Deep Learning with Python and PyTorch (edX)

Deep Learning with Python and PyTorch (edX)
Course Auditing
Categories
Effort
Certification
Languages
Python & Jupyter notebooks. Machine Learning concepts. Deep Learning concepts
Misc

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Deep Learning with Python and PyTorch (edX)
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch. In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods.

Class Deals by MOOC List - Click here and see edX's Active Discounts, Deals, and Promo Codes.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

NOTE: In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. If you do not have this pre-requiste knowledge, it is highly recommended you complete the PyTorch Basics for Machine Learning course prior to starting this course.

You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward 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 then 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. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will finally learn about 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 final project.

This course is part of the Deep Learning Professional Certificate.


What you'll learn

- Apply knowledge of Deep Neural Networks and related machine learning methods

- Build and Train Deep Neural Networks using PyTorch

- Build Deep learning pipelines


Syllabus


Module 1 - Classification

- Softmax Regression

- Softmax in PyTorch Regression

- Training Softmax in PyTorch Regression


Module 2 - Neural Networks

- Introduction to Networks

- Network Shape Depth vs Width

- Back Propagation

- Activation functions


Module 3 - Deep Networks

- Dropout

- Initialization

- Batch normalization

- Other optimization methods


Module 4 - Computer Vision Networks

- Convolution

- Max Polling

- Convolutional Networks

- Pre-trained Networks


Module 5 - Computer Vision Networks

- Convolution

- Max Pooling

- Convolutional Networks

- Training 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 -Independent Project



0
No votes yet

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
83.00 EUR
Python & Jupyter notebooks. Machine Learning concepts. Deep Learning concepts

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.