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Tag: tensorflow

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

Fourier Transforms (and More) Using Light

Linear transforms — like a Fourier transform — are a key math tool in engineering and science. A team from UCLA recently published a paper describing how they used deep learning techniques to design an all-optical solution for arbitrary linear transforms. The technique doesn’t use any conventional processing elements and, instead, relies on diffractive surfaces. They also describe a “data free” design approach that does not rely on deep learning.

There is obvious appeal to using light to compute transforms. The computation occurs at the speed of light and in a highly parallel fashion. The final system will have multiple diffractive surfaces to compute the final result.… Read more...

A Breakdown of Deep Learning Frameworks – KDnuggets

What is a Deep Learning Framework?

A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning.

These frameworks offer building blocks for designing, training, and validating models through a high-level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.

Why Use a Deep Learning Framework?


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.


Introducing TensorFlow Similarity

Often we need to be able to find things that are like other things. Similarity searching is a useful technique for doing so. In data science, contrastive learning can be used to build similarity models which can then be used for similarity searching.

Similarity models are trained to output embeddings in which items are embedded in a metric space, resulting in a situation where similar items are close to one another and further from dissimilar items. This is directly related — both intuitively and mathematically — to word embeddings, with which you are already familiar; Paris and London are close to one another, as are mustard and ketchup, but these 2 groups are comparatively further apart from one another.


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

Boy or Girl? A Machine Learning Web App to Detect Gender from Name

Find out a name’s likely gender using Natural Language Processing in Tensorflow, Plotly Dash, and Heroku.

Choosing a name for your child is one of the most stressful decisions you’ll have to make as a new parent. Especially for a data-driven guy like me, having to decide on a name without any prior data about my child’s character and preferences is a nightmare come true!

Since my first name starts with “Marie,” I’ve gone through countless experiences of people addressing me as “Miss” over emails and text only to be disappointed to realize that I’m actually a guy when we finally meet or talk 😜.


Reviewing the TensorFlow Decision Forests library

A library to build tree-based models with Tensorflow and Keras

Parul Pandey

In their paper, Tabular Data: Deep Learning is Not All You Need, the authors argue that while deep learning methods have shown tremendous success in the image and text domains, traditional tree-based methods like XGBoost still continue to shine when it comes to tabular data. The authors examined Tabnet, Neural Oblivious Decision Ensembles (NODE), DNF-Net, and 1D-CNN deep learning models and compared their performance on eleven datasets with XGBoost.


News category classification: fine-tuning RoBERTa on TPUs with TensorFlow




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.


Deserialization bug in TensorFlow machine learning framework allowed arbitrary code execution

Ben Dickson

31 August 2021 at 11:05 UTC

Updated: 31 August 2021 at 11:44 UTC

Developers revoke YAML support to protect against exploitation

The team behind TensorFlow, Google’s popular open source Python machine learning library, has revoked support for YAML due to an arbitrary code execution vulnerability.

YAML is a general-purpose format used to store data and pass objects between processes and applications. Many Python applications use YAML to serialize and deserialize objects.

According to an advisory on GitHub, TensorFlow and Keras, a wrapper library for TensorFlow, used an unsafe function to deserialize YAML-encoded machine learning models.

A proof-of-concept shows the vulnerability being exploited to return the contents of a sensitive system file:

“Given that YAML format support requires a significant amount of work, we have removed it for now,” the maintainers of the library said in their advisory.


How to Train your own TensorFlow models, and run them on shared hardware

Photo by Alexander Sinn on Unsplash
pip3 install tensorflow
pip3 install tflite-model-maker
pip3 install numpy~=1.19.2
pip3 install pandas
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tflite_model_maker import model_spec
from tflite_model_maker import text_classifier
from tflite_model_maker.config import ExportFormat
from tflite_model_maker.text_classifier import AverageWordVecSpec
from tflite_model_maker.text_classifier import DataLoader
# Get the data
data_dir = tf.keras.utils.get_file(
data_dir = os.path.join(os.path.dirname(data_dir),

7. How TensorFlow Works?

Tensor Flow permits the subsequent:

  • Tensor Flow helps you to deploy computation to as a minimum one or extra CPUs or GPUs in a computing tool, server, or mobile device in a completely easy manner. This way the matters may be completed very speedy.
  • Tensor Flow lets you specific your computation as a statistics glide graph.
  • Tensor Flow helps you to visualize the graph using the in-constructed tensor board. You can test and debug the graph very without difficulty.
  • Tensor Flow offers the amazing regular overall performance with an capability to iterate brief, teach models faster and run more experiments. Python Course Online
  • Tensor Flow runs on almost the entirety: GPUs and CPUs—together with cellular and embedded systems—or even tensor processing gadgets (TPUs), which may be specialized hardware to do the tensor math on.

Hyperparameter Tuning with KerasTuner and TensorFlow

Understand best practices to optimize your model’s architecture and hyperparameters with KerasTuner and TensorFlow

Figure 0. Cover illustration | Image by author

Building machine learning models is an iterative process that involves optimizing the model’s performance and compute resources. The settings that you adjust during each iteration are called hyperparameters. They govern the training process and are held constant during training.

The process of searching for optimal hyperparameters is called hyperparameter tuning or hypertuning, and is essential in any machine learning project. Hypertuning helps boost performance and reduces model complexity by removing unnecessary parameters (e.g., number of units in a dense layer).


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.


NLP Datasets from HuggingFace: How to Access and Train Them

NLP Datasets library from hugging Face provides an efficient way to load and process NLP datasets from raw files or in-memory data. The datasets library has a total of 1182 datasets that can be used to create different NLP solutions. You can use this library with other popular machine learning frameworks in machine learning, such as Numpy, Pandas, Pytorch, and TensorFlow. All these datasets can also be browsed on the HuggingFace Hub and can be viewed and explored online.

Davis David Hacker Noon profile picture

@davisdavidDavis David

Data Scientist | AI Practitioner | Software Developer. Giving talks, teaching, writing.

The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data.


15 Articles and Tutorials about Outliers

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC

15 Articles and Tutorials about Outliers

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Continue reading: https://www.datasciencecentral.com/xn/detail/6448529%3ABlogPost%3A523312

Source: www.datasciencecentral.com

Overview of Albumentations: Open-source library for advanced image augmentations

By Olga Chernytska, Senior Machine Learning Engineer

Native PyTorch and TensorFlow augmenters have a big disadvantage – they cannot simultaneously augment an image and its segmentation mask, bounding box, or keypoint locations. So there are two options – either write functions on your own or use third-party libraries. I tried both, and the second option is just better 🙂

Why Albumentations?

Albumentations was the first library that I’ve tried, and I’ve stuck with it, because:

  • It is open-source,
  • Intuitive,
  • Fast,
  • Has more than 60 different augmentations,
  • Well-documented,
  • And, what is most important, can simultaneously augment an image and its segmentation mask, bounding box, or keypoint locations.