- 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!
AI, Books, Deep Learning, Machine Learning
- 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.
Deep Learning, Ensemble Methods, Explainability, LIME, SHAP
- 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.
AI, Deep Learning, Disentanglement, NeurIPS, Representation, Research
- 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.
Complexity, Deep Learning, Model Performance, Regularization
- 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.
2020 Predictions, Agile, Cloud Computing, Data Science Skills, Deep Learning, Deployment, GitHub, NLP
- Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
Deep Learning, Keras, Neural Networks, Overfitting, Python, Regularization, Transfer Learning
- 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.
Coursera, Data Science Education, Deep Learning, edX, Machine Learning, Online Education
- 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.
Deep Learning, Graph Analytics, Graph Databases, Machine Learning, Scalability
- 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.
2020 Predictions, AI, Ajit Jaokar, Analytics, Andriy Burkov, Anima Anandkumar, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Research, Rosaria Silipo, Xavier Amatriain
- 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.
Books, Computer Vision, Courses, Deep Learning, Explainability, Graph Analytics, Interpretability, Machine Learning, NLP, Python
- 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.
Deep Learning, Gradient Descent, Neural Networks, Optimization
- 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.
Advice, AI, Data Science, Data Scientist, Data Visualization, Deep Learning, Facebook, Google, Open Source, Python, Uber
- 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.
Deep Learning, Image Classification, Neural Networks, Small 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.
Complexity, Deep Learning, Dropout, Neural Networks, Overfitting, Regularization, Training Data
- 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.
Accuracy, Deep Learning, Image Classification, Keras, Machine Learning, TensorFlow, Transfer Learning
- 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.
Deep Learning, Neural Networks, Research
- 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.
Deep Learning, Facebook, PyTorch
- 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.
Cancer Detection, Deep Learning, Healthcare, Python
- 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.
Computer Vision, Deep Learning, Neural Networks, Video recognition
- 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.
Architecture, Deep Learning, Neural Networks, Python
- 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.
Andrej Karpathy, Andrew Ng, Deep Learning, Demis Hassabis, Fei-Fei Li, Geoff Hinton, Ian Goodfellow, Influencers, Jeremy Howard, Research, Yann LeCun
- 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.
Career, Data Science Skills, Data Visualization, Deep Learning, Excel, Machine Learning, Poll, Python, PyTorch, Scala, Skills, Statistics, TensorFlow
- 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.
Deep Learning, Pipeline, Python, PyTorch, Workflow
- 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.
Deep Learning, NLP, Speech
- 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, Exxact, Keras, NLP, PyTorch, TensorFlow
- 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.
Attention, Deep Learning, NLP, Transformer
- 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.
Deep Learning, Deployment, Exxact, TensorFlow
- 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.
AI, Deep Learning, Machine Learning
- 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?
Chatbot, Deep Learning, Keras, NLP, Python
- 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.
Deep Learning, Graph Mining, NLP, Top tweets
- 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!
Deep Learning, Explained, LSTM, Neural Networks, NLP, Recurrent Neural Networks
- 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.
Beginners, Cheat Sheet, Deep Learning, Google Colab, Python, PyTorch, Udacity
- 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.
Deep Learning, Deployment, GPU, Inference, NVIDIA
- 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.
Deep Learning, Keras, Programming, Python
- 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.
Big Data, Data Science, DBSCAN, Deep Learning, GPU, NVIDIA, Python
- 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.
Deep Learning, Logistic Regression, Neural Networks, Noise, Random, Sampling, word2vec
- 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.
Accuracy, Deep Learning, Google, Interpretability, Neural Networks
- 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.
Bioinformatics, Deep Learning, DeepMind, Exxact, Generative Adversarial Network, Gradient Descent, Protein
- 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.
Computer Vision, Deep Learning, Image Processing, Python
- 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.
Deep Learning, Deployment, NLP, OpenAI, Scalability, Transformer
- 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.
Adversarial, Cartoon, Deep Learning, Self-Driving Car
- 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.
Computer Vision, Convolutional Neural Networks, Deep Learning, Image Recognition, Object Detection
- 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.
Deep Learning, Differential Privacy, Privacy, Python, Security
- 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.
Algorithms, Deep Learning, Gradient Descent, Optimization
- 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.
Deep Learning, fast.ai, Jeremy Howard, Machine Learning, MOOC
- 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.
Bayesian, Deep Learning, Hyperparameter, Machine Learning, Neural Networks, Optimization, Python, TensorFlow
- 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.
Anaconda, Apache Spark, Big Data Software, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, Tableau, TensorFlow
- 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.
Pages: 1 2
Anaconda, Apache Spark, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, TensorFlow
- 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?
Deep Learning, Lottery, Machine Learning, Neural Networks, Training Data
- 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.
Pages: 1 2
Apache Spark, Big Data, Deep Learning, Machine Learning, NLP, Optimus, Python, Twint
- 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.
AI, Computer Vision, Deep Learning, ImageNet, Machine Learning, Neural Networks
- 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.
AI, Algorithms, Computer Vision, Deep Learning, Machine Learning
- 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.
Advice, AI, Big Data, Data Science, Deep Learning
- 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.
Book, Deep Learning, Graph Databases, Machine Learning, Manning, Search, Search Engine
- 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.
Big Data, Data Mining Software, Data Science, Deep Learning, Machine Learning, Poll, Programming Languages
- 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?
Data Science, Data Scientist, Deep Learning, fast.ai, Keras, Python, PyTorch, TensorFlow
- 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.
Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression
- 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.
Pages: 1 2
Deep Learning, GANs, Generative Adversarial Network, Generative Models, MNIST, Neural Networks, Python
- 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.
AI, Data Science, Deep Learning, Keras, Machine Learning, NLP, Reinforcement Learning, TensorFlow, U. of Washington, UC Berkeley, Unsupervised Learning
- 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.
Big Data Analytics, Deep Learning, Neural Networks, Sisense, SQL
- 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.
Deep Learning, GANs, Generative Adversarial Network, Neural Networks, NVIDIA, Python
- 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.
AI, Computer Vision, Deep Learning, Keras, Object Detection, Retina Net
- 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.
Pages: 1 2
Deep Learning, Feature Engineering, Genetic Algorithm, Neural Networks, numpy, Python, scikit-learn
- 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.
Compression, Convolutional Neural Networks, Deep Learning, ICLR, Inference, Optimization, Regularization
- 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.
Data Science Education, Data Scientist, Deep Learning, Flask, Programming, Python, PyTorch
- 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.
Deep Learning, Google Colab, Keras, Python, TensorFlow, TPU
- 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.
Pages: 1 2
AI, Algorithms, Deep Learning, Machine Learning, Neural Networks, numpy, Optimization, Python
- 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.
Bayesian, Deep Learning, Machine Learning, Sciforce, Text Analysis, Text Mining, Topic Modeling
- 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.
AI, China, Deep Learning, Europe, Investment, IoT, Machine Learning, Neural Networks, Startups, Trends, USA
- 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.
Deep Learning, Image Recognition, Object Detection
- Breaking neural networks with adversarial attacks - Mar 7, 2019.
We develop an intuition behind "adversarial attacks" on deep neural networks, and understand why these attacks are so successful.
Adversarial, Deep Learning, Neural Networks, Privacy
- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
AISC, Attention, Deep Learning, GANs, Image Generation, Machine Learning
- Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Pages: 1 2
Deep Learning, Machine Learning, Neural Networks, numpy, Python
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
Deep Learning, Deep Neural Network, Machine Learning, Neural Networks, Optimization, TensorFlow
- Trending Deep Learning Github Repositories - Feb 1, 2019.
Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.
Deep Learning, GitHub, Trends
- NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing - Jan 8, 2019.
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
Deep Learning, Neural Networks, NLP
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
Decision Trees, Deep Learning, Linear Regression, Logistic Regression, Machine Learning, Neural Networks, SVM
- Deep learning in Satellite imagery - Dec 26, 2018.
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
Deep Learning, Image Recognition, R
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
AI, Algorithms, Big Data, Data Science, Deep Learning, Machine Learning, MIT, NLP, Reinforcement Learning, U. of Washington, UC Berkeley, Yandex
- Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow
- How to do Deep Learning with SAS - Dec 18, 2018.
Build a deep learning model using SAS. This paper offers a how-to guide so that you can get up and running.
Deep Learning, SAS
- State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
Deep Learning, Generative Adversarial Network, NLP, PyTorch, TensorFlow, Trends
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
Cheat Sheet, Data Science Education, Deep Learning, Machine Learning, Mathematics, Open Source, Reinforcement Learning, Resources, Statistics
- Handling Imbalanced Datasets in Deep Learning - Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
Balancing Classes, Datasets, Deep Learning, Keras, Python
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau
- Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.
Pages: 1 2
AI, Deep Learning, Machine Learning, Semantic Analysis
- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow
- Deep Learning Cheat Sheets - Nov 28, 2018.
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.
Cheat Sheet, Deep Learning, Neural Networks
- How to Engineer Your Way Out of Slow Models - Nov 27, 2018.
We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.
Deep Learning, Scalability
- Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU - Nov 15, 2018.
A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.
Cloud Computing, Deep Learning, GPU, TPU
- Deep Learning Performance Cheat Sheet - Nov 8, 2018.
We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.
Cheat Sheet, Deep Learning, Performance
- 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.
Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex
- 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.
Deep Learning, Neural Networks, Python, PyTorch
- 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.
Caffe, Deep Learning, GitHub, MXNet, Python, PyTorch, TensorFlow, Theano
- 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.
Pages: 1 2
Deep Learning, Keras, Neural Networks, Python
- 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.
Adversarial, Deep Learning
- 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!
Pages: 1 2 3
Data Preprocessing, Deep Learning, Image Processing, Mathematics
- 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.
Deep Learning, Image Recognition, Machine Learning, Object Detection, Segmentation
- 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.
Cloud Computing, Deep Learning, MathWorks
- 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.
Deep Learning, Explained, Flat Minima, Linear Networks, Machine Learning, Optimization, SGD
- 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.
Beginners, Deep Learning, Neural Networks
- 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.
Deep Learning, Explained, Neural Networks
- Deep Learning Framework Power Scores 2018 - Sep 24, 2018.
Who’s on top in usage, interest, and popularity?
CNTK, Deep Learning, fast.ai, Java, Keras, MXNet, Python, PyTorch, TensorFlow, Theano
- 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.
Deep Learning, Image Recognition
- 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, Explained, Neural Networks
- 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.
Cloud Computing, Deep Learning, IoT, Security
- 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.
Pages: 1 2
Deep Learning, Image Recognition, Neural Networks, Object Detection, Python
- 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.
Cheat Sheet, Deep Learning, Machine Learning, Mathematics, Neural Networks, Probability, Statistics, Supervised Learning, Tips, Unsupervised Learning
- 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.
Book, Charu Aggarwal, Deep Learning, Neural Networks
- 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, Dropout, Python, 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.
Pages: 1 2
Deep Learning, NLP, Word Embeddings, word2vec
- 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.
AI, Classification, Deep Learning, Machine Intelligence, Machine Learning, Neural Networks
- 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.
Deep Learning, Google, Keras, Machine Learning, Python, TensorFlow
- 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.
Cartoon, Deep Learning, Humor, Machine Learning, Robots
- 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.
Autoencoder, Deep Learning, Image Recognition, Word Embeddings
- 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.
Deep Learning, fast.ai, Jeremy Howard, MOOC
- 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.
Architecture, Deep Learning, Hyperparameter, Neural Networks
- 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, fast.ai, Jeremy Howard, MOOC
- 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 Learning, NLP, Text Classification
- 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.
Deep Learning, Hyperparameter, Keras, Neural Networks, Python, Regression
- 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.
AI, Deep Learning, Machine Learning, NLP
- 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.
Pages: 1 2
Deep Learning, GitHub, Neural Networks, NLP, Semantic Analysis
- 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.
Computer Vision, Data Science, Deep Learning, LinkedIn, Neural Networks, OpenCV, Python
- 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.
CRISP-DM, Deep Learning, Descriptive Analytics, Machine Learning
- 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.
Deep Learning, fast.ai, Kaggle, Neural Networks, Rachel Thomas, word2vec
- 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?”
Deep Learning, Machine Learning, Scalability
- 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.
Data Preparation, Deep Learning
- 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.
Cancer Detection, Deep Learning, Healthcare, Neural Networks
- 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.
Accessibility, AI, Deep Learning, Rachel Thomas, Research
- 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.
Deep Learning, Feature Engineering, NLP, Python, Text Mining