Duration: Hours

MATLAB lets you build deep learning models with minimal code. With MATLAB, you can quickly import pretrained models and visualize and debug intermediate results as you adjust training parameters. Perform Deep Learning Without Being an Expert. You can use MATLAB to learn and gain expertise in the area of deep learning.

Training Mode: Online


Deep Learning with MATLAB : It is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Deep learning uses neural networks to learn useful Representations of Features directly from data.

Transfer Learning for Image Classification

Objective: Perform image Classification using Pretrained networks. Use transfer learning to train Customized Classification networks.
1. Pretrained networks
2. Image datastores
3. Transfer learning
4. Network evaluation

Interpreting Network Behaviour

Objective: Gain insight into how a network is operating by Visualizing image data as it passes through the network. Apply this technique to different kinds of images.
1. Activations
2. Feature extraction for machine learning

Creating Networks on MATLAB

Objective: Build Convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
1. Training from scratch
2. Neural networks
3. Convolution layers and filters

Training a Network

Objective: Understand how training Algorithms work. Set training options to monitor and control training.
1. Network training
2. Training progress plots
3. Validation

Improving Network Performance

Objective: Choose and Implement Modifications to training Algorithm options, network architecture, or training data to improve network performance.
1. Training options
2. Directed acyclic graphs
3. Augmented datastores

Performing Image Regression

Objective: Create Convolutional networks that can predict Continuous numeric responses.
1. Transfer learning for regression
2. Evaluation metrics for regression networks

Using Deep Learning for Computer Vision

Objective: Train networks to locate and label specific objects within images.
1. Image application workflow
2. Object detection

Classifying Sequence Data

Objective: Build and train networks to perform Classification on ordered Sequences of data, such as time series or sensor data.
1. Long short-term memory networks
2. Sequence classification
3. Sequence preprocessing
4. Categorical sequences

Generating Sequences of Output

Objective: Use Recurrent networks to create Sequences of Predictions.
1. Sequence to sequence classification
2. Sequence forecasting


For more inputs on Deep Learning with MATLAB you can connect here.
Contact the L&D Specialist at Locus IT.


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