PyTorch: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

4.83 (2318 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)

Predict Stock Returns

Time Series Forecasting

Computer Vision

How to build a Deep Reinforcement Learning Stock Trading Bot

GANs (Generative Adversarial Networks)

Recommender Systems

Image Recognition

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Natural Language Processing (NLP) with Deep Learning

Demonstrate Moore's Law using Code

Transfer Learning to create state-of-the-art image classifiers

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

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Comidoc Review

Our Verdict

PyTorch: Deep Learning and Artificial Intelligence is a comprehensive and engaging course that covers various applications of PyTorch in deep learning. With numerous examples and practical exercises, students are given ample opportunity to apply their knowledge and gain hands-on experience with the library. While some students may find the course challenging due to its mathematical content and assumptions about prior knowledge, others will appreciate the depth and breadth of coverage provided by the instructor. However, it is worth noting that a small number of students have reported having a negative experience with the instructor's communication style, which may be a consideration for some learners.

What We Liked

The course offers in-depth coverage of various applications of PyTorch in deep learning, including computer vision, time series forecasting, and natural language processing.
It includes numerous examples and practical exercises that provide hands-on experience with the library.
The instructor explains complicated concepts in a simple and engaging manner, making it easy for students to follow along and learn.
The course is suitable for both beginners and seasoned programmers, with content ranging from linear regression to more advanced topics.

Potential Drawbacks

Some students may find the abundance of equations in the course unhelpful or unnecessary, preferring a more visual approach.
The course assumes some prior knowledge of programming and mathematics, which may be challenging for absolute beginners.
A few students have reported having a negative experience with the instructor's attitude and communication style.
While the course is regularly updated, some parts of it contain legacy code that may not work as expected or follow current best practices.