What Softmax is, how it’s used, and how to implement it in Python.
Latest Resources in Machine Learning
Machine Learning
A Simple Explanation of the Softmax Function victorzhou.com
Cyclical Learning Rates with Keras and Deep Learning pyimagesearch.com
In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.
Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.
Keras Mask R-CNN pyimagesearch.com
In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).
Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of for each object in an image; Instance segmentation, enabling us to obtain a pixel-wise mask for each individual object in an image.
Artificial Intelligence Made Easy with H2O.ai towardsdatascience.com
A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
Fine-tuning with Keras and Deep Learning pyimagesearch.com
In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning.
We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.
Today is the final post in our three-part series on fine-tuning:
- Part #1: Transfer learning with Keras and Deep Learning
- Part #2: Feature extraction with on large datasets with Keras and Deep Learning
- Part #3: Fine-tuning with Keras and Deep Learning (today’s post)
CNNs, Part 2: Training a Convolutional Neural Network victorzhou.com
In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch (using only numpy), and ultimately building a full training pipeline!
One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how.
Text Analysis in Python to Test a Hypothesis dataquest.io
People often complain about important subjects being covered too little in the news. One such subject is climate change. The scientific consensus is that this is an important problem, and it stands to reason that the more people are aware of it, the better our chances may be of solving it. But how can we assess how widely covered climate change is by various media outlets? We can use Python to do some text analysis!
Understanding PyTorch with an example: a step-by-step tutorial towardsdatascience.com
PyTorch is the fastest growing Deep Learning framework and it is also used by Fast.ai in its MOOC, Deep Learning for Coders and its library. PyTorch is also very pythonic, meaning, it feels more natural to use it if you already are a Python developer.
Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.
How to Automate Tasks on GitHub With Machine Learning for Fun and Profit towardsdatascience.com
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
PyTorch Sentiment Analysis github.com
Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.
