Mastering the Fundamentals of Artificial Intelligence: A Comprehensive Guide in Deep Learning
In an ongoing commitment to providing free, practical, and cutting-edge education for deep learning practitioners and educators, a new course titled "Deep Learning from the Foundations" has been announced. This course, taught by an individual, promises to equip learners with the foundational knowledge needed to build upon as the field of deep learning continues to evolve rapidly.
The course is a collaborative effort between the website and Google Brain's Swift for TensorFlow group, resulting in Google releasing a new version of Swift for TensorFlow (0.4) to accompany the new course. This version is designed to offer improved performance and functionality for deep learning tasks.
One of the key features of the course is a new GPU-based data augmentation approach, which significantly speeds up the process, making it more efficient for deep learning practitioners. The course also delves into various academic papers that form the foundations of modern deep learning, such as "Understanding the difficulty of training deep feedforward neural networks" and "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift".
The course is divided into two parts. The first part, "Practical Deep Learning for Coders", serves as a required prerequisite, while the second part, the new "Deep Learning from the Foundations", will be released in the coming months. More lessons on applications of deep learning are also planned for the future.
The teaching method for this course is "code-first", where each method is implemented from scratch in Python and explained in detail. The first five lessons of the series focus on audio processing and audio models, using Python, PyTorch, and the fastai library. The last two lessons, co-taught by Chris Lattner, the original developer of Swift and the lead of the Swift for TensorFlow project at Google Brain, delve into Swift for TensorFlow and cover high-performance mixed-precision training, various neural network architectures, and learning techniques.
Chris Lattner also discusses kernel fusion, XLA, and MLIR, exciting technologies coming soon to Swift programmers. He shows how to access and change LLVM builtin types directly from Swift, providing insights on using types to reduce errors and explaining some of the key pieces of Swift syntax needed to get started.
In addition, the course covers topics such as matrix multiplication, loss functions, optimizers, the training loop, and looking inside the model. It also introduces a DataBunch container for the transformed data, flexible functions for splitting and labeling data, and a generic optimizer, callbacks, etc., to train Imagenette from scratch in Swift.
The Data Block API, implemented in Swift, takes advantage of Swift's protocols and offers an even better version than the original Python one. It is used to create transformations using a simple but powerful function composition and to learn a highly optimized way to access the filesystem and a powerful recursive tree walking abstraction.
While there is no widely recognized official course titled "Deep Learning from the Foundations" in major educational platforms, the closest available and comprehensive advanced deep learning course is the New York University (NYU) Deep Learning course led by Yann LeCun and Alfredo Canziani. This course covers topics such as supervised, unsupervised, and self-supervised models, graph theory concepts, graph neural networks, and their applications, control and optimization techniques, and advanced architectures like convolutional and recurrent neural networks.
[1] NYU Deep Learning course:
- The new course, "Deep Learning from the Foundations," aims to provide learners with foundational knowledge for deep learning, a rapidly evolving field.
- This course is a collaboration between a website and Google Brain's Swift for TensorFlow group, resulting in a new version of Swift for TensorFlow (0.4).
- The course includes a GPU-based data augmentation approach, improving the efficiency of deep learning tasks.
- The course examines academic papers such as "Understanding the difficulty of training deep feedforward neural networks" and "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift."
- The course is divided into two parts, with the first, "Practical Deep Learning for Coders," serving as a required prerequisite.
- The second part, the new "Deep Learning from the Foundations," focuses on the foundations of deep learning and will be released in the coming months.
- The course teaches methods from scratch in Python, using PyTorch and the fastai library for audio processing and audio models.
- The last two lessons of the series, co-taught by Chris Lattner, cover topics like Swift for TensorFlow, mixed-precision training, and neural network architectures.
- Chris Lattner discusses upcoming technologies such as kernel fusion, XLA, and MLIR for Swift programmers.
- The course covers matrix multiplication, loss functions, optimizers, the training loop, model inspection, and training Imagenette from scratch in Swift.
- The Data Block API implemented in Swift, using protocols, offers an improved version of the original Python one.
- While there's no official "Deep Learning from the Foundations" course on major educational platforms, the New York University (NYU) Deep Learning course covers advanced topics like supervised, unsupervised, and self-supervised models, graph neural networks, and advanced architectures.
