Neural Networks (301)

• 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, Research, UC Berkeley

• Neural Networks from a Bayesian Perspective - Nov 3, 2021.
  
  This article looks at neural networks from a Bayesian perspective.

  Bayesian, Neural Networks

• 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.

  Deep Learning, Neural Networks, PyTorch, PyTorch Lightning

• 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.

  Google, Neural Networks, TensorFlow

• 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.

  Dask, GPU, Neural Networks, Training

• 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?

  Complexity, Deep Learning, Neural Networks

• 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.

  Neural Networks, Pinecone, Recommender Systems, Search

• 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.

  Machine Translation, Neural Networks, NLP, Text Analytics

• What is Neural Search? - May 6, 2021.
  
  And how to get started with it with no prior experience in Machine Learning.

  Neural Networks, NLP, Search, Search Engine

• 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.

  CMU, Courses, Neural Networks, NLP

• 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.

  Andrew Ng, CMU, Coursera, Courses, deeplearning.ai, Neural Networks, NLP

• 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.

  Deep Learning, Machine Learning, Neural Networks, NLP

• 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.

  Automated Machine Learning, AutoML, Google, Neural Networks, Open Source

• 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.

  Computer Vision, Deep Learning, Neural Networks, Video recognition

• 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.

  IBM, Learning, Neural Networks, Training

• 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.

  AI, Convolutional Neural Networks, History, Jurgen Schmidhuber, Neural Networks

• 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.

  Architecture, Keras, Neural Networks, Python

• 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.

  AI, Capsule Networks, Deep Learning, Geoff Hinton, Neural Networks, Research

• 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.

  Neural Networks, Python, TensorFlow

• 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.

  Data Science, Free ebook, Graph, Neural Networks, Representation

• 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.

  Deployment, Neural Networks, Python, UI/UX

• 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.

  Neural Networks, Python, Visualization

• 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.

  Gradient Descent, Neural Networks, Optimization

• 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.

  Data Visualization, Deep Learning, Keras, Metrics, Neural Networks, Python

• 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.

  Deployment, Modeling, Neural Networks, Python, TensorFlow

• 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.

  Graph, Neural Networks, Recurrent Neural Networks

• 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.

  Algorithms, Machine Learning, Neural Networks

• 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.

  Computer Vision, Data Science, Deep Learning, Machine Learning, Neural Networks, NLP, Python

• How to deploy PyTorch Lightning models to production - Nov 5, 2020.
  
  A complete guide to serving PyTorch Lightning models at scale.

  Deployment, Neural Networks, Production, Python, PyTorch, PyTorch Lightning

• 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.

  Deep Learning, Google Colab, Neural Networks, Python, PyTorch

• 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.

  Computer Vision, Convolutional Neural Networks, Data Preprocessing, Neural Networks, Roadmap

• Getting Started with PyTorch - Oct 14, 2020.
  
  A practical walkthrough on how to use PyTorch for data analysis and inference.

  Neural Networks, Python, PyTorch

• 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.

  Data Science, Neural Networks, NLP, Transformer

• 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.

  Neural Networks, Python, PyTorch, PyTorch Lightning

• 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.

  Data Science, Data Scientist, Neural Networks, Python, PyTorch

• 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?

  Automation, Certificate, Courses, Data Science, Deep Learning, DeepMind, Machine Learning, Neural Networks, Python

• 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.

  Deep Learning, Neural Networks, Python

• 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.

  Agents, AI, Creativity, DeepMind, Neural Networks

• 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.

  Deep Learning, Neural Networks, Python, PyTorch

• 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.

  AI, Attention, Convolutional Neural Networks, Data Science, GANs, Neural Networks, Reformer, Research

• 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.

  Beginners, Neural Networks

• 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, Neural Networks, Normalization, Regularization

• 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.

  Deep Learning, Neural Networks

• 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!

  Deep Learning, Free ebook, Neural Networks, PyTorch

• 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.

  Deep Learning, DeepMind, Learning, Neural Networks

• 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.

  Deep Learning, Neural Networks, Python, PyTorch, Tensor

• Introduction to Convolutional Neural Networks - Jun 3, 2020.
  
  The article focuses on explaining key components in CNN and its implementation using Keras python library.

  Convolutional Neural Networks, Keras, Neural Networks, Python

• 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.

  Face Recognition, Image Recognition, Machine Learning, Neural Networks

• 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.

  AI, Deep Learning, Hype, Neural Networks

• 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.

  AI, Courses, Deep Learning, DeepMind, Neural Networks, Reinforcement Learning

• 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.

  Aaron Courville, Book, Deep Learning, Free ebook, Ian Goodfellow, Neural Networks, Yoshua Bengio

• 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.

  AGI, AI, Brain, Neural Networks

• 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.

  Deep Learning, Forecasting, LSTM, Neural Networks, Recurrent Neural Networks, Time Series

• 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.

  Neural Networks, Open Source, OpenAI, Visualization

• 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.

  API, Keras, Neural Networks, Python, PyTorch

• 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.

  AI, API, Cloud, Data Analytics, Datasets, fast.ai, Machine Learning, Neural Networks, Social Media

• 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?

  Machine Learning, Neural Networks, random forests algorithm

• 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.

  Neural Networks, numpy, Python

• 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.

  GANs, Generative Adversarial Network, Neural Networks, Python

• 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.

  Neural Networks, Optimization, Python, TensorFlow, Training

• Recreating Fingerprints using Convolutional Autoencoders - Mar 4, 2020.
  
  The article gets you started working with fingerprints using Deep Learning.

  Autoencoder, Convolutional Neural Networks, Neural Networks, Python

• Deep Neural Networks - Feb 14, 2020.
  
  We examine the features and applications of a deep neural network.

  Applications, Deep Learning, Neural Networks, Robots

• 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.

  Algorithms, Microsoft, Neural Networks

• 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.

  Generative Adversarial Network, Neural Networks, Training, Uber

• 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

• 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.

  Churn, Customer Analytics, Neural Networks, random forests algorithm

• 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.

  Neural Networks, Overfitting

• 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

• 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.

  Attention, Google, 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.

  Autoencoder, Machine Learning, Missing Values, Neural Networks

• 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

• 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

• 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.

  Beginners, Classification, Dropout, Gradient Descent, Neural Networks, Regression

• 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.

  Neural Networks, numpy, Python

• 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).

  Image Recognition, Microsoft, Neural Networks, Question answering, Training

• 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.

  Keras, Neural Networks, 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

• 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.

  Architecture, Automated Machine Learning, Neural Networks

• 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

• Introduction to Artificial Neural Networks - Oct 8, 2019.
  
  In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.

  Beginners, Gradient Descent, Neural Networks

• 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.

  DeepMind, Neural Networks

• 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.

  Neural Networks, Python, PyTorch

• 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.

  Backpropagation, Neural Networks, numpy, Python

• 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.

  fast.ai, Neural Networks, Python, PyTorch, PyTorch Lightning

• 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.

  Explained, Keras, Neural Networks, Python

• 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.

  Neural Networks, Performance, PyTorch, PyTorch Lightning, Tips

• 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

• 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.

  Convolutional Neural Networks, Keras, Neural Networks, Python, TensorFlow

• 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

• 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.

  Facebook, Information Retrieval, Natural Language Processing, Neural Networks, NLP, Programming

• 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

• 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.

  Keras, LSTM, Natural Language Generation, Neural Networks, Python, TensorFlow

• 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.

  Architecture, Automated Machine Learning, Evolutionary Algorithm, Neural Networks

• 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

• 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?

  Decision Trees, Neural Networks, random forests algorithm

• 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.

  Backpropagation, LSTM, Neural Networks, Recurrent Neural Networks

• 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

• 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

• 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

• 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

• 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.

  Neural Networks, NLP, PyTorch, Recurrent Neural Networks

• 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

• 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

• Checklist for Debugging Neural Networks - Mar 22, 2019.
  
  Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.

  Checklist, Modeling, Neural Networks, Optimization, Tips, Training

• 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

• 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

• 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

• Neural Networks with Numpy for Absolute Beginners: Introduction - Mar 5, 2019.
  
  In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.

  Beginners, Neural Networks, numpy, Python

• Comparing MobileNet Models in TensorFlow - Mar 1, 2019.
  
  MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

  Computer Vision, Mobile, Neural Networks, TensorFlow

• TensorFlow.js: Machine learning for the web and beyond - Feb 28, 2019.
  
  TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. Read a summary of the paper which describes the design, API, and implementation of TensorFlow.js.

  Javascript, Keras, Neural Networks, TensorFlow

• How to do Everything in Computer Vision - Feb 27, 2019.
  
  The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!

  Computer Vision, Convolutional Neural Networks, Image Classification, Image Recognition, Neural Networks, Object Detection

• 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

• A comprehensive survey on graph neural networks - Feb 15, 2019.
  
  This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.

  Graphs, Neural Networks

• Neural Networks – an Intuition - Feb 7, 2019.
  
  Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.

  Explained, History, Machine Learning, Neural Networks, Perceptron

• 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

• The Backpropagation Algorithm Demystified - Jan 2, 2019.
  
  A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.

  Backpropagation, Explained, Neural Networks

• 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

• BERT: State of the Art NLP Model, Explained - Dec 26, 2018.
  
  BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.

  Explained, Modeling, Neural Networks, NLP, Transformer

• The brain as a neural network: this is why we can’t get along - Dec 19, 2018.
  
  This article sets out to answer the question: what insights can we gain about ourselves by thinking of the brain as a machine learning model?

  Brain, Confirmation Bias, Neural Networks, Overfitting, Politics

• 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

• Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices - Nov 21, 2018.
  
  LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.

  Finance, Keras, LSTM, Neural Networks, Stocks

• 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

• Mastering the Learning Rate to Speed Up Deep Learning - Nov 6, 2018.
  
  Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.

  Hyperparameter, Neural Networks

• 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

• Generative Adversarial Networks – Paper Reading Road Map - Oct 24, 2018.
  
  To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.

  GANs, Generative Adversarial Network, Neural Networks

• The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
  
  Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.

  Machine Learning, Neural Networks, NLP, Text Classification

• Sequence Modeling with Neural Networks – Part I - Oct 3, 2018.
  
  In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.

  Neural Networks, NLP, Recurrent Neural Networks, Sequences

• How to Create a Simple Neural Network in Python - Oct 2, 2018.
  
  The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.

  Machine Learning, Neural Networks, Python