Remote Course for Applied Deep Learning with PyTorch

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Description
Content
Target Audience
Benefits
Certificates

Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The course begins by helping you browse through the basics of deep learning and PyTorch.

Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you’ll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN).

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this course useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.

Introduction to Deep Learning and PyTorch
Building Blocks of Neural Networks
Classification Problem Using DNN
Convolutional Neural Networks
Style Transfer
Analyzing the Sequence of Data with RNNs

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques.

Anyone who wants to explore and implement advanced algorithms with PyTorch will also find this course useful. Some working knowledge of Python and knowledge of machine learning fundamentals is required. However, knowledge of NumPy and Pandas will be helpful but not essential.

Detect a variety of data problems to which you can apply deep learning solutions
Learn the PyTorch syntax and build a single-layer neural network with it
Build a deep neural network to solve a classification problem
Develop a style transfer model
Implement data augmentation and retrain your model
Build a system for text processing using a recurrent neural network

Certificate of attendance at the course published by Semos Education

Description

Content

Introduction to Deep Learning and PyTorch
Building Blocks of Neural Networks
Classification Problem Using DNN
Convolutional Neural Networks
Style Transfer
Analyzing the Sequence of Data with RNNs

Target Audience

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques.

Benefits

Detect a variety of data problems to which you can apply deep learning solutions
Learn the PyTorch syntax and build a single-layer neural network with it
Build a deep neural network to solve a classification problem
Develop a style transfer model
Implement data augmentation and retrain your model
Build a system for text processing using a recurrent neural network

Certificates

Certificate of attendance at the course published by Semos Education

Our students for us:

- Aleksandar Maksimov Student CertNexus Artificial Intelligence Academy

Because Artificial Intelligence is the challenge of the future. With the modernization of lifestyles and technological advancements on a global scale, artificial intelligence is increasingly playing a key role in all aspects of life and development in society.
- Kristijan Stojoski Artificial Intelligence Academy

With taking the first step and investing enough effort, everyone can master this modern topic and stand out in the job market in one of the fastest-growing industries in the world.
- Viktor Vanchov Artificial Intelligence Academy

The final project taught me many useful things, beyond the realm of video games. However, it greatly helped me get an idea of how machines 'learn' and how powerful they can be.