Fresh unveiling of deep learning course, accompanied by four libraries and an extensive 600-page guidebook, on our site.
In an exciting development for the tech community, the self-funded research, software development, and teaching lab known for making deep learning more accessible has announced the release of FastAI v2 and a new book, "Deep Learning for Coders with fastai and PyTorch."
FastAI v2, a rewritten version of their deep learning library, promises to be faster, easier, and more flexible. The new library is designed around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable.
The creators of the fastai deep learning library, Jeremy Howard and Sylvain Gugger, have written the new book to guide programmers with basic programming skills but no prior experience in machine learning. The book covers the latest advances in computer vision, natural language processing, and tabular data processing, making it an invaluable resource for those looking to delve into the world of deep learning.
FastAI v2 includes several new features, such as a new type dispatch system for Python, a GPU-optimized computer vision library, an optimizer, a novel 2-way callback system, a new data block API, and more. It also incorporates foundational libraries called fastcore and fastgpu, which simplify parallel processing and offer high-level abstractions, respectively.
The book is organized to gradually dig deeper into understanding the tools and components of deep learning models, ensuring a comprehensive learning experience. It uses examples to explain concepts and covers key topics like data ethics that some other books miss.
In addition to FastAI v2, the website is also releasing Practical Deep Learning for Coders (2020 course, part 1), which covers both an introduction to machine learning and deep learning, and production and deployment of data products. This course, like the book, is designed to be accessible and practical, making deep learning concepts easy to understand for beginners.
Recently, the website also released the Practical Data Ethics course, focusing on urgent and practical topics. This course underscores the lab's commitment to promoting responsible and ethical practices in the field of deep learning.
The book's accompanying Notebooks, available on the Colab and Gradient platforms, allow for completion without any installation. This feature makes the book and course accessible to a wider audience, furthering the lab's mission of making deep learning more accessible.
FastAI v2's ease of use and efficiency have been praised by industry experts. It allows for complex tasks to be accomplished with fewer lines of code compared to other libraries like Keras, making it a popular choice among beginners and experienced programmers alike.
For the most current features and innovations in FastAI v2, it's best to consult the official documentation or recent updates from the developers. The book, Deep Learning for Coders with fastai and PyTorch, is already being hailed as a game-changer in the field of deep learning, offering a practical and accessible approach to this complex but exciting field.
- The new fastai deep learning library, FastAI v2, is designed to be fast, easy, and flexible, focusing on approachability and productivity.
- Jeremy Howard and Sylvain Gugger, creators of the fastai deep learning library, have written a new book titled "Deep Learning for Coders with fastai and PyTorch" to guide programmers with basic skills into the world of deep learning.
- The book covers advancements in computer vision, natural language processing, and tabular data processing, making it an essential resource for those new to the field.
- FastAI v2 includes new features such as a type dispatch system for Python, a GPU-optimized computer vision library, an optimizer, a 2-way callback system, a new data block API, and more.
- The library also incorporates fastcore and fastgpu, libraries simplifying parallel processing and offering high-level abstractions, respectively.
- The book is structured to provide a comprehensive learning experience, gradual understanding of tools and components, and coverage of key topics like data ethics often missed in other books.
- The accompanying Notebooks, available on Colab and Gradient platforms, enable completion without installation for wider accessibility.
- Industry experts have praised FastAI v2 for its ease of use and efficiency, allowing complex tasks to be accomplished with fewer lines of code compared to other libraries like Keras.
- The lab's website also offers the Practical Deep Learning for Coders (2020 course, part 1), covering an introduction to machine learning, deep learning, and production and deployment of data products.
- The lab recently released the Practical Data Ethics course, emphasizing responsible and ethical practices in deep learning.
- The book, Deep Learning for Coders with fastai and PyTorch, is already being applauded as a game-changer, offering a practical and accessible approach to deep learning.
- The book covers the latest technology and artificial intelligence concepts applied to data-and-cloud-computing, technology, education-and-self-development, and the vision of AI and computer vision.
- The new book and course materials, specifically the Notebooks, are accessible on the Jupyter platform for hassle-free learning and practical inference in audio, image, and text.
- Machine learning techniques explained in the course include gradient descent, classification, and transformers, learning essential skills for both beginners and experienced programmers.
- While FastAI v2 represents a significant leap forward in the field of deep learning, it's essential to regularly consult the official documentation or updates from the developers for the most current features and innovations.
