(testing signal)

Tag: keras

Deep learning model to predict mRNA degradation

We will be using TensorFlow as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.# Dataframeimport jsonimport pandas as pdimport numpy as np# Visualizationimport plotly.express as px# Deeplearningimport tensorflow.keras.layers as Limport tensorflow as tf# Sklearnfrom sklearn.model_selection import train_test_split#Setting seedstf.random.set_seed(2021)np.random.seed(2021)Target…

Continue reading: https://pub.towardsai.net/deep-learning-model-to-predict-mrna-degradation-1533a7f32ad4?source=rss—-98111c9905da—4

Source: pub.towardsai.net

How to create a real-time Face Detector

using Python, TensorFlow/Keras and OpenCV

In this article, I will show you how to write a real-time face detector using Python, TensorFlow/Keras and OpenCV.

All code is available in this repo. You can also read this tutorial directly on GitLab. Python code is highlighted there, so it is more convenient to read.

First, in Theoretical Part I will tell you a little about the concepts that will be useful for us (Transfer Learning and Data Augmentation), and then I will go to the code analysis in the Practical Part section.


Why Training your CNN with 16 Bit Images isn’t Working

A caveat when implementing CNNs in Keras and Tensorflow using Uint16 images

Photo by Halacious on Unsplash

Custom Loss Function in TensorFlow

Customise your algorithm by creating the function to be optimised

Why read this article?

In this article and the youtube video above we will recall the basic concepts of the loss function and cost function, we will then see how to create a custom loss function in tensorflow with the Keras API and subclassing the base class “Loss” of Keras.


Darkeras: Execute YOLOv3/YOLOv4 Object Detection on Keras with Darknet Pre-trained Weights

Everything in the universe is connected.


TensorFlow Decision Forests — Train your favorite tree-based models using Keras

Photo by veeterzy on Unsplash

Yes, you read that right — the same API for both Neural Networks and tree-based models!

Eryk Lewinson

In this article, I will briefly describe what decision forests are and how to train tree-based models (such as Random Forest or Gradient Boosted Trees) using the same Keras API as you would normally use for Neural Networks. Let’s dive into it!

I will get straight to the point, it is not another fancy algorithm like XGBoost, LightGBM, or CatBoost. Decision forests are simply a family of machine learning algorithms built from many decision trees. That includes many of your favorites like Random Forest and various flavors of gradient-boosted trees.


Machine Learning is Not Just for Big Tech

For the analysis, the following python libraries were used:

import keras
from keras.layers import Input, Conv1D, Embedding , MaxPooling1D, GlobalMaxPooling1D, Dense
from keras.models import Model
from keras.preprocessing.text import Tokenizer
from keras.optimizers import Adam
from keras.preprocessing.sequence import pad_sequences
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt


The training data was a corpus that compiled online reviews from Yelp, TripAdvisor, and Google Reviews. The reviews covered the past 10 years of operation for Altomonte’s. Each review in the training set had an associated rating from 1 to 5, where 1 is considered bad, and 5 is considered excellent.