Grokking deep learning is a free online course that provides a comprehensive guide to deep learning, covering the basics of neural networks and how to implement them using popular deep learning frameworks.
The course is designed to be self-paced, allowing you to learn at your own speed and on your own schedule. It's a great resource for beginners who want to get started with deep learning.
You can download the Grokking Deep Learning PDF from the official course website, which includes all the course materials, including lecture notes, assignments, and code examples.
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Andrew W. Trask
Andrew W. Trask is the author of the book "Grokking Deep Learning". He's done a fantastic job of breaking down the complex concepts of deep learning into easy-to-understand language, making it perfect for beginners.
The book covers a wide range of topics, from the basics of deep learning to more advanced concepts like convolutional neural networks and recurrent layers. It's written in a way that's engaging and practical, with code examples and snippets that are carefully explained.
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One of the standout features of the book is its focus on building deep neural networks from scratch, using a first principles approach. This means you'll learn how to code and understand the most basic building blocks of ANNs, without relying on pre-made frameworks like TensorFlow or Keras.
The book uses Python and NumPy, which are great tools for building and training artificial neural networks. With "Grokking Deep Learning", you'll learn how to build networks that can understand images, translate text, and even generate poetry.
Here are some of the key topics covered in the book:
- Introducing deep learning: why you should learn it
- Fundamental concepts: how do machines learn?
- Introduction to neural prediction: forward propagation
- Introduction to neural learning: gradient descent
- Learning multiple weights at a time: generalizing gradient descent
- Building your first deep neural network: introduction to backpropagation
- How to picture neural networks: in your head and on paper
- Learning signal and ignoring noise: introduction to regularization and batching
- Modeling probabilities and nonlinearities: activation functions
- Neural learning about edges and corners: intro to convolutional neural networks
- Neural networks that understand language: king – man + woman == ?
- Neural networks that write like Shakespeare: recurrent layers for variable-length data
- Introducing automatic optimization: let's build a deep learning framework
- Learning to write like Shakespeare: long short-term memory
- Deep learning on unseen data: introducing federated learning
- Where to go from here: a brief guide
Andrew W. Trask's book "Grokking Deep Learning" is a must-read for anyone looking to learn about deep learning in a practical and engaging way.
Book Structure
The book "Grokking Deep Learning" is organized in a logical and easy-to-follow manner. The table of contents reveals a clear structure that will guide you through the world of deep learning.
The book starts by introducing the reader to deep learning, explaining why it's worth learning and how machines learn. This sets the foundation for the rest of the book.
The book is divided into 12 chapters, each covering a specific topic in deep learning. Here's a brief overview of what you can expect from each chapter:
- Introducing deep learning: why you should learn it
- Fundamental concepts: how do machines learn?
- Introduction to neural prediction: forward propagation
- Introduction to neural learning: gradient descent
- Learning multiple weights at a time: generalizing gradient descent
- Building your first deep neural network: introduction to backpropagation
- How to picture neural networks: in your head and on paper
- Learning signal and ignoring noise:introduction to regularization and batching
- Modeling probabilities and nonlinearities: activation functions
- Neural learning about edges and corners: intro to convolutional neural networks
- Neural networks that understand language: king – man + woman == ?
- Neural networks that write like Shakespeare: recurrent layers for variable-length data
The book also includes code examples and snippets that are carefully explained, so you won't be left wondering what a particular line of code does.
Deep Learning Topics
Deep Learning Topics can be overwhelming, but understanding the basics can make all the difference. One of the most fundamental concepts in Deep Learning is Backpropagation, which is a method used to update the model's parameters during training.
Backpropagation is an optimization algorithm that helps the model learn from its mistakes. It's a crucial component of many Deep Learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
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In the context of Deep Learning, Autoencoders are a type of Neural Network that can be used for dimensionality reduction and generative modeling. They work by compressing the input data into a lower-dimensional representation and then reconstructing it.
Generative Adversarial Networks (GANs) are another type of Deep Learning model that can be used for generative modeling. They consist of two Neural Networks, a Generator and a Discriminator, that compete with each other to generate new, synthetic data that resembles the real data.
Transfer Learning is a technique used in Deep Learning where a pre-trained model is fine-tuned for a specific task. This can save a lot of time and resources, as the model has already learned to recognize certain patterns in the data.
For another approach, see: Is Generative Ai Deep Learning
Technologies and Tools
Grokking Deep Learning requires the right set of technologies and tools to get started.
TensorFlow and PyTorch are two popular deep learning frameworks that can be used for building and training neural networks.
Keras is a high-level neural networks API that can run on top of TensorFlow or PyTorch, making it a great choice for beginners.
The Grokking Deep Learning course uses a combination of Jupyter Notebooks and Python code to provide an interactive learning experience.
Deep learning models can be trained and tested using datasets such as MNIST and CIFAR-10, which are included in the course materials.
The course also covers tools like NumPy and Pandas for data manipulation and analysis.
Sources
- https://www.everand.com/book/511816875/Grokking-Deep-Learning
- https://datatalks.club/books/20210809-grokking-machine-learning.html
- https://medium.com/@malshikhareaditya/grokking-machine-learning-97865bdcec95
- https://howtolearnmachinelearning.com/books/machine-learning-books/grokking-deep-learning/
- https://www.oreilly.com/library/view/grokking-deep-learning/9781617293702/
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