- KDnuggets™ News 21:n43, Nov 10: Data Scientist Career Path from Novice to First Job; Neural Networks from a Bayesian Perspective - Nov 10, 2021.
Data Scientist Career Path: from Novice to First Job; Understand Neural Networks from a Bayesian Perspective; The Best Ways for Data Professionals to Market AWS Skills; Build Your Own Automated Machine Learning App.
- A First Principles Theory of Generalization - Nov 4, 2021.
Some new research from University of California, Berkeley shades some new light into how to quantify neural networks knowledge.
- Neural Networks from a Bayesian Perspective - Nov 3, 2021.
This article looks at neural networks from a Bayesian perspective.
- My AI Plays Piano for Me - Oct 6, 2021.
Training an RNN with a Combined Loss Function.
- Introduction to PyTorch Lightning - Oct 4, 2021.
PyTorch Lightning is a high-level programming layer built on top of PyTorch. It makes building and training models faster, easier, and more reliable.
- Introducing TensorFlow Similarity - Sep 17, 2021.
TensorFlow Similarity is a newly-released library from Google that facilitates the training, indexing and querying of similarity models. Check out more here.
- Speeding up Neural Network Training With Multiple GPUs and Dask - Sep 14, 2021.
A common moment when training a neural network is when you realize the model isn’t training quickly enough on a CPU and you need to switch to using a GPU. It turns out multi-GPU model training across multiple machines is pretty easy with Dask. This blog post is about my first experiment in using multiple GPUs with Dask and the results.
- KDnuggets™ News 21:n29, Aug 4: GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Aug 4, 2021.
GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks; A Brief Introduction to the Concept of Data; MLOps Best Practices; GPU-Powered Data Science (NOT Deep Learning) with RAPIDS
- How to Tell if You Have Trained Your Model with Enough Data - Jul 12, 2021.
WeightWatcher is an open-source, diagnostic tool for evaluating the performance of (pre)-trained and fine-tuned Deep Neural Networks. It is based on state-of-the-art research into Why Deep Learning Works.
- How to Build An Image Classifier in Few Lines of Code with Flash - Jul 7, 2021.
Introducing Flash: The high-level deep learning framework for beginners.
- Computational Complexity of Deep Learning: Solution Approaches - Jun 29, 2021.
Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can?
- Similarity Search: Euclid of Alexandria goes shoe shopping - Jun 2, 2021.
Many applications can be improved with similarity search. Similarity search can provide more relevant results and therefore improve business outcomes such as conversion rates, engagement rates, detected threats, data quality, and customer satisfaction.
- Machine Translation in a Nutshell - May 17, 2021.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.
- What is Neural Search? - May 6, 2021.
And how to get started with it with no prior experience in Machine Learning.
- KDnuggets™ News 21:n17, May 5: Charticulator: Microsoft Research open-source game-changing Data Visualization platform; Data Science to Predict and Prevent Real World Problems - May 5, 2021.
Charticulator: Microsoft Research game-changing Data Visualization platform; How Data Science is used to predict and prevent real world problems; Hilarious Data Science Humor; Neural Networks for Natural Language Processing Now; and more.
- Learn Neural Networks for Natural Language Processing Now - Apr 30, 2021.
Still haven't come across enough quality contemporary natural language processing resources? Here is yet another freely-accessible offering from a top-notch university that might help quench your thirst for learning materials.
- 10 Real-Life Applications of Reinforcement Learning - Apr 12, 2021.
In this article, we’ll look at some of the real-world applications of reinforcement learning.
- Microsoft Research Trains Neural Networks to Understand What They Read - Apr 7, 2021.
The new models make inroads in a new areas of deep learning known as machine reading comprehension.
- 3 More Free Top Notch Natural Language Processing Courses - Mar 31, 2021.
Are you looking to continue your learning of natural language processing? This small collection of 3 free top notch courses will allow you to do just that.
- Explainable Visual Reasoning: How MIT Builds Neural Networks that can Explain Themselves - Mar 30, 2021.
New MIT research attempts to close the gap between state-of-the-art performance and interpretable models in computer vision tasks.
- Deep Learning Is Becoming Overused - Mar 29, 2021.
Understanding the data is the first port of call.
- Reducing the High Cost of Training NLP Models With SRU++ - Mar 4, 2021.
The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.
- Google’s Model Search is a New Open Source Framework that Uses Neural Networks to Build Neural Networks - Mar 1, 2021.
The new framework brings state-of-the-art neural architecture search methods to TensorFlow.
- Deep Learning-based Real-time Video Processing - Feb 17, 2021.
In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.
- IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks - Feb 15, 2021.
Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.
- 2011: DanNet triggers deep CNN revolution - Feb 4, 2021.
In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.
- Working With The Lambda Layer in Keras - Jan 28, 2021.
In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.
- Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI - Jan 26, 2021.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
- Mastering TensorFlow Variables in 5 Easy Steps - Jan 20, 2021.
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.
- Graph Representation Learning: The Free eBook - Jan 19, 2021.
This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models.
- 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 7, 2021.
Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.
- Generating Beautiful Neural Network Visualizations - Dec 30, 2020.
If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out.
- Optimization Algorithms in Neural Networks - Dec 18, 2020.
This article presents an overview of some of the most used optimizers while training a neural network.
- How to Create Custom Real-time Plots in Deep Learning - Dec 14, 2020.
How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.
- Deploying Trained Models to Production with TensorFlow Serving - Nov 30, 2020.
TensorFlow provides a way to move a trained model to a production environment for deployment with minimal effort. In this article, we’ll use a pre-trained model, save it, and serve it using TensorFlow Serving.
- A Friendly Introduction to Graph Neural Networks - Nov 30, 2020.
Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.
- How to Know if a Neural Network is Right for Your Machine Learning Initiative - Nov 26, 2020.
It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.
- Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
- From Y=X to Building a Complete Artificial Neural Network - Nov 13, 2020.
In this tutorial, we will start with the most simple artificial neural network (ANN) and move to something much more complex. We begin by building a machine learning model with no parameters—which is Y=X.
- How to deploy PyTorch Lightning models to production - Nov 5, 2020.
A complete guide to serving PyTorch Lightning models at scale.
- Building Neural Networks with PyTorch in Google Colab - Oct 30, 2020.
Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that.
- Roadmap to Computer Vision - Oct 26, 2020.
Read this introduction to the main steps which compose a computer vision system, starting from how images are pre-processed, features extracted and predictions are made.
- Getting Started with PyTorch - Oct 14, 2020.
A practical walkthrough on how to use PyTorch for data analysis and inference.
- Understanding Transformers, the Data Science Way - Oct 1, 2020.
Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
- Looking Inside The Blackbox: How To Trick A Neural Network - Sep 28, 2020.
In this tutorial, I’ll show you how to use gradient ascent to figure out how to misclassify an input.
- The Most Complete Guide to PyTorch for Data Scientists - Sep 24, 2020.
All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.
- KDnuggets™ News 20:n36, Sep 23: New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project - Sep 23, 2020.
New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python; Online Certificates/Courses in AI, Data Science, Machine Learning; Can Neural Networks Show Imagination?
- Implementing a Deep Learning Library from Scratch in Python - Sep 17, 2020.
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
- Can Neural Networks Show Imagination? DeepMind Thinks They Can - Sep 16, 2020.
DeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.
- Autograd: The Best Machine Learning Library You’re Not Using? - Sep 16, 2020.
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.
- AI Papers to Read in 2020 - Sep 10, 2020.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
- How Do Neural Networks Learn? - Aug 17, 2020.
With neural networks being so popular today in AI and machine learning development, they can still look like a black box in terms of how they learn to make predictions. To understand what is going on deep in these networks, we must consider how neural networks perform optimization.
- Batch Normalization in Deep Neural Networks - Aug 7, 2020.
Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.
- Deep Learning for Signal Processing: What You Need to Know - Jul 27, 2020.
Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.
- KDnuggets™ News 20:n28, Jul 22: Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning - Jul 22, 2020.
Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning; Building a REST API with Tensorflow Serving (Part 1); 3 Advanced Python Features You Should Know; Understanding How Neural Networks Think;
- PyTorch for Deep Learning: The Free eBook - Jul 7, 2020.
For this week's free eBook, check out the newly released Deep Learning with PyTorch from Manning, made freely available via PyTorch's website for a limited time. Grab it now!
- Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston Neuron - Jun 25, 2020.
DeepMind’s research shows how to understand the role of individual neurons in a neural network.
- The Most Important Fundamentals of PyTorch you Should Know - Jun 18, 2020.
PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.
- Introduction to Convolutional Neural Networks - Jun 3, 2020.
The article focuses on explaining key components in CNN and its implementation using Keras python library.
- 5 Machine Learning Papers on Face Recognition - May 28, 2020.
This article will highlight some of that research and introduce five machine learning papers on face recognition.
- Are Tera Operations Per Second (TOPS) Just hype? Or Dark AI Silicon in Disguise? - May 27, 2020.
This article explains why TOPS isn’t as accurate a gauge as many people think, and discusses other criteria that should be considered when evaluating a solution to a real application.
- Satellite Image Analysis with fast.ai for Disaster Recovery - May 14, 2020.
We were asked to build ML models using the novel xBD dataset provided by the organizers to estimate damage to infrastructure with the goal of reducing the amount of human labour and time required to plan an appropriate response. This article will focus on the technical aspects of our solution and share our experiences.
- 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.
- 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.
- Introducing Brain Simulator II: A New Platform for AGI Experimentation - Apr 29, 2020.
A growing consensus of researchers contend that new algorithms are needed to transform narrow AI to AGI. Brain Simulator II is free software for new algorithm development targeted at AGI that you can experiment with and participate in its development.
- 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.
- OpenAI Open Sources Microscope and the Lucid Library to Visualize Neurons in Deep Neural Networks - Apr 17, 2020.
The new tools shows the potential of data visualizations for understanding features in a neural network.
- Build PyTorch Models Easily Using torchlayers - Apr 9, 2020.
torchlayers aims to do what Keras did for TensorFlow, providing a higher-level model-building API and some handy defaults and add-ons useful for crafting PyTorch neural networks.
- 10 Must-read Machine Learning Articles (March 2020) - Apr 9, 2020.
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.
- 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning - Apr 8, 2020.
Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?
- Graph Neural Network model calibration for trusted predictions - Mar 24, 2020.
In this article, we’ll talk about calibration in graph machine learning, and how it can help to build trust in these powerful new models.
- Build an Artificial Neural Network From Scratch: Part 2 - Mar 20, 2020.
The second article in this series focuses on building an Artificial Neural Network using the Numpy Python library.
- Generate Realistic Human Face using GAN - Mar 10, 2020.
This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator.
- TensorFlow 2.0 Tutorial: Optimizing Training Time Performance - Mar 5, 2020.
Tricks to improve TensorFlow training time with tf.data pipeline optimizations, mixed precision training and multi-GPU strategies.
- Recreating Fingerprints using Convolutional Autoencoders - Mar 4, 2020.
The article gets you started working with fingerprints using Deep Learning.
- KDnuggets™ News 20:n07, Feb 19: 20 AI, Data Science, Machine Learning Terms for 2020; Why Did I Reject a Data Scientist Job? - Feb 19, 2020.
This week on KDnuggets: 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020; Why Did I Reject a Data Scientist Job?; Fourier Transformation for a Data Scientist; Math for Programmers; Deep Neural Networks; Practical Hyperparameter Optimization; and much more!
- Deep Neural Networks - Feb 14, 2020.
We examine the features and applications of a deep neural network.
- A bird’s-eye view of modern AI from NeurIPS 2019 - Jan 28, 2020.
With the explosion of the field of AI/ML impacting so many applications and industries, there is great value coming out of recent progress. This review highlights many research areas covered at the NeurIPS 2019 conference recently held in Vancouver, Canada, and features many important areas of progress we expect to see in the coming year.
- Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem - Jan 20, 2020.
The new algorithm takes a novel approach to neural architecture search.
- Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks - Jan 13, 2020.
The new technique can really improve how deep learning models are trained at scale.
- Top KDnuggets tweets, Dec 18-30: A Gentle Introduction to Math Behind Neural Networks - Dec 31, 2019.
A Gentle Introduction to #Math Behind #NeuralNetworks; Learn How to Quickly Create UIs in Python; I wanna be a data scientist, but... how!?; I created my own deepfake in two weeks
- Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
- Random Forest® vs Neural Networks for Predicting Customer Churn - Dec 26, 2019.
Let us see how random forest competes with neural networks for solving a real world business problem.
- 5 Techniques to Prevent Overfitting in Neural Networks - Dec 6, 2019.
In this article, I will present five techniques to prevent overfitting while training neural networks.
- 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.
- KDnuggets™ News 19:n45, Nov 27: Interpretable vs black box models; Advice for New and Junior Data Scientists - Nov 27, 2019.
This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification
- Can Neural Networks Develop Attention? Google Thinks they Can - Nov 25, 2019.
Google recently published some work about modeling attention mechanisms in deep neural networks.
- Neural Networks 201: All About Autoencoders - Nov 21, 2019.
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
- 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.
- 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.
- Designing Your Neural Networks - Nov 4, 2019.
Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.
- Build an Artificial Neural Network From Scratch: Part 1 - Nov 1, 2019.
This article focused on building an Artificial Neural Network using the Numpy Python library.
- KDnuggets™ News 19:n40, Oct 23: How to Become a (Good) Data Scientist; Writing Your First Neural Net in 30 Lines with Keras - Oct 23, 2019.
Read useful advice on how to become a good data scientist; see how you can write your 1st neural net in under 30 lines of Keras code; Understand why AI salaries are heading skywards and what skills you need for them; and read about key ideas and methods in anomaly detection
- This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training - Oct 21, 2019.
Recently, a group of AI experts from Microsoft Research published a paper proposing a method for scene understanding that combines two key tasks: image captioning and visual question answering (VQA).
- Writing Your First Neural Net in Less Than 30 Lines of Code with Keras - Oct 18, 2019.
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
- 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.
- Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search - Oct 14, 2019.
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
- 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.
- Introduction to Artificial Neural Networks - Oct 8, 2019.
In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.
- Research Guide for Neural Architecture Search - Oct 4, 2019.
In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.
- Recreating Imagination: DeepMind Builds Neural Networks that Spontaneously Replay Past Experiences - Oct 3, 2019.
DeepMind researchers created a model to be able to replay past experiences in a way that simulate the mechanisms in the hippocampus.
- A Gentle Introduction to PyTorch 1.2 - Sep 20, 2019.
This comprehensive tutorial aims to introduce the fundamentals of PyTorch building blocks for training neural networks.
- KDnuggets™ News 19:n32, Aug 28: Handy SQL Features for Data Scientists; Nothing but NumPy: Creating Neural Networks with Computational Graphs - Aug 28, 2019.
Most useful SQL features for Data Scientist; Excellent tutorial on creating neural nets from scratch with Numpy; TensorFlow 2.0 highlights, explained; How to sell your boss on Data Analytics; and more.
- Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 23, 2019.
Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.
- Pytorch Lightning vs PyTorch Ignite vs Fast.ai - Aug 16, 2019.
Here, I will attempt an objective comparison between all three frameworks. This comparison comes from laying out similarities and differences objectively found in tutorials and documentation of all three frameworks.
- Keras Callbacks Explained In Three Minutes - Aug 9, 2019.
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.
- 9 Tips For Training Lightning-Fast Neural Networks In Pytorch - Aug 9, 2019.
Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.
- 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!
- Top KDnuggets tweets, Jul 24-30: Nothing but NumPy: Understanding and Creating Neural Nets w. Computational Graphs from Scratch; How Netflix works - Jul 31, 2019.
How Netflix works: the (hugely simplified) complex stuff that happens every time; Top Certificates and Certifications in Analytics, Data Science, ML; Nothing but NumPy: Understanding &Creating Neural Networks with Computation.
- Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras - Jul 26, 2019.
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.
- 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.
- Neural Code Search: How Facebook Uses Neural Networks to Help Developers Search for Code Snippets - Jul 24, 2019.
Developers are always searching for answers to questions about their code. But how do they ask the right questions? Facebook is creating new NLP neural networks to help search code repositories that may advance information retrieval algorithms.
- 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.
- Training a Neural Network to Write Like Lovecraft - Jul 11, 2019.
In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.
- Evolving Deep Neural Networks - Jun 18, 2019.
This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural network design.
- 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.
- KDnuggets™ News 19:n22, Jun 12: The Modern Open-Source Data Science/Machine Learning Ecosystem; Simplifying the Data Visualisation Process in Python - Jun 12, 2019.
The 6 tools in the modern open-source Data Science ecosystem; Simplifying the Data Visualisation Process in Python; The Infinity Stones of Data Science; Best resources for developers transitioning into data science.
- Random Forests® vs Neural Networks: Which is Better, and When? - Jun 7, 2019.
Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
- Understanding Backpropagation as Applied to LSTM - May 30, 2019.
Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.
- 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?
- 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.
- Large-Scale Evolution of Image Classifiers - May 16, 2019.
Deep neural networks excel in many difficult tasks, given large amounts of training data and enough processing power. The neural network architecture is an important factor in achieving a highly accurate model... Techniques to automatically discover these neural network architectures are, therefore, very much desirable.
- 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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- KDnuggets™ News 19:n14, Apr 10: Which Data Science/ML methods and algorithms you used? Predict Age and Gender Using Neural Nets - Apr 10, 2019.
Getting started with NLP using the PyTorch framework; Building a Recommender System; Advice for New Data Scientists; All you need to know about text preprocessing for NLP and Machine Learning; Advanced Keras - Constructing Complex Custom Losses and Metrics; Top 8 Data Science Use Cases in Gaming
- Advanced Keras — Constructing Complex Custom Losses and Metrics - Apr 8, 2019.
In this tutorial I cover a simple trick that will allow you to construct custom loss functions in Keras which can receive arguments other than
- 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.
- Getting started with NLP using the PyTorch framework - Apr 3, 2019.
We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers.
- 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.