- Tokenization and Text Data Preparation with TensorFlow & Keras - Mar 6, 2020.
This article will look at tokenizing and further preparing text data for feeding into a neural network using TensorFlow and Keras preprocessing tools.
- Hands on Hyperparameter Tuning with Keras Tuner - Feb 28, 2020.
Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.
- Francois Chollet on the Future of Keras and Reinforce Conference - Feb 25, 2020.
Ahead of Reinforce Conference in Budapest, we asked Francois Chollet, the creator of Keras, about Keras future, proposed developments, PyTorch, energy efficiency, and more. Listen to him in person in Budapest, April 6-7, and use code KDNuggets to save 15% on conference tickets.
- Intent Recognition with BERT using Keras and TensorFlow 2 - Feb 10, 2020.
TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!
- Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
- PyTorch in 2019 and where in Europe you can learn about PyTorch in 2020 - Dec 4, 2019.
The Reinforce AI Conference is coming to Budapest again. Join us Apr 6-7 for the conference days, and optionally Apr 8 for workshops. Stefan Otte returns as a speaker, while Francois Chollet joins this time as well.
- Automated Machine Learning Project Implementation Complexities - Nov 22, 2019.
To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.
- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
- KDnuggets™ News 19: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
- 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.
- A Single Function to Streamline Image Classification with Keras - Sep 23, 2019.
We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model.
- 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.
- KDnuggets™ News 19:n31, Aug 21: Become a Marketable Data Scientist; Data Science Command Line Basics; Chatbots with Keras - Aug 21, 2019.
This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; and much more!
- 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?
- 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.
- 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.
- 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.
- 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.
- Build your own AutoML computer vision pipeline, July 16 webinar - Jul 2, 2019.
This webinar will present a step-by-step use case so you can build your own AutoML computer vision pipelines, and will go through the essentials for research, deployment and training using Keras, PyTorch and 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.
- Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
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- 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?
- 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
- 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.
- Top KDnuggets tweets, Mar 20-26: 10 More Free Must-Read Books for Machine Learning and Data Science - Mar 27, 2019.
Also - 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition; 10 Free Must-See Courses for Machine Learning and Data Science; How to Train a Keras Model 20x Faster with a TPU for Free.
- 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.
- KDnuggets™ News 19:n11, Mar 20: Another 10 Free Must-Read Books for Data Science; 19 Inspiring Women in AI, Big Data, Machine Learning - Mar 20, 2019.
Also: Who is a typical Data Scientist in 2019?; The Pareto Principle for Data Scientists; My favorite mind-blowing Machine Learning/AI breakthroughs; Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision; Advanced Keras - Accurately Resuming a Training Process
- 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.
- Advanced Keras — Accurately Resuming a Training Process - Mar 14, 2019.
This article on practical advanced Keras use covers handling nontrivial cases where custom callbacks are used.
- TensorFlow.js: Machine learning for the web and beyond - Feb 28, 2019.
- Automated Machine Learning in Python - Jan 18, 2019.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top KDnuggets tweets, Jan 02-08: 10 Free Must-Read Books for Machine Learning and Data Science - Jan 9, 2019.
Also: Papers with Code: A Fantastic GitHub Resource; Most Recommended #DataScience and #MachineLearning Books by Top MS programs;10 More Free Must-Read Books for ML and DS
- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
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- Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
- 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.
- KDnuggets™ News 18:n45, Nov 28: Your Favorite Python IDE/editor? Intro to Data Science for Managers - Nov 28, 2018.
Also: 6 Goals Every Wannabe Data Scientist Should Make for 2019; Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices.
- 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.
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
- 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.
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- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Deep Learning Framework Power Scores 2018 - Sep 24, 2018.
Who’s on top in usage, interest, and popularity?
- Training with Keras-MXNet on Amazon SageMaker - Sep 10, 2018.
In this post, you will learn how to train Keras-MXNet jobs on Amazon SageMaker. I’ll show you how to build custom Docker containers for CPU and GPU training, configure multi-GPU training, pass parameters to a Keras script, and save the trained models in Keras and MXNet formats.
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- Don’t Use Dropout in Convolutional Networks - Sep 5, 2018.
If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.
- KDnuggets™ News 18:n32, Aug 29: Data Visualization Cheat Sheet; Auto-Keras, or Deep Learning Model in 4 Lines of Code - Aug 29, 2018.
KDnuggets News took a break last week, so this issue is doubly full of Data Science goodness. Check Data Visualization Cheat Sheet, see how to do Deep Learning in just 4 lines, learn about Emotion and Sentiment analysis, check 16 other interesting tutorials, and may be you know someone who can fill any of 16 jobs below.
- 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.
- Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code - Aug 17, 2018.
Auto-Keras is an open source software library for automated machine learning. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.
- AutoKeras: The Killer of Google’s AutoML - Aug 15, 2018.
Auto-Keras is an open source "competitor" to Google’s AutoML, a new cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in Neural Architecture Search (NAS).
- GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows - Aug 8, 2018.
This post includes a wide spectrum of data science projects, all of which are open source and are present on GitHub repositories.
- Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors - Jul 5, 2018.
In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.
- 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.
- Building a Basic Keras Neural Network Sequential Model - Jun 29, 2018.
The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.
- Top 20 Python Libraries for Data Science in 2018 - Jun 27, 2018.
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.
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- 5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
- KDnuggets™ News 18:n22, Jun 6: 10 More Free Must-Read Books for Machine Learning and Data Science; Beginner Guide to Data Science Pipeline - Jun 6, 2018.
Summer. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and learn! Here is a great selection to get started.
- The Keras 4 Step Workflow - Jun 4, 2018.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.
- Deep Learning With Apache Spark: Part 2 - May 23, 2018.
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
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- Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis - May 22, 2018.
Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.
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- Top 16 Open Source Deep Learning Libraries and Platforms - Apr 24, 2018.
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
- Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Comparing Deep Learning Frameworks: A Rosetta Stone Approach - Mar 26, 2018.
A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.
- 5 Fantastic Practical Natural Language Processing Resources - Feb 22, 2018.
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
- Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 20, 2018.
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
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- 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- Using Genetic Algorithm for Optimizing Recurrent Neural Networks - Jan 22, 2018.
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
- Deep Learning Made Easy with Deep Cognition - Dec 21, 2017.
So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.
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- KDnuggets™ News 17:n47, Dec 13: Top Data Science, Machine Learning Methods in 2017; Main Data Science Developments in 2017, Key Trends; Lunch Break with Keras - Dec 13, 2017.
Also: Managing Machine Learning Workflows with Scikit-learn Pipelines; Best Masters in Data Science and Analytics - Europe Edition; Another Day in the Life of a Data Scientist; TensorFlow for Short-Term Stocks; Creating Simple Data Visualizations as an Act of Kindness
- Robust Algorithms for Machine Learning - Dec 11, 2017.
This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.
- Today I Built a Neural Network During My Lunch Break with Keras - Dec 8, 2017.
So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.
- Top KDnuggets tweets, Nov 29 – Dec 5: Teaching the Data Science Process - Dec 6, 2017.
Also An Introduction to Key Data Science Concepts; Using Deep Learning To Extract Knowledge From Job Descriptions; A General Approach to Preprocessing Text Data; keras-text - A Text Classification Library in #Keras.
- Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras - Nov 29, 2017.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
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- Implementing Enterprise AI course using TensorFlow and Keras - Nov 27, 2017.
The course is for developers and architects who want to transition their career to Enterprise AI, but also has strategic (non-coding) version. The course starts in Jan 2018 and will take 3 months for the content and up to 3 months for the team project.
- Top 10 Videos on Deep Learning in Python - Nov 17, 2017.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
- KDnuggets™ News 17:n42, Nov 1: 7 Steps to Mastering Deep Learning with Keras; 6 Books Every Data Scientist Should Keep Nearby - Nov 1, 2017.
7 Steps to Mastering Deep Learning with Keras; 6 Books Every Data Scientist Should Keep Nearby; Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning; XGBoost: A Concise Technical Overview; AlphaGo Zero: The Most Significant Research Advance in AI
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
- Ranking Popular Deep Learning Libraries for Data Science - Oct 23, 2017.
We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
- Top KDnuggets tweets, Oct 11-17: A Beginners Guide to #DeepLearning - Oct 18, 2017.
Also Collecting #DataScience Cheat Sheets; Luminoth: Open source toolkit for #ComputerVision.
- KDnuggets™ News 17:n38, Oct 4: What Blockchains Mean to Big Data; Keras Deep Learning Cheat Sheet; Machine Learning in Finance - Oct 4, 2017.
Also: XGBoost, a Top Machine Learning Method on Kaggle, Explained; How to win Kaggle competition based on NLP task, if you are not an NLP expert; Fundamental Breakthrough in 2 Decade Old Algorithm Redefines Big Data Benchmarks
- Keras Cheat Sheet: Deep Learning in Python - Sep 27, 2017.
Keras is a Python deep learning library for Theano and TensorFlow. The package is easy to use and powerful, as it provides users with a high-level neural networks API to develop and evaluate deep learning models.
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- KDnuggets™ News 17:n37, Sep 27: Essential Data Science & Machine Learning Cheat Sheets; 5 Machine Learning Projects to Check Out Now! - Sep 27, 2017.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; 5 Machine Learning Projects You Can No Longer Overlook - Episode VI; Putting Machine Learning in Production; 5 Ways to Get Started with Reinforcement Learning; Ensemble Learning to Improve Machine Learning Results
- The Search for the Fastest Keras Deep Learning Backend - Sep 26, 2017.
This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.
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- Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks - Sep 18, 2017.
In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.
- Top KDnuggets tweets, Sep 06-12: Visualizing Cross-validation Code; Intro to #Blockchain and #BigData - Sep 13, 2017.
Also: WTF #Python - A collection of interesting and tricky Python examples; Thoughts after taking @AndrewYNg #Deeplearning #ai course; Another #Keras Tutorial For #NeuralNetwork Beginners.
- ACM Data Science Camp 2017, Oct 14, Silicon Valley - Sep 6, 2017.
Data Science Camp is SF Bay ACM annual event combining sessions, keynote, and optional tutorial - an excellent opportunity to learn and connect with others, at very low cost.
- An Intuitive Guide to Deep Network Architectures - Aug 28, 2017.
How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.
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- First Steps of Learning Deep Learning: Image Classification in Keras - Aug 16, 2017.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
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- Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!? - Jul 3, 2017.
NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE
- Deep Learning with R + Keras - Jun 27, 2017.
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
- The world’s first protein database for Machine Learning and AI - Jun 22, 2017.
dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp
- Top KDnuggets tweets, Jun 14-20: 5 EBooks to Read Before Getting into A Data Science or Big Data Career - Jun 21, 2017.
Also 10 Free Must-Read Books for #MachineLearning and #DataScience; #Keras implementation of a simple Neural Net module for relational reasoning; Applying #deeplearning to real-world problems
- AI for fintech course – Early discounts and limited places - Jun 20, 2017.
This new course with limited places will focus on AI design (product, development and Data) for the fintech industry and will be taught online by Ajit Jaokar and Jakob Aungiers.
- Getting Started with Deep Learning - Mar 24, 2017.
This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.
- An Overview of Python Deep Learning Frameworks - Feb 27, 2017.
Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.
- Top KDnuggets tweets, Aug 03-09: Understanding the Bias-Variance Tradeoff: An Overview - Aug 10, 2016.
Understanding the Bias-Variance Tradeoff: An Overview; Cartoon: Facebook #DataScience experiments and Cats; Bayesian #Machine Learning, Explained; Deep Reinforcement Learning for Keras.
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.