Depthwise Separable Convolution - The technique of depthwise separable convolution was introduced in the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam, published in 2017.
🔊 Watch till last for a detailed description
💯 Read Full Blog with Code
[ Ссылка ]
💬 Leave your comments and doubts in the comment section
📌 Save this channel and video for watch later
👍 Like this video to show your support and love ❤️
~~~~~~~~
🆓 Watch My Top Free Data Science Videos
👉🏻 Python for Data Scientist
[ Ссылка ]
👉🏻 Machine Learning for Beginners
[ Ссылка ]
👉🏻 Feature Selection in Machine Learning
[ Ссылка ]
👉🏻 Text Preprocessing and Mining for NLP
[ Ссылка ]
👉🏻 Natural Language Processing (NLP)
Tutorials [ Ссылка ]
👉🏻 Deep Learning with TensorFlow 2.0
and Keras [ Ссылка ]
👉🏻 COVID 19 Data Analysis and Visualization
Masterclass [ Ссылка ]
👉🏻 Machine Learning Model Deployment Using
Flask at AWS [ Ссылка ]
👉🏻 Make Your Own Automated Email Marketing
Software in Python [ Ссылка ]
***********
🤝 BE MY FRIEND
🌍 Check Out ML Blogs: [ Ссылка ]
🐦Add me on Twitter: [ Ссылка ]
📄 Follow me on GitHub: [ Ссылка ]
📕 Add me on Facebook: [ Ссылка ]
💼 Add me on LinkedIn: [ Ссылка ]
👉🏻 Complete Udemy Courses: [ Ссылка ]
⚡ Check out my Recent Videos: [ Ссылка ]
🔔 Subscribe me for Free Videos: [ Ссылка ]
🤑 Get in touch for Promotion: info@kgptalkie.com
✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐
ENROLL in My Highest Rated Udemy Courses
to 🔑 Unlock Data Science Interviews 🔎 and Tests
📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS
Build & Deploy ML NLP Models with Real-world use Cases.
Multi-Label & Multi-Class Text Classification using BERT.
Course Link: [ Ссылка ]
📊 📈 Data Visualization in Python Masterclass: Beginners to Pro
Visualization in matplotlib, Seaborn, Plotly & Cufflinks,
EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.
Course Link: [ Ссылка ]
📘 📙 Natural Language Processing (NLP) in Python for Beginners
NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn,
Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT
Course Link: [ Ссылка ]
📈 📘 2021 Python for Linear Regression in Machine Learning
Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch.
Course Link: [ Ссылка ]
📙📊 2021 R 4.0 Programming for Data Science || Beginners to Pro
Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more.
Course Link: [ Ссылка ]
Ещё видео!