IBM - Deep Neural Networks with PyTorch
- Offered byCoursera
Deep Neural Networks with PyTorch at Coursera Overview
Duration | 31 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Deep Neural Networks with PyTorch at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 6 in the IBM AI Engineering
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 31 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Deep Neural Networks with PyTorch at Coursera Course details
- The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
- Learning Outcomes:
- After completing this course, learners will be able to:
- ? explain and apply their knowledge of Deep Neural Networks and related machine learning methods
- ? know how to use Python libraries such as PyTorch for Deep Learning applications
- ? build Deep Neural Networks using PyTorch
Deep Neural Networks with PyTorch at Coursera Curriculum
Tensor and Datasets
1.0 Overview of Tensors
1.1 Tensors 1D
1.2 Two-Dimensional Tensors
Differentiation in PyTorch
1.3 Simple Dataset
1.5 Dataset
1.1 Tensors 1D
1.2 Two-Dimensional Tensors
1.3 Derivatives in PyTorch
Simple Dataset
Datasets
Linear Regression
2.1 Linear Regression Prediction
2.1 Linear Regression Training
Loss
Gradient Descent
Cost
Linear Regression PyToch
PyTorch Linear Regression Training Slope and Bias
Prediction in One Dimension
Linear Regression Training
Loss
Gradient Descent
Cost
Training Parameters in PyTorch
PyTorch Linear Regression Training Slope and Bias
Stochastic Gradient Descent
Mini-Batch Gradient Descent
Optimization in PyTorch
Training, Validation and Test Split
Training, Validation and Test Split PyTorch
Quiz: Stochastic Gradient Descent
Mini-Batch Gradient Descent
3.3 Optimization in PyTorch
Training and Validation Data PyTorch
Multiple Input Output Linear Regression
Multiple Linear Regression Prediction
Multiple Linear Regression Training
Linear Regression Multiple Outputs
Multiple Output Linear Regression Training
Multiple Linear Regression Prediction
Multiple Output Linear Regression
5.0 Linear Classifiers
5.1 Logistic Regression: Prediction
Bernoulli Distribution and Maximum Likelihood Estimation
Logistic Regression Cross Entropy Loss
5.0 Linear Classifiers
5.0 Linear Classifiers
5.1 Logistic Regression: Prediction
Bernoulli Distribution and Maximum Likelihood Estimation
5.3 Logistic Regression Cross Entropy Loss
Softmax Rergresstion
6.1 Softmax
6.2 Softmax Function:Using Lines to Classify Data
Softmax PyTorch
6.1 Softmax Function:Using Lines to Classify Data
6.2 Softmax Prediction
6.3 Softmax PyTorch Quizz
What's a Neural Network
More Hidden Neurons
Neural Networks with Multiple Dimensional Input
7.4 Multi-Class Neural Networks
7.5 Backpropagation
7.5 Activation Functions
Neural Networks
More Hidden Neurons
Neural Networks with Multiple Dimensional Inputs
Multi-Class Neural Networks
Backpropagation
Activation Functions
Deep Networks
8.1.1 Deep Neural Networks
8.1.2 Deeper Neural Networks : nn.ModuleList()
8.2 Dropout
8.3 Neural Network initialization Weights
8.4 Gradient Descent with Momentum
Batch Normalization
Deep Neural Networks
Deeper Neural Networks : nn.ModuleList()
Dropout
Neural Network initialization
Gradient Descent with Momentum
Batch Normalization
Convolutional Neural Network
9.1 Convolution
9.2 Activation Functions and Max Polling
9.3. Multiple Input and Output Channels
9.4.1 Convolutional Neural Network
9.4.2 Convolutional Neural Network
GPU in PyTorch
TORCH-VISION MODELS
9.1 Convolution
Activation Functions and Max Pooling
Convolutional Neural Network
Convolutional Neural Networks
TORCH-VISION MODELS
Peer Review
Deep Neural Networks with PyTorch at Coursera Admission Process
Important Dates
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