Deep Learning In Matlab Training and Certification | Online and Classroom Training

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"I've been to a different training facilities for other technologies, and this is one of the few where I've left feeling like I've learned more than I expected.
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Deep Learning In Matlab

Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.
Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking.

Who Should Do ?

B.Tech, M.Tech, PHD Researchers, Other Professional Researchers.

Modules Covered in the Course

TRAINING MODULES

Module 1 - Machine Learning

What Is Machine Learning?
Challenges with Machine Learning

Overfitting
Confronting Overfitting

Types of Machine Learning

Classification and Regression

Module 2 - Neural Network

Nodes of a Neural Network
Layers of Neural Network
Supervised Learning of a Neural Network
Training of a Single-Layer Neural Network: Delta Rule
Generalized Delta Rule
SGD, Batch, and Mini Batch
Stochastic Gradient Descent

Batch
Mini Batch

Example: Delta Rule
Implementation of the SGD Method
Implementation of the Batch Method
Comparison of the SGD and the Batch
Limitations of Single-Layer Neural Networks

Module 3 - Training of Multi-Layer Neural Network

Back-Propagation Algorithm
Example: Back-Propagation

XOR Problem
Momentum

Cost Function and Learning Rule
Example: Cross Entropy Function
Cross Entropy Function
Comparison of Cost Functions

Module 4 - Neural Network and Classification

Binary Classification
Multiclass Classification
Example: Multiclass Classification

Module 5 - Deep Learning

Improvement of the Deep Neural Network

Vanishing Gradient
Overfitting
Computational Load

Example: ReLU and Dropout

ReLU Function
Dropout

Module 6 - Convolutional Neural Network

Architecture of ConvNet
Convolution Layer
Pooling Layer
Example: MNIST

Module 7 - Deep Learning Toolbox

Perform transfer learning to fine-tune a network with your data
Classify images with pretrained networks
Create a new deep neural network for classification or regression
Resize, rotate, or preprocess images for training or prediction
Label your image data automatically based on folder names, or interactively using an app
Create deep learning networks for sequence and time series data
Classify each pixel of an image (for example, road, car, pedestrian)
Detect and recognize objects in images
Classify text data
Classify audio data for speech recognition
Visualize what features networks have learned
Train on CPU, GPU, multiple GPUs, in parallel on your desktop or on clusters in the cloud, and work with data sets too large to fit in memory