PyTorch Bootcamp for Artificial Neural Networks and Deep Learning Applications [Video]
PyTorch Bootcamp for Artificial Neural Networks and Deep Learning Applications [Video]
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 15m | 5.30 GB
eLearning | Skill level: All Levels
PyTorch Bootcamp for Artificial Neural Networks and Deep Learning Applications [Video]: Hands-on PyTorch boot camp for Artificial Intelligence applications with artificial neural networks and deep learning
Master the latest and hottest deep learning frameworks (PyTorch) for Python data science. This course is your complete guide to practical machine learning and deep learning using the PyTorch framework in Python and covers the important aspects of PyTorch. If you take this course, you’ll have no need to take other courses or buy books on PyTorch.
In this age of big data, companies across the Globe use Python to sift through the avalanche of information at their disposal; the advent of frameworks such as PyTorch is revolutionizing deep learning. By gaining proficiency in PyTorch, you can give your company a competitive edge and take your career to the next level.
After taking this course, you’ll be able to use packages such as Numpy, Pandas, and PIL to work with real data in Python and you’ll be fluent in PyTorch. We even introduce you to deep learning models such as Convolution Neural Networks (CNNs)!
- Deep Learning Basics – Getting started with Anaconda, an important Python data science environment
- Neural Network Python Applications – Configuring the Anaconda environment to get started with PyTorch
- Introduction to Deep Learning Neural Networks – Theoretical underpinnings of important concepts (such as deep learning) without the jargon
- AI Neural Networks – Implementing Artificial Neural Networks (ANNs) with PyTorch
- Neural Network Model – Implementing deep learning (DL) models with PyTorch
- Deep Learning AI – Implement common machine learning algorithms for image classification
- Deep Learning Neural Networks – Implement PyTorch-based deep learning algorithms on image data
The underlying motivation for the course is to ensure you can apply Python-based data science on real data today, start analyzing data for your own projects whatever your skill level, and impress potential employers with actual examples of your data science abilities.
