Published 6/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.52 GB | Duration: 8h 43m
Complete package of deep learning
What you’ll learn
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Clean your input data to remove outliers
Advanced usage of python
Requirements
Just a basic understanding of programming. We’ll take care of the rest !!
Description
To sidestep the unbending idea of PCs, complex and diverse profound brain networks are constructed and utilized as a base for learning. Rather than working as a sequential program, profound learning models work utilizing networks joined by hubs — like how the human mind utilizes a large number of associated neurons to work. It’s the blend of designing advances, best practices, and hypotheses that empowers an abundance of beforehand unimaginable savvy applications.Regardless of as yet being a programmable machine, profound learning frameworks don’t deal with information like your normal PC. A solitary profound brain network comprises various layers — the more layers there are, the more exact the outcomes. Be that as it may, rather than working independently or alternating, they expand upon one another’s work. While brain networks fluctuate by the way they work, and not every one of them process information in a straight design, they all have a comparative establishment.Rather than working with the crude information of the info once more, each layer works with a somewhat more refined and streamlined rendition in view of the past layer’s forecast and classification. The work the machine does inside the layers remains for the most part covered up, except for information and result layers, otherwise called the ‘apparent layers’.
Overview
Section 1: INTRODUCTION
Lecture 1 Introduction to Deep Learning
Lecture 2 Introduction to Deep Learning – neural networks
Section 2: DEEP LEARNING MODELS
Lecture 3 Deep Learning Models – basics
Lecture 4 Deep Learning Models – DETAILED
Lecture 5 Deep Learning Models – neural network and its characteristics
Lecture 6 Deep Learning Models – neural network and its characteristics
Lecture 7 Deep Learning Models – neural network and its characteristics
Section 3: ADDITIONAL MODELS
Lecture 8 Additional Deep Learning Models – Part 1
Lecture 9 Additional Deep Learning Models – Part 2
Lecture 10 Additional Deep Learning Models – Generative Adversarial Networks (GAN)
Section 4: LIBRARIES
Lecture 11 Deep Learning Platforms Libraries – basics
Lecture 12 Deep Learning Platforms Libraries – basics
Lecture 13 Deep Learning Platforms Libraries – DataGraph Lab
Lecture 14 Deep Learning Platforms Libraries – DataGraph Lab Conclusion
Lecture 15 Deep Learning Platforms Libraries – Theano and Caffe
Lecture 16 Deep Learning Platforms Libraries – TensorFlow part -1
Lecture 17 Deep Learning Platforms Libraries – Theano or TensorFlow Part – 2
Python developers curious about deep learning and data science,Programming enthusiasts,Students who wish to broaden their skillsets in python
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