Recommender Systems And Deep Learning In Python Download UPDATED

Recommender Systems And Deep Learning In Python Download

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Estimated six weeks

ii–4 hours per week

Self-paced

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Deep Learning with Python and PyTorch

About this course

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Delight Note: Learners who successfully complete this IBM class can earn a skill bluecoat — a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this class. Enroll to larn more than, complete the course and claim your badge!

Note: In social club to be successful in completing this course, please ensure y'all are familiar with PyTorch Basics and have applied knowledge to employ it to Machine Learning. If yous do not accept this pre-requiste cognition, it is highly recommended you complete the PyTorch Nuts for Machine Learning form prior to starting this form.

This grade is the second part of a two-part grade on how to develop Deep Learning models using Pytorch.

In the first course, you learned the basics of PyTorch; in this course, you lot volition larn how to build deep neural networks in PyTorch. Too, yous will learn how to train these models using land of the fine art methods. Y'all will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This volition be followed by an in-depth introduction on how to construct Feed-forrad neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.

You will and so learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. Nosotros will and then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You lot will finally learn well-nigh dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.

Finally, you will test your skills in a concluding projection.

Awards

At a glance

• Establishment: IBM
• Subject: Data Analysis & Statistics
• Level: Intermediate
• Prerequisites:
  • Python & Jupyter notebooks
  • Machine Learning concepts
  • Deep Learning concepts
  • https://www.edx.org/class/pytorch-nuts-for-machine-learning

• Linguistic communication: English
• Video Transcript: English
• Associated programs:
  • Professional Certificate in Deep Learning

What you'll learn

Skip What you'll larn

• Apply noesis of Deep Neural Networks and related motorcar learning methods
• Build and Train Deep Neural Networks using PyTorch
• Build Deep learning pipelines

Module ane - Classification

• Softmax Regression
• Softmax in PyTorch Regression
• Preparation Softmax in PyTorch Regression

Module two - Neural Networks

• Introduction to Networks
• Network Shape Depth vs Width
• Back Propagation
• Activation functions

Module three - Deep Networks

• Dropout
• Initialization
• Batch normalization
• Other optimization methods

Module 4 - Computer Vision Networks

• Convolution
• Max Polling
• Convolutional Networks
• Pre-trained Networks

Module five - Computer Vision Networks

• Convolution
• Max Pooling
• Convolutional Networks
• Preparation your model with a GPU
• Pre-trained Networks

Module 6 Dimensionality reduction and autoencoders

• Principle component analysis
• Linear autoencoders
• Autoencoders
• Transfer learning
• Deep Autoencoders

Module 7 -Contained Project

About the instructors

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not exist able to register for this grade: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Function of Foreign Avails Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow the states to offer this course in all locations. edX truly regrets that U.S. sanctions prevent u.s.a. from offering all of our courses to anybody, no matter where they live.

Means to have this course

Choose your path when yous enroll.

	Verified Track	Audit Rails
Price	$99 USD	Costless
Admission to class materials	Unlimited	Limited Expires on Apr 19
World form institutions and universities
edX support
Shareable certificate upon completion
Graded assignments and exams

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