PyTorch has become a popular framework for building Deep Learning models, offering flexibility and ease of use for developers working in the field of Artificial Intelligence. Its dynamic computational graph and strong community support make it a go-to tool for both research and production-level AI applications.
What you’ll 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
Description
Welcome to PyTorch: The Future of Deep Learning and Artificial Intelligence
In recent years, Google’s deep learning library, TensorFlow, has gained significant popularity in the field of Artificial Intelligence (AI). However, PyTorch has rapidly emerged as the go-to library for professionals and researchers in Deep Learning and AI due to its flexibility, ease of use, and strong community support.
But is TensorFlow’s success purely driven by Google’s widespread influence and marketing? While TensorFlow enjoys global recognition, PyTorch—backed by Facebook’s AI Research Lab (FAIR)—is quickly becoming the preferred tool for AI practitioners, thanks to its dynamic computational graph and intuitive interface.
One reason for the growing shift towards PyTorch is that TensorFlow 2.0 introduced significant changes from its predecessor, raising concerns about backward compatibility and usability. In contrast, PyTorch maintains a stable and user-friendly evolution, ensuring that your existing deep learning code won’t break with future updates.
With backing from tech giants like Facebook and Google, both PyTorch and TensorFlow have strong foundations. However, if you're looking for a deep learning library that combines community-driven improvements with long-term stability, PyTorch stands out as a top choice for AI development.
Why Top AI Shops Choose PyTorch for Deep Learning and Artificial Intelligence
In the ever-evolving landscape of Deep Learning and Artificial Intelligence (AI), PyTorch has become the framework of choice for industry leaders like OpenAI, Apple, and JPMorgan Chase. A notable shift happened in 2020 when OpenAI fully transitioned to PyTorch, signaling its growing dominance in the AI community.
For AI professionals, PyTorch offers an unparalleled experience in developing and testing new ideas, thanks to its user-friendly interface and flexibility. Unlike other libraries that try to do everything for you, PyTorch allows for greater control, and as a bonus, it’s faster.
Achievements of Deep Learning
Recent advances in Deep Learning have led to some groundbreaking accomplishments, including:
- Generative Adversarial Networks (GANs): Generating stunning, photo-realistic images of people and objects that don’t exist.
- Deep Reinforcement Learning: Defeating world champions in complex strategy games like Go, CS, and Dota 2.
- Computer Vision: Powering self-driving cars.
- Natural Language Processing (NLP): Enabling technologies like speech recognition (Siri) and machine translation.
- DeepFakes: Creating convincing videos of people doing and saying things they never did, a controversial application of deep learning.
A Course for All Levels
This course is tailored to both beginner and expert-level students. If you've completed my free Numpy prerequisite, you're already equipped to dive right into machine learning models. We’ll start with basic concepts and gradually progress to state-of-the-art Deep Learning techniques.
As you advance, you’ll explore key Deep Learning architectures, including:
- Deep Neural Networks (DNNs)
- Convolutional Neural Networks (CNNs) for image processing
- Recurrent Neural Networks (RNNs) for sequential data
Exciting Projects Await You
This course includes cutting-edge projects such as:
- Natural Language Processing (NLP)
- Recommender Systems
- Transfer Learning for computer vision
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning for building a stock trading bot
Even if you’ve taken my previous courses, you'll learn how to convert your existing code to PyTorch, and you'll tackle all-new projects like time series forecasting and stock predictions.
Learn at Your Own Pace
While this course is designed for students who want to learn fast, there are also "in-depth" sections for those who want to dive deeper into the theory behind concepts like loss functions and different types of gradient descent.
Whether you're confident with the math or not, you’ll still be able to excel in this course. Our focus is on using the PyTorch library to build real-world applications without getting bogged down in complex equations.
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 12/2023
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