Machine Learning 5 Genetic Algorithm Applications Using PyGAD This tutorial introduces PyGAD, an open-source Python library for implementing the genetic algorithm and training machine learning algorithms. PyGAD supports 19 parameters for customizing the genetic algorithm for various applications.
Tutorial A Guide to Paperspace's Gradient Community Notebooks Gradient Community Notebooks allow users to create, run, and share Jupyter notebooks on free GPUs. In this post Gradient Community Notebooks will be introduced and the steps to get started
Neural Network Neural Architecture Search Part 3: Controllers and Accuracy Predictors In the first part of this series we saw an overview of neural architecture search, including a state of the art review of the literature. In Part 2 we then
Neural Network Neural Architecture Search Part 2: Search Space, Architecture Design and One-Shot Training In the first part of the series, we took a look at all the different angles the problem of neural architecture is being approached from. With the foundation covered, we'll
Applications Using PoseNet To Create a Snapchat-Like Eye Filter for Android PoseNet is a computer vision deep learning model used for estimating a person's physical position. Based on MobileNet, it can be deployed to mobile devices and takes even less time
Data Science Implementing The Levenshtein Distance for Word Autocompletion and Autocorrection The Levenshtein distance is a text similarity measure that compares two words and returns a numeric value representing the distance between them. The distance reflects the total number of single-character
Series: GauGAN Understanding GauGAN Part 4: Debugging Training & Deciding If GauGAN Is Right For You In this post we cover how to tackle common training issues that may arise with GauGAN. We conclude with advice on whether GauGAN will fit your business needs or not.
Series: GauGAN Understanding GauGAN Part 3: Model Evaluation Techniques Hi folks, welcome to the third part of the GauGAN series. In the last two parts we've covered the components and losses of GauGAN, and how to set up custom
Series: GauGAN Understanding GauGAN Part 2: Training on Custom Datasets In Part One, we covered the basic components of GauGAN as well as the loss functions it makes use of. In this part, we'll cover the training details and see
Deep Learning Deploying Deep Learning Models Part 2: Hosting on Paperspace Gradient is a Paperspace product that simplifies developing, training, and deploying deep learning models. The platform provides infrastructure automation and a software development kit for machine learning developers. For creating
Generative Models Implementing CycleGAN for Age Conversion If you've been on any sort of social media this year, you've probably seen people uploading a recent picture of themselves right next to another picture of what they'll look
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 4: Applying to Any Number of Neurons In this tutorial we extend our implementation of gradient descent to work with a single hidden layer with any number of neurons. Part 4 is divided into two sections. In
Tutorial Neural Machine Translation with TensorFlow If  you are a fan of Google translate or some other translation service, do  you ever wonder how these programs are able to make spot-on translations from one language to
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 3: Adding a Hidden Layer Hello again in the series of tutorials for implementing a generic gradient descent (GD) algorithm in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Tutorial Unpaired Image to Image Translation with CycleGAN Yann LeCun, director of AI research at Facebook and Professor at NYU described Generative Adversarial Networks, GANs as the most interesting idea in Machine Learning in the last 10 years.
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 2: Extending for Any Number of Inputs Hello again in the series of tutorials for implementing a generic gradient descent (GD) algorithm in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Tutorial TensorFlow 2.0 in Action TensorFlow is one of the most popular frameworks used for deep learning projects and is approaching a major new release- Â TensorFlow 2.0. Luckily, we don't have to wait for
Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 1: The Forward and Backward Pass Through a series of tutorials, the gradient descent (GD) algorithm will be implemented from scratch in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The
Deep Learning Building a State of the Art Bacterial Classifier with Paperspace Gradient and Fast.ai One of the great promises of Deep Learning is its applicability in a wide variety of complex tasks. Recent years have seen an explosion in the number of fields Deep
PyTorch PyTorch 101, Part 5: Understanding Hooks In this post, we cover debugging and Visualisation in PyTorch. We go over PyTorch hooks and how to use them to debug our backpass, visualise activations and modify gradients.
Tutorial PyTorch 101, Part 4: Memory Management and Using Multiple GPUs This article covers PyTorch's advanced GPU management features, including how to multiple GPU's for your network, whether be it data or model parallelism. We conclude with best practises for debugging memory error.
Tutorial PyTorch 101, Part 3: Going Deep with PyTorch In this tutorial, we dig deep into PyTorch's functionality and cover advanced tasks such as using different learning rates, learning rate policies and different weight initialisations etc
PyTorch PyTorch 101, Part 2: Building Your First Neural Network In this part, we will implement a neural network to classify CIFAR-10 images. We cover implementing the neural network, data loading pipeline and a decaying learning rate schedule.
Deep Learning PyTorch 101, Part 1: Understanding Graphs, Automatic Differentiation and Autograd In this article, we dive into how PyTorch's Autograd engine performs automatic differentiation.
Tutorial Convert Full ImageNet Pre-trained Model from MXNet to PyTorch Currently there are many available deep learning frameworks for researchers and engineers to implement their desired deep models. Each deep learning framework has its own advantages and disadvantages. For example,
