Deep Learning

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Our team has been continuously working to bring new content under the theme of Artificial Intelligence. We began with a premium course on Neural Network followed by Machine Learning. Here, we proudly present our latest course on Deep Learning which can help you advance in your AI profession. Gain access to world-class education to broaden your technical knowledge, as well as hands-on instruction to learn practical skills.

Deep learning is a machine learning method that guides computers to do what comes typically to humans, i.e., learn by example. Deep learning is a powerful technology behind driverless cars, identifying objects from satellites, detecting cancer cells, voice control like Alexa, Siri, etc. Deep learning performs "end-to-end learning" – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically. Deep learning models can obtain state-of-the-art accuracy, seldom beating human-level performance. Models are trained using a large set of labeled data and neural network architectures containing many layers.

MATLAB makes deep learning easy. With just a few lines of code, MATLAB lets you do deep learning without being an expert. Get started quickly, create and visualize models, and deploy models to devices.

With our intuitive lessons, you will be confident that you understand all of the Deep Learning techniques with MATLAB. Once you get into the hands-on coding problems, you'll discover how much practical experience is valuable. You will learn how to create and train neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, and LSTMs; How to improve them with tactics like Dropout, Batch-Normalization, Xavier/He initialization, etc. and more. This course is going to be a game-changer for you. Prepare to study theoretical principles and their applications in the industry with MATLAB.

Deep Learning is a Premium Course from MATLAB Helper. Book here.

Deep Learning

1 Chapter

Overview

3 Lessons

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from extensive data.

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Have you ever questioned how your images are kept on your computer when you know that your computer only understands 1's and 0's? Let's figure it out together!

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Deep Learning algorithms train the system to detect and classify individuals and objects in the video for video content analysis.

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NN

7 Lessons

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Is it better to use a computer or your brain? Most people would leap at the chance to have a brain like a computer. What are neural networks, and how do they work? What is their mechanism of operation? Let's look at it more closely!

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Backpropagation's most fundamental concept is the chain rule of differentiation. Backpropagation reduces the loss function by updating the weights as we go back in time. Let us look at how backpropagation works in a neural network.

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One of the decisions you'll have to make while designing a neural network is which activation function to employ in the hidden layer and the output layer. Let us discuss some of the options in this lesson.

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Weight initialization is the first step to consider while creating a neural network that produces better and more optimal results. The role of optimizers in network convergence is critical. The time consumed by the network can vary dramatically depending on the optimizers utilized.

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Neural networks are capable of various tasks, ranging from forecasting continuous values such as monthly expenditure to identifying discrete groupings such as cats and dogs. Because the output format will vary, each activity will necessitate a distinct type of loss. Let us look at several forms of cost functions.

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Create a neural network to generalize nonlinear relationships between sample inputs and outputs, and use a simple neural network to solve regression problems.

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Create a neural network to generalize nonlinear relationships between sample inputs and outputs, and use a simple neural network to solve classification problems.

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CNN

3 Lessons

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Convolutional Neural Network is a type of neural network that is particularly good at processing data with a grid-like architecture, such as images. Let us understand the working of CNN.

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Create a convolutional neural network in MATLAB to generalize relationships between sample inputs and outputs, and use a simple neural network to solve classification problems.

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Data augmentation refers to increasing the amount of data by adding slightly modified copies of previously collected data. Learn how to use data augmentation to create and train a convolutional neural network for deep learning classification

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RNN

4 Lessons

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Recurrent neural networks (RNN) are artificial neural networks that allow previous outputs to be used as inputs while having hidden states.

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The Long Short Term Memory Network (LSTMN) is a sophisticated RNN (sequential network) that allows information to be retained.

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GRU (Gated Recurrent Unit) aims to solve the vanishing gradient problem, which comes with a standard recurrent neural network.

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LSTM model can train a deep neural network to classify sequence data. An LSTM network allows us to feed sequence data into a system and identify conclusions based on the sequences data's distinct time steps.

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CNN Architectures

3 Lessons

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A CNN architecture is made up of a series of discrete layers that use a differentiable function to transform the input volume into an output volume.

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Pretrained networks can be used to solve classification issues directly. Use categories() to classify a new image.

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To extract relevant features from new samples, use the representations learned by a previous network.

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Architectures & Applications

2 Lessons

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Visual recognition techniques such as picture classification, object detection, image segmentation, object tracking, optical character recognition, and image captioning are used in computer vision.

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Get introduced to advanced NLP approach; how to use machine learning algorithms to extract information from text input, and a solution strategy.

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Conclusion

2 Lessons

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Summary of the Deep Learning course.

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Score 60%+ for completing the Deep Learning course & get the certificate.

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About the teacher

Anish Yadav

Artificial Intelligence Expert with experience of MATLAB & Simulink

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