- More Effective Transfer Learning for NLP - Oct 1, 2018.
Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.
Neural Networks, NLP, Transfer Learning, Word Embeddings
- Introduction to Deep Learning - Sep 28, 2018.
I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.
Beginners, Deep Learning, Neural Networks
- Power Laws in Deep Learning 2: Universality - Sep 26, 2018.
It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.
Deep Learning, Explained, Neural Networks
6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.
Data Science, Machine Learning, Neural Networks
- Power Laws in Deep Learning - Sep 20, 2018.
In pretrained, production quality DNNs, the weight matrices for the Fully Connected (FC ) layers display Fat Tailed Power Law behavior.
Deep Learning, Explained, Neural Networks
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection - Sep 19, 2018.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
Pages: 1 2
Deep Learning, Image Recognition, Neural Networks, Object Detection, Python
Machine Learning Cheat Sheets - Sep 11, 2018.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Cheat Sheet, Deep Learning, Machine Learning, Mathematics, Neural Networks, Probability, Statistics, Supervised Learning, Tips, Unsupervised Learning
Neural Networks and Deep Learning: A Textbook - Sep 7, 2018.
This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.
Book, Charu Aggarwal, Deep Learning, Neural Networks
AI Knowledge Map: How To Classify AI Technologies - Aug 31, 2018.
What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.
AI, Classification, Deep Learning, Machine Intelligence, Machine Learning, Neural Networks
- 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.
Automated Machine Learning, Keras, Neural Networks, Python
- Only Numpy: Implementing GANs and Adam Optimizer using Numpy - Aug 6, 2018.
This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.
GANs, Generative Adversarial Network, Neural Networks, numpy, Optimization, Python
Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?” - Jul 16, 2018.
By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.
Architecture, Deep Learning, Hyperparameter, Neural Networks
- Deep Quantile Regression - Jul 3, 2018.
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.
Deep Learning, Hyperparameter, Keras, Neural Networks, Python, Regression
- Inside the Mind of a Neural Network with Interactive Code in Tensorflow - Jun 29, 2018.
Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
Pages: 1 2
Convolutional Neural Networks, Image Recognition, Neural Networks, Python, TensorFlow
- 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.
Keras, MNIST, Neural Networks, Python
- Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks - Jun 28, 2018.
Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.
Ayasdi, Convolutional Neural Networks, MNIST, Neural Networks, Topological Data Analysis
- Batch Normalization in Neural Networks - Jun 26, 2018.
This article explains batch normalization in a simple way. I wrote this article after what I learned from Fast.ai and deeplearning.ai.
Hyperparameter, Neural Networks
- Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health - Jun 14, 2018.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.
LSTM, Neural Networks, PyTorch, Recurrent Neural Networks
- How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning - Jun 13, 2018.
An end-to-end example of how to build a system that can search objects semantically.
Pages: 1 2
Deep Learning, GitHub, Neural Networks, NLP, Semantic Analysis
DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
Computer Vision, Data Science, Deep Learning, LinkedIn, Neural Networks, OpenCV, Python
- 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.
Francois Chollet, Keras, Neural Networks, Python, Workflow
- On the contribution of neural networks and word embeddings in Natural Language Processing - May 31, 2018.
In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
Neural Networks, NLP, Word Embeddings, word2vec
- Improving the Performance of a Neural Network - May 30, 2018.
There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.
Ensemble Methods, Hyperparameter, Neural Networks, Overfitting, Tips
- An Introduction to Deep Learning for Tabular Data - May 17, 2018.
This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
Deep Learning, fast.ai, Kaggle, Neural Networks, Rachel Thomas, word2vec
How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 - May 17, 2018.
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
Computer Vision, Image Recognition, Neural Networks, Object Detection, Python, PyTorch, YOLO
- GANs in TensorFlow from the Command Line: Creating Your First GitHub Project - May 16, 2018.
In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.
GANs, Generative Adversarial Network, GitHub, Neural Networks, Python, Rubens Zimbres, TensorFlow
Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API - May 15, 2018.
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
Pages: 1 2
API, Convolutional Neural Networks, Dropout, Flask, Neural Networks, Python, RESTful API, TensorFlow
- Detecting Breast Cancer with Deep Learning - May 9, 2018.
Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.
Cancer Detection, Deep Learning, Healthcare, Neural Networks
Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
Convolutional Neural Networks, Image Recognition, Neural Networks, numpy, Python
- Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
Deep Learning, Neural Networks, NLP, Packt Publishing, word2vec
- Neural Network based Startup Name Generator - Apr 20, 2018.
How to build a recurrent neural network to generate suggestions for your new company’s name.
Neural Networks, Python, Startups
- Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
Convolutional Neural Networks, Deep Learning, Neural Networks
- 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?
API, Deep Learning, Francois Chollet, Keras, Machine Learning, Neural Networks, TensorFlow
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
Pages: 1 2
Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM
- Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
Pages: 1 2
Deep Learning, Neural Networks, Python, PyTorch
Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
Deep Learning, Deep Neural Network, Free ebook, Machine Learning, Neural Networks
- Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
Algorithms, Deep Learning, Machine Learning, Neural Networks, TensorFlow, Text Analytics, Trends
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
Deep Learning, Neural Networks, NLP, word2vec
A “Weird” Introduction to Deep Learning - Mar 30, 2018.
There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.
Pages: 1 2
Deep Learning, Dropout, Neural Networks, Representation, Tensor, TensorFlow
- Is ReLU After Sigmoid Bad? - Mar 23, 2018.
Recently [we] were analyzing how different activation functions interact among themselves, and we found that using relu after sigmoid in the last two layers worsens the performance of the model.
Architecture, Neural Networks
- 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, Keras, LSTM, Neural Networks, NLP, NLTK, Python
- Resurgence of AI During 1983-2010 - Feb 16, 2018.
We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.
AI, Big Data, History, Machine Learning, Neural Networks, Reinforcement Learning, Trends
Neural network AI is simple. So… Stop pretending you are a genius - Feb 15, 2018.
This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time.
AI, Deep Learning, Neural Networks
- The Birth of AI and The First AI Hype Cycle - Feb 13, 2018.
A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.
AI, Alan Turing, Herbert A. Simon, History, Hype, Marvin Minsky, Neural Networks
A Simple Starter Guide to Build a Neural Network - Feb 5, 2018.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
Machine Learning, Neural Networks, Python, PyTorch
- Understanding Learning Rates and How It Improves Performance in Deep Learning - Feb 1, 2018.
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
Deep Learning, Hyperparameter, Neural Networks
The 8 Neural Network Architectures Machine Learning Researchers Need to Learn - Jan 31, 2018.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
Pages: 1 2
Architecture, Deep Learning, Machine Learning, Neural Networks
- My Journey into Deep Learning - Jan 30, 2018.
In this post I’ll share how I’ve been studying Deep Learning and using it to solve data science problems. It’s an informal post but with interesting content (I hope).
Deep Learning, MOOC, Neural Networks
- 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).
Automated Machine Learning, Genetic Algorithm, Keras, Neural Networks, Python, Recurrent Neural Networks
- Is Learning Rate Useful in Artificial Neural Networks? - Jan 15, 2018.
This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.
Hyperparameter, Neural Networks, Python
- 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.
Pages: 1 2
Cloud, Deep Learning, Keras, Neural Networks, TensorFlow
- The 10 Deep Learning Methods AI Practitioners Need to Apply - Dec 13, 2017.
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
Pages: 1 2
Backpropagation, Convolutional Neural Networks, Deep Learning, Dropout, Gradient Descent, LSTM, Neural Networks, Transfer Learning
- 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.
Keras, Neural Networks, Python
What is a Bayesian Neural Network? - Dec 5, 2017.
BNNs are important in specific settings, especially when we care about uncertainty very much.
Bayesian, Bayesian Networks, Neural Networks
Using Deep Learning to Solve Real World Problems - Dec 4, 2017.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
Advice, Career, Deep Learning, Neural Networks
- Exploring Recurrent Neural Networks - Dec 1, 2017.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
Neural Networks, Packt Publishing, Python, Recurrent Neural Networks, TensorFlow
- How To Unit Test Machine Learning Code - Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
Machine Learning, Neural Networks, Python, Software Engineering, TensorFlow
- Understanding Objective Functions in Neural Networks - Nov 23, 2017.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
Cost Function, Deep Learning, Gradient Descent, Neural Networks, Optimization
- Estimating an Optimal Learning Rate For a Deep Neural Network - Nov 21, 2017.
This post describes a simple and powerful way to find a reasonable learning rate for your neural network.
Deep Learning, Hyperparameter, Neural Networks
- Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
A great introductory and high-level summary of Generative Adversarial Networks.
Deep Learning, GANs, Generative Adversarial Network, Neural Networks
- TensorFlow: What Parameters to Optimize? - Nov 9, 2017.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
Neural Networks, Optimization, Python, TensorFlow
Want to know how Deep Learning works? Here’s a quick guide for everyone - Nov 3, 2017.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
Deep Learning, Neural Networks
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.
7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks
- Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning - Oct 28, 2017.
This is a short post for beginners learning neural networks, covering several essential neural networks concepts.
Beginners, Deep Learning, Neural Networks
- Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation - Oct 25, 2017.
In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?
Backpropagation, Explained, Gradient Descent, Neural Networks
- TensorFlow: Building Feed-Forward Neural Networks Step-by-Step - Oct 23, 2017.
This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.
Pages: 1 2 3
Deep Learning, Neural Networks, TensorFlow
- 5 Free Resources for Furthering Your Understanding of Deep Learning - Oct 20, 2017.
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
Andrew Ng, Deep Learning, Neural Networks, NIPS, Summer School
- 7 Types of Artificial Neural Networks for Natural Language Processing - Oct 19, 2017.
What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.
Pages: 1 2
AI, Deep Learning, Natural Language Processing, Neural Networks
- An Overview of 3 Popular Courses on Deep Learning - Oct 13, 2017.
After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.
Andrew Ng, Coursera, Deep Learning, deeplearning.ai, fast.ai, Jeremy Howard, Neural Networks
- How I started with learning AI in the last 2 months - Oct 9, 2017.
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.
AI, Chatbot, Gradient Descent, Neural Networks, Python
- A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs) - Oct 5, 2017.
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
Pages: 1 2
Finance, LSTM, Neural Networks, Recurrent Neural Networks, Statsbot
Understanding Machine Learning Algorithms - Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
Algorithms, Ensemble Methods, Gradient Boosting, Machine Learning, Neural Networks, Predictive Analytics, random forests algorithm, SVM
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.
Pages: 1 2 3
Cheat Sheet, Data Science, Deep Learning, Machine Learning, Neural Networks, Probability, Python, R, SQL, Statistics
- 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
Deep Learning, Machine Learning, Neural Networks, Reinforcement Learning
- 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.
Games, Keras, Neural Networks, Python
- Neural Network Foundations, Explained: Activation Function - Sep 13, 2017.
This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.
Explained, Neural Networks
- Detecting Facial Features Using Deep Learning - Sep 4, 2017.
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
Convolutional Neural Networks, Deep Learning, Image Recognition, Neural Networks
- PyTorch or TensorFlow? - Aug 29, 2017.
PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.
Deep Learning, Neural Networks, PyTorch, TensorFlow
- 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.
Pages: 1 2
Deep Learning, Keras, Neural Networks
37 Reasons why your Neural Network is not working - Aug 22, 2017.
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.
Pages: 1 2
Data Engineering, Data Preparation, Gradient Descent, Neural Networks
- Deep Learning and Neural Networks Primer: Basic Concepts for Beginners - Aug 18, 2017.
This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
Deep Learning, Neural Networks
- 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!
Pages: 1 2
Deep Learning, Image Recognition, Keras, Neural Networks
- How Convolutional Neural Networks Accomplish Image Recognition? - Aug 9, 2017.
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
Clarifai, Convolutional Neural Networks, IBM Watson, Image Recognition, Neural Networks
- Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings - Aug 9, 2017.
This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.
Brain, Convolutional Neural Networks, Deep Learning, Neural Networks, SVDS
- Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
Deep Learning, Machine Learning, Model Performance, Modeling, Neural Networks
- Visualizing Convolutional Neural Networks with Open-source Picasso - Aug 1, 2017.
Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?
Convolutional Neural Networks, Neural Networks, Open Source, Visualization
Introduction to Neural Networks, Advantages and Applications - Jul 25, 2017.
Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.
Pages: 1 2
Applications, Beginners, Brain, Neural Networks
- Summary of Unintuitive Properties of Neural Networks - Jul 24, 2017.
Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.
AI, Deep Learning, Hugo Larochelle, Neural Networks
- 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing - Jul 19, 2017.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
Deep Learning, Natural Language Processing, Neural Networks, NLP
- The Strange Loop in Deep Learning - Jul 11, 2017.
This ‘strange loop’ is in fact is the fundamental reason for what Yann LeCun describes as “the coolest idea in machine learning in the last twenty years.”
Deep Learning, Neural Networks, Yann LeCun
Applying Deep Learning to Real-world Problems - Jun 30, 2017.
In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
Balancing Classes, Deep Learning, Neural Networks, Training, Unbalanced
- 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.
Deep Learning, Keras, Neural Networks, R
Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
Deep Learning, Machine Learning, Neural Networks
- Deep Learning Papers Reading Roadmap - Jun 13, 2017.
The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.
Deep Learning, Neural Networks
- Why Does Deep Learning Not Have a Local Minimum? - Jun 2, 2017.
"As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"
Deep Learning, Neural Networks
- The Two Phases of Gradient Descent in Deep Learning - May 12, 2017.
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!
Deep Learning, ICLR, Neural Networks
- Top 10 Recent AI videos on YouTube - May 10, 2017.
Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.
AI, Google, Machine Learning, MIT, Neural Networks, NVIDIA, Robots, Youtube
Using Deep Learning To Extract Knowledge From Job Descriptions - May 9, 2017.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
Convolutional Neural Networks, Deep Learning, Natural Language Processing, Neural Networks, NLP, Text Mining
- Building, Training, and Improving on Existing Recurrent Neural Networks - May 8, 2017.
In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.
Deep Learning, Neural Networks, Recurrent Neural Networks, SVDS
Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Machine Learning, Neural Networks, Robots, Video Games, Yaser Abu-Mostafa, Youtube
- Deep Learning – Past, Present, and Future - May 2, 2017.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
Pages: 1 2
Andrew Ng, Big Data, Deep Learning, Geoff Hinton, Google, GPU, History, Neural Networks, NVIDIA
- One Deep Learning Virtual Machine to Rule Them All - Apr 28, 2017.
The frontend code of programming languages only needs to parse and translate source code to an intermediate representation (IR). Deep Learning frameworks will eventually need their own “IR.”
Deep Learning, Neural Networks
- How to Build a Recurrent Neural Network in TensorFlow - Apr 26, 2017.
This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.
Deep Learning, Neural Networks, Recurrent Neural Networks, TensorFlow
Awesome Deep Learning: Most Cited Deep Learning Papers - Apr 21, 2017.
This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.
Deep Learning, Neural Networks, Research
5 Machine Learning Projects You Can No Longer Overlook, April - Apr 13, 2017.
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. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.
Data Exploration, Deep Learning, Java, Machine Learning, Neural Networks, Overlook, Python, Scala, scikit-learn, Topic Modeling
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.
Deep Learning, Keras, Neural Networks, Python, TensorFlow, Theano, Torch
The Anatomy of Deep Learning Frameworks - Feb 24, 2017.
This post sketches out some common principles which would help you better understand deep learning frameworks, and provides a guide on how to implement your own deep learning framework as well.
Deep Learning, Neural Networks
- Deep Learning Research Review: Natural Language Processing - Jan 31, 2017.
This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.
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Deep Learning, Natural Language Processing, Neural Networks, NLP
Deep Learning Can be Applied to Natural Language Processing - Jan 16, 2017.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.
Deep Learning, Natural Language Processing, Neural Networks, NLP
- Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.
Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS
5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn
- ResNets, HighwayNets, and DenseNets, Oh My! - Dec 19, 2016.
This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.
Convolutional Neural Networks, Deep Learning, Neural Networks
- Artificial Neural Networks (ANN) Introduction, Part 2 - Dec 9, 2016.
Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.
Algobeans, Deep Learning, Neural Networks
- Artificial Neural Networks (ANN) Introduction, Part 1 - Dec 8, 2016.
This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
Algobeans, Image Recognition, MNIST, Neural Networks
The hard thing about deep learning - Dec 1, 2016.
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.
CA, Deep Learning, Neural Networks, NP-hard, Optimization, San Jose, Strata
- Deep Learning Reading Group: Skip-Thought Vectors - Nov 17, 2016.
Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.
Deep Learning, Lab41, Natural Language Processing, Neural Networks, word2vec
- An Intuitive Explanation of Convolutional Neural Networks - Nov 11, 2016.
This article provides a easy to understand introduction to what convolutional neural networks are and how they work.
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Convolutional Neural Networks, Deep Learning, Explanation, Machine Learning, Neural Networks
- A Quick Introduction to Neural Networks - Nov 9, 2016.
This article provides a beginner level introduction to multilayer perceptron and backpropagation.
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Backpropagation, Deep Learning, Machine Learning, Neural Networks
- Deep Learning cleans podcast episodes from ‘ahem’ sounds - Nov 8, 2016.
“3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.
Convolutional Neural Networks, Deep Learning, Deep Neural Network, Neural Networks, Podcast, Speech
- Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera - Nov 4, 2016.
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
DeepMind, Geoff Hinton, Image Recognition, Machine Learning, Neural Networks, Reddit, Self-Driving Car
A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18! - Oct 20, 2016.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
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Beginners, Machine Learning, Neural Networks, Python, scikit-learn
- Artificial Intelligence, Deep Learning, and Neural Networks, Explained - Oct 14, 2016.
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.
AI, Artificial Intelligence, Deep Learning, Explained, Neural Networks
- Deep Learning Reading Group: SqueezeNet - Sep 29, 2016.
This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
Compression, Deep Learning, Lab41, Machine Learning, Neural Networks
- Deep Learning Reading Group: Deep Residual Learning for Image Recognition - Sep 22, 2016.
Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.
Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks
9 Key Deep Learning Papers, Explained - Sep 20, 2016.
If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.
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Academics, Deep Learning, Explained, Neural Networks
- Urban Sound Classification with Neural Networks in Tensorflow - Sep 12, 2016.
This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.
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Deep Learning, Feature Extraction, Machine Learning, Neural Networks, TensorFlow
- Deep Learning Reading Group: Deep Networks with Stochastic Depth - Sep 8, 2016.
An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.
Academics, Deep Learning, Lab41, Neural Networks
- A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2 - Sep 8, 2016.
This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.
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Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
- KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning - Sep 7, 2016.
Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?
Balancing Classes, Convolutional Neural Networks, Data Scientist, Deep Learning, Neural Networks
- A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1 - Sep 6, 2016.
Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.
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Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
