Boost your career in one week with the cutting-edge field of Deep Learning with PyTorch
About This Video
A systematic guide on Deep Learning to help you build smart applications
Cover core concepts and architectures of Deep Learning systems without getting bogged down in mathematical notation
Solve Machine Learning problems by applying Deep Learning architectures
In Detail
PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has …
PyTorch Deep Learning in 7 Days
Video description
Boost your career in one week with the cutting-edge field of Deep Learning with PyTorch
About This Video
A systematic guide on Deep Learning to help you build smart applications
Cover core concepts and architectures of Deep Learning systems without getting bogged down in mathematical notation
Solve Machine Learning problems by applying Deep Learning architectures
In Detail
PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks. PyTorch has a unique interface that makes it as easy to learn as NumPy.
This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks.
By the end of the course, you will be able to build Deep Learning applications with PyTorch.
Audience
This course is for software development professionals and machine learning enthusiasts, who have heard the hype of Deep Learning and want to learn it to stay relevant in their field. Basic knowledge of machine learning concepts and Python programming is required.
Convolutional Concepts: Filters, Strides, Padding, and Pooling
Implementing a Convolutional Network
Visualizing Convolutional Network Layers
Implementing an End-To-End Deep Convolutional Network
Assignment
Chapter 5 : Implementing Transfer Learning
Transfer Learning and Prebuilt Models
Deep Learning with VGG
Transfer Learning with VGG
Transfer Learning with ResNet
Assignment
Chapter 6 : LSTM and Embedding for Natural Language Models
Recurrent Networks, RNN, and LSTM, GRU
Text Modeling with Bag-of-Words
Sentiment Analysis with Bag-of-Words
Sentiment Analysis with Word Embeddings
Assignment
Chapter 7 : Deep Convolutional Generative Adversarial Networks
Introduction to GANs and DCGANs
Implementing DCGAN Model with PyTorch
Training and Evaluating DCGAN on an Image Dataset
Improving Performance
Assignment
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