Deep Learning with Python and PyTorch
Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library. You'll then apply them to build Neural Networks and Deep Learning models.
Created by: Joseph Santarcangelo
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Course Description
The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.
We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
Module 1 - Introduction to Pytorch What's Deep Learning and why Pytorch
1-D Tensors and useful Pytoch Functions
2-D Tensors and useful functions
Derivatives and Graphs in Pytorch
Data Loader
Module 2 - Linear Regression
Prediction 1D regression
Training 1D regression
Stochastic gradient descent, mini-batch gradient descent
Train, test, split and early stopping
Pytorch way
Multiple Linear Regression
Module 3 - Classification Logistic Regression
Training Logistic Regressions Part 1
Training Logistic Regressions Part 2
Softmax Regression
Module 4 - Neural Networks Introduction to Networks
Network Shape Depth vs Width
Back Propagation
Activation functions
Module 5 - Deep Networks Dropout
Initialization
Batch normalization
Other optimization methods
Module 6 - Computer Vision Networks Convolution
Max Polling
Convolutional Networks
Pre-trained Networks
We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
Module 1 - Introduction to Pytorch What's Deep Learning and why Pytorch
1-D Tensors and useful Pytoch Functions
2-D Tensors and useful functions
Derivatives and Graphs in Pytorch
Data Loader
Module 2 - Linear Regression
Prediction 1D regression
Training 1D regression
Stochastic gradient descent, mini-batch gradient descent
Train, test, split and early stopping
Pytorch way
Multiple Linear Regression
Module 3 - Classification Logistic Regression
Training Logistic Regressions Part 1
Training Logistic Regressions Part 2
Softmax Regression
Module 4 - Neural Networks Introduction to Networks
Network Shape Depth vs Width
Back Propagation
Activation functions
Module 5 - Deep Networks Dropout
Initialization
Batch normalization
Other optimization methods
Module 6 - Computer Vision Networks Convolution
Max Polling
Convolutional Networks
Pre-trained Networks
Instructor Details
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Joseph Santarcangelo
Joseph Santarcangelo is currently working as a Data Scientist at IBM. Joseph has a Ph.D. in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition.
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- Video Duration:
6 Weeks - 2-4 hours per week - Price: Free 0 Track Price
- Provider: edX
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IBM - Level:
Intermediate - Language:
English - Certificate:
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