Deep Learning Next Step: Transformers and Attention Mechanism; R Users' Salaries from the 2019 Stackoverflow Survey; Types of Bias in Machine Learning; 4 Tips for Advanced Feature Engineering and Preprocessing; and much more!
- TensorFlow vs PyTorch vs Keras for NLP - Sep 3, 2019.
These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.
- Deep Learning Next Step: Transformers and Attention Mechanism - Aug 29, 2019.
With the pervasive importance of NLP in so many of today's applications of deep learning, find out how advanced translation techniques can be further enhanced by transformers and attention mechanisms.
- TensorFlow 2.0: Dynamic, Readable, and Highly Extended - Aug 27, 2019.
With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.
- Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference? - Aug 26, 2019.
Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.
- Deep Learning for NLP: Creating a Chatbot with Keras! - Aug 19, 2019.
Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?
- Top KDnuggets tweets, Aug 07-13: Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners To Follow - Aug 14, 2019.
Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs.
- KDnuggets™ News 19:n30, Aug 14: Know Your Neighbor: Machine Learning on Graphs; 12 NLP Researchers, Practitioners You Should Follow - Aug 14, 2019.
Machine Learning on Graphs; 12 amazing leaders in NLP; Deep Learning for NLP explained, including ANNs, RNNs and LSTMs; Benford's Law and why is it important for data science; Key concepts in Andrew Ng "Machine Learning Yearning".
- Postdoctoral position (2 years) in multivariate analysis and deep learning [Umeå, Sweden] - Aug 13, 2019.
Help develop new e-science methods that fundamentally integrates Deep Learning and Multivariate analysis. The postdoc position is full-time for a period of two years
- Deep Learning for NLP: ANNs, RNNs and LSTMs explained! - Aug 7, 2019.
Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!
- Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree - Aug 2, 2019.
This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.
- Easily Deploy Deep Learning Models in Production - Aug 1, 2019.
Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.
- How a simple mix of object-oriented programming can sharpen your deep learning prototype - Aug 1, 2019.
By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.
- Here’s how you can accelerate your Data Science on GPU - Jul 30, 2019.
Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.
- A Gentle Introduction to Noise Contrastive Estimation - Jul 25, 2019.
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.
- This New Google Technique Help Us Understand How Neural Networks are Thinking - Jul 24, 2019.
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
- KDnuggets™ News 19:n27, Jul 24: Bayesian deep learning and near-term quantum computers; DeepMind’s CASP13 Protein Folding Upset Summary - Jul 24, 2019.
This week on KDnuggets: Learn how DeepMind dominated the last CASP competition for advancing protein folding models; Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning; The Evolution of a ggplot; Adapters: A Compact and Extensible Transfer Learning Method for NLP; 12 Things I Learned During My First Year as a Machine Learning Engineer; Things I Learned From the SciPy 2019 Lightning Talks; and much more!
- A Summary of DeepMind’s Protein Folding Upset at CASP13 - Jul 17, 2019.
Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.
- Computer Vision for Beginners: Part 1 - Jul 17, 2019.
Image processing is performing some operations on images to get an intended manipulation. Think about what we do when we start a new data analysis. We do some data preprocessing and feature engineering. It’s the same with image processing.
- Scaling a Massive State-of-the-art Deep Learning Model in Production - Jul 15, 2019.
A new NLP text writing app based on OpenAI's GPT-2 aims to write with you -- whenever you ask. Find out how the developers setup and deployed their model into production from an engineer working on the team.
- Cartoon: AI + Self-Driving + BBQ = ? - Jul 4, 2019.
KDnuggets Cartoon looks at what happens when AI and self-driving technology collide with the traditional summer pastime of grilling.
- Nvidia’s New Data Science Workstation — a Review and Benchmark - Jul 3, 2019.
Nvidia has recently released their Data Science Workstation, a PC that puts together all the Data Science hardware and software into one nice package. The workstation is a total powerhouse machine, packed with all the computing power — and software — that’s great for plowing through data.
- Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work - Jul 2, 2019.
As The Transformer may become the new NLP standard, this review explores its architecture along with a comparison to existing approaches by RNN.
- An Overview of Human Pose Estimation with Deep Learning - Jun 28, 2019.
Human Pose Estimation is one of the main research areas in computer vision. The reason for its importance is the abundance of applications that can benefit from such a technology. Here's an introduction to the different techniques used in Human Pose Estimation based on Deep Learning.
- PySyft and the Emergence of Private Deep Learning - Jun 27, 2019.
PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.
- 10 Gradient Descent Optimisation Algorithms + Cheat Sheet - Jun 26, 2019.
Gradient descent is an optimization algorithm used for minimizing the cost function in various ML algorithms. Here are some common gradient descent optimisation algorithms used in the popular deep learning frameworks such as TensorFlow and Keras.
- Why do we need AWS SageMaker? - Jun 26, 2019.
Today, there are several platforms available in the industry that aid software developers, data scientists as well as a layman in developing and deploying machine learning models within no time.
- Do Conv-nets Dream of Psychedelic Sheep? - Jun 25, 2019.
In deep learning, understanding your model well enough to interpret its behavior will help improve model performance and reduce the black-box mystique of neural networks.
- 10 New Things I Learnt from fast.ai Course V3 - Jun 24, 2019.
Fastai offers some really good courses in machine learning and deep learning for programmers. I recently took their "Practical Deep Learning for Coders" course and found it really interesting. Here are my learnings from the course.
- How to Automate Hyperparameter Optimization - Jun 12, 2019.
A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.
- What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem - Jun 10, 2019.
We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.
- Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
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- How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks - May 30, 2019.
The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?
- Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint - May 24, 2019.
Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.
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- Extracting Knowledge from Knowledge Graphs Using Facebook’s Pytorch-BigGraph - May 22, 2019.
We are using the state-of-the-art Deep Learning tools to build a model for predict a word using the surrounding words as labels.
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- Building a Computer Vision Model: Approaches and datasets - May 20, 2019.
How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.
- Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision - May 17, 2019.
Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.
- What’s Going to Happen this Year in the Data World - May 14, 2019.
"If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.
- Books on Graph-Powered Machine Learning, Graph Databases, Deep Learning for Search – 50% off - May 9, 2019.
These 3 books will help you make the most from graph-powered databases. For a limited time, get 50% off any of them with the code kdngraph.
- 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2019.
Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.
- Which Deep Learning Framework is Growing Fastest? - May 1, 2019.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?
- Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
- Meet the World’s Leading AI & Deep Learning Experts - Apr 25, 2019.
RE-WORK returns to San Francisco Jun 20-21 with the Deep Reinforcement Learning Summit, the Applied AI Summit and the AI for Good Summit. KDnuggets subscribers get 20% off Early Bird discounted passes when you register before May 3 with code KDNUGGETS.
- Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
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- Attention Craving RNNS: Building Up To Transformer Networks - Apr 24, 2019.
RNNs let us model sequences in neural networks. While there are other ways of modeling sequences, RNNs are particularly useful. RNNs come in two flavors, LSTMs (Hochreiter et al, 1997) and GRUs (Cho et al, 2014)
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- Approach pre-trained deep learning models with caution - Apr 23, 2019.
Pre-trained models are easy to use, but are you glossing over details that could impact your model performance?
- Earn a Deep Learning Certificate - Apr 22, 2019.
Now is your chance to break into AI, even if you don’t have a PhD. If you want a job in AI and Deep Learning, Andrew Ng’s Specialization will help you get there.
- Hot Deep Learning Applications at Deep Learning World – Las Vegas, June 16-20 - Apr 15, 2019.
Deep Learning World 2019, Jun 16-20 in Las Vegas, will cover a good portion of the wide range of deep learning application areas. Regular prices available until Apr 26. Register now!
- Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
- Training a Champion: Building Deep Neural Nets for Big Data Analytics - Apr 4, 2019.
Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.
- Which Face is Real? - Apr 2, 2019.
Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.
- The Deep Learning Toolset — An Overview - Mar 28, 2019.
We are observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.
- Pedestrian Detection in Aerial Images Using RetinaNet - Mar 26, 2019.
Object Detection in Aerial Images is a challenging and interesting problem. By using Keras to train a RetinaNet model for object detection in aerial images, we can use it to extract valuable information.
- Feature Reduction using Genetic Algorithm with Python - Mar 25, 2019.
This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.
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- Deep Compression: Optimization Techniques for Inference & Efficiency - Mar 20, 2019.
We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.
- Deploy your PyTorch model to Production - Mar 20, 2019.
This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.
- Xing, Heycar & Microsoft on stage at Deep Learning World in Munich - Mar 19, 2019.
Deep Learning World Munich is coming May 6-7 in Munich, Germany. You still have the chance to get your early bird pass until Apr 5. Secure your ticket for a lower price and access case studies and deep dives covering the commercial deployment of deep learning!
- How to Train a Keras Model 20x Faster with a TPU for Free - Mar 19, 2019.
This post shows how to train an LSTM Model using Keras and Google CoLaboratory with TPUs to exponentially reduce training time compared to a GPU on your local machine.
- Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.
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- Deep Learning Summit Boston – Meet RE•WORK’s Newest Speakers - Mar 14, 2019.
RE-WORK returns to Boston in May to showcase global experts in Deep Learning. Get up to 50% off passes with code KDNUGGETS if you register before March 22.
- Top KDnuggets tweets, Mar 06-12: Most impactful AI trends of 2018; Google open-sources GPipe for efficiently training large deep neural networks - Mar 13, 2019.
The rise of ML Engineering; Build your own Robust #DeepLearning Environment in Minutes; Another 10 Free Must-Read Books for Machine Learning and Data Science; Top 5 #MachineLearning Courses for 2019 - from @Coursera and @EdX.
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
- AI: Arms Race 2.0 - Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
- People Tracking using Deep Learning - Mar 12, 2019.
Read this overview of people tracking and how deep learning-powered computer vision has allowed for phenomenal performance.