PyTorch for Deep Learning with Python Bootcamp (Udemy)

Taught By: Jose Portilla (Head of Data Science, Pierian Data Inc.)

Course Type: Video (Course taught on Udemy)

Course Description: This course covers the following topics that you will lear about:

  • Learn to use NumPy to format data into arrays
  • Learn classic Machine Learning theory principles
  • Lear to use PyTorch with Recurrent Neural Networks for Sequence Time-Series Data
  • Learn to use Pandas for Data Manipulation and Cleaning
  • Learn to use PyTorch Deep Learning Library for Image Classification
  • Create state of the art Deep Learning models to work with tabular data

Prerequisite: You need to have a basic knowledge about Python programming and basic Maths (such as calculating derivatives).

Price: Rs. 490 INR (as of October 2020)

Course Link: Visit the course here

 

Deep Neural Networks with PyTorch (Coursera)

Taught By: Joseph Santarcangelo (PhD., Data Scientist at IBM)

Course Type: Video (Course taught on Coursera)

Course Level: Intermediate

Course Description: This course will teach you how to develop Deep Learning models using PyTorch. It will start with an introduction to PyTorch and you will work on algorithms such as Linear Regression, Logistic Regression and Softmax Regression. It will then move to Neural Networks, Deep Neural Networks and Convolutional Neural Networks where you will learn how to apply them in real-life using PyTorch.

Prerequisite: You need to have basic knowledge in Python programming and a basic knowledge about Machine Learning will be beneficial.

Price: Both Paid and Free

Course Link: Visit the course here

 

 

Intro to Machine Learning with PyTorch (Udacity)

Taught By: This course has been been created by Machine Learning specialists at Udacity. The curriculum lead of the course is Cezanne Camacho (Masters in Electrical Engineering from Stanford University).

Course Type: Video (Course taught on Udacity)

Course Duration: 3 months

Course Description: In this course you will learn about foundational Machine Learning algorithms, starting with Data Cleaning and Supervised Learning. Then you will move to Deep Learning and Unsupervised Learning. This course intended for students and people with experience with Python, who don't have much knowledge about Machine Learning.

Prerequisite: You need to have a basic knowledge about Python programming and a basic knowledge about Probability and Statistics.

Price: Paid

Course Link: Visit the course here

 

Practical Deep Learning with PyTorch (Udemy)

Taught By: Deep Learning Wizard

Course Type: Video (Course taught on Udemy)

Course Duration: This course is self-paced and the video content is about 6.5 hours.

Course Description: This course covers the following topics that you will learn about:

  • PyTorch Fundamentals (Matrices, Variables, Gradients, etc.)
  • Linear Regression with PyTorch
  • Logistic Regression with PyTorch
  • Feed Forward Neural Network with PyTorch
  • Convolutional Neural Network (CNN) with PyTorch
  • Recurrent Neural Network Network with PyTorch
  • Long-Term Short Memory Networks (LSTM) with PyTorch

Prerequisite: You need to have a basic knowledge about Python programming and basics Mathematics such as Differentiation and Linear Algebra.

Price: Rs. 518 INR (as of October 2020)

Course Link: Visit the course here

 

 

Deep Learning with Python and PyTorch (edX)

Taught By: Joseph Santarcangelo (PhD., Data Scientist at IBM)

Course Type: Video (Course taught on edX)

Course Duration: 6 weeks

Course Description: This course covers the following topics where you will learn to implement the respective concepts in PyTorch:

  • Classification (Softmax)
  • Neural Networks (Backpropagation, Activation Functions)
  • Deep Neural Networks (Dropout, Initialization, Batch Normalization, Optimization methods)
  • Convolutional Neural Networks (Convolution, Max-Pooling)
  • Dimensionality Reduction and Auto-Encoders (Principal Component Analysis, Auto-Encoders, Transfer Learning)

Prerequisite: You need to have a basic knowledge about Python programming and you need to have a basic knowledge about Machine Learning and Deep Learning concepts.

Price: Free (You can also get a certificate with this course for Rs. 7,256 INR).

Course Link: Visit the course here

 

Intro to Deep Learning with PyTorch (Udacity)

Taught By: This course has been taught by Machine Learning specialists at Udacity. The Lead Instructor of the course is Luis Serrano. This course is also taught by Soumith Chintala (Co-Creator of PyTorch)

Course Type: Video (Course is taught on Udacity)

Course Description: This course covers the following topics that you will learn about:

  • Introduction to Deep Learning
  • Introduction to PyTorch (Soumith Chintala, the co-creator of PyTorch, will teach you this part)
  • Deep Learning with PyTorch
  • Convolutional Neural Networks with PyTorch
  • Style Transfer
  • Recurrent Neural Networks with PyTorch
  • Natural Language Classification with PyTorch
  • Deploying with PyTorch

Prerequisite: You need to have a basic knowledge about Python programming and data processing libraries such as NumPy and Matplotlib. Basic knowledge of linear algebra and calculus is also recommended but not required.

Price: Paid

Course Link: Visit the course here

 

 

Introduction to Deep Learning with PyTorch (Datacamp)

Taught By: Ismail Elezi (Researcher PhD student at Ca'Foscari University of Venice)

Course Type: Video (Course taught on Datacamp)

Course Description: This course covers the following topics that your will learn about:

  • Introduction to PyTorch and Neural Networks (Tensors, Matrix Multiplication, Backpropagation using PyTorch)
  • Artificial Neural Networks (ReLU activation, Loss function, Training Neural Networks)
  • Convolutional Neural Networks (Convolution, Max Pooling, Training CNN)
  • Using Convolutional Neural Networks (L2 Regularization, Batch Normalization, Transfer Learning, Finetuning a CNN)

Prerequisite: You need to have a basic knowledge about Python programming and a basic knowledge in Machine Learning.

Price: Paid

Course Link: Visit the course here