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G2Net Basic audio data augmentation inference

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01bbf1f2e5254c9998dc3e291afc2043 Copied From Medium Click here to Read More On Medium! Subscribe to ONEPAGECODE Newsletter. COLAB = False if COLAB == True : from google.colab import drive drive.mount( '/content/drive' ) % cd '/content/drive/MyDrive/Colab Notebooks/kaggle/G2Net2022/code' ! pip3 install timm - q WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv import numpy as np import pandas as pd import matplotlib.pyplot as plt import time import h5py import timm import torch import torch.nn as nn import torchaudio import torchvision.transforms as TF from tqdm.auto import tqdm from sklearn.model_selection import KFold from sklearn.metrics import roc_auc_score from timm.scheduler import CosineLRS...
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Does Autocorrect Make Life Better?

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  Systemic failures in machine learning: a cautionary tale. Data science can reduce friction and inconvenience in our everyday lives by applying to many products and businesses. Photo by   Gertrūda Valasevičiūtė   on   Unsplash Every device and service we use is embedded with crafted machine learning models. We become ever more free to focus on what matters in life as they tirelessly remove all irritations and burdens from our lives. What are the chances of this becoming a reality? Taking stock of the many ways machine learning fails us in everyday life is crucial if we ever hope to realize the potential of these technologies. Among the many forms of psychopathy manifest in chatbots are racist image classifiers, sexist recruitment tools, and racist image classifiers. We should instead consider one of the most common forms of machine learning failure, one which affects minorities just as much as majorities: autocorrection. Digital assistance can be as simple as autoco...
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Neural Networks from Scratch For Beginner and Also For Experts

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NN-from-Scratch Neural Networks from Scratch ¶ Chia-Hung Yuan & DataLab Fall 2020 In this tutorial, you will learn the fundamentals of how you can build neural networks without the help of the deep learning frameworks, and instead by using NumPy. Creating complex neural networks with different architectures in Python should be a standard practice for any Machine Learning Engineer and Data Scientist. But a genuine understanding of how a neural network works is equally as valuable. This is what we aim to expand on in this article, the very fundamentals on how we can build neural networks, without the help of the frameworks that make it easy for us. Model architecture ¶ We are building a basic deep neural network with 3 layers in total: 1 input layer, 1 hidden layers and 1 output layer. All layers will be fully connected. We implement ReLU and sigmoid activation functions. SGD and Momentum optimizer are available. Let'...
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