Master PyTorch: Deep Learning and Artificial Intelligence
Welcome to the comprehensive PyTorch course covering a wide array of deep learning applications including Computer Vision, NLP, GANs, Reinforcement Learning, and more!
What You’ll Learn:
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Stock Returns Prediction
- Time Series Forecasting
- Computer Vision
- Building a Deep Reinforcement Learning Stock Trading Bot
- Generative Adversarial Networks (GANs)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Natural Language Processing (NLP) with Deep Learning
- Demonstrating Moore’s Law using Code
- Transfer Learning for State-of-the-Art Image Classifiers
Requirements:
- Proficiency in Python and Numpy
- Optional understanding of derivatives and probability for theoretical sections
Course Overview:
In the realm of deep learning and AI, PyTorch has emerged as a preferred tool for professionals and researchers globally. While Tensorflow has its merits, PyTorch, backed by Facebook AI Research (FAIR), offers robust community support and stability without compromising backward compatibility – unlike its counterparts.
Why Choose PyTorch?
- PyTorch is adopted by leading AI entities like OpenAI, Apple, and JPMorgan Chase.
- It facilitates rapid prototyping and testing of new ideas compared to other complex libraries.
- Known for its speed and efficiency in deep learning model development.
Deep Learning Achievements:
Recent advancements enabled by deep learning include generating realistic images with GANs, defeating world champions in games through Deep Reinforcement Learning, autonomous driving with Computer Vision, and enhancing speech recognition and machine translation with NLP.
Course Structure:
This course caters to both beginners and advanced learners, starting with foundational machine learning models and progressing to cutting-edge techniques:
- Architectures Covered: Deep Neural Networks, CNNs (for image processing), RNNs (for sequence data).
- Practical Projects: NLP applications, Recommender Systems, Transfer Learning for CV, GANs, and a Deep RL Stock Trading Bot.
- Unique Content: New projects such as time series forecasting and stock prediction using PyTorch.
Teaching Approach:
Emphasizing practical application over theoretical depth, this course ensures accessibility even for learners less comfortable with mathematical concepts. Focus remains on mastering PyTorch functionalities rather than deriving equations, leveraging the instructor’s extensive teaching experience.
Instructor’s Perspective:
This course prioritizes breadth of knowledge, steering away from dense theory in favor of hands-on project development. For those seeking a deeper theoretical dive, specialized courses on individual topics like reinforcement learning and computer vision are also available.
Who Should Enroll:
- Beginners to advanced students eager to delve into PyTorch for deep learning and AI.
- Learners looking for a fast-paced learning experience with opportunities for deeper exploration.
Instructor: Lazy Programmer Team, Lazy Programmer Inc.
Additional Notes:
- Every line of code is meticulously explained.
- No time wasted on basic typing exercises.
- Includes insights typically overlooked in other courses.
For a roadmap on course sequence and prerequisites, refer to the “Machine Learning and AI Prerequisite Roadmap” available in the FAQ section of any course, including the free Numpy course.
Join now and unlock the potential of PyTorch for mastering deep learning and AI. Let’s embark on this exciting journey together!
Created by Lazy Programmer Team, Lazy Programmer Inc.
English
Size: 7.91 GB
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