Deep Learning Self-Study Resources Mega-Pack: Free Books, Courses, and Practical Tools

In recent years, Deep Learning, as an important branch of artificial intelligence, has achieved remarkable success. Whether it's image recognition, natural language processing, or reinforcement learning, deep learning has demonstrated powerful capabilities in various fields. However, for developers who want to get started with or delve deeper into deep learning, the massive amount of information and complex concepts can often be overwhelming.

Based on popular discussions on X/Twitter, this article selects a series of free deep learning self-study resources, including books, courses, and practical tools, aiming to provide readers with a clear and practical learning path to help everyone quickly master the core skills of deep learning.

I. Free Book Resources: Theoretical Foundation and Practical Guidance

The theoretical foundation is the cornerstone of deep learning. Mastering solid mathematical and machine learning foundations is crucial for understanding and applying deep learning. The following recommends several free books covering various aspects from basic theory to practical application:

1. Understanding Machine Learning

• Link: https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
• Features: This book delves into the theoretical foundations of machine learning, covering important concepts such as generalization theory, PAC learning, VC dimension, etc. It is suitable not only as a textbook but also for readers interested in machine learning theory.
• Recommended reason: Emphasizes both theory and algorithms, helping to build a solid machine learning knowledge system.

2. Mathematics for Machine Learning

• Link: https://mml-book.github.io/book/mml-book.pdf
• Features: This book systematically introduces the mathematical knowledge required for machine learning, including linear algebra, calculus, probability theory, etc. It emphasizes the application of mathematical concepts in machine learning and helps readers understand through numerous examples.
• Recommended reason: For readers with weak mathematical foundations, this is a rare introductory textbook that can effectively make up for the lack of mathematical knowledge.

3. Mathematical Analysis of ML Algorithms

• Link: https://tongzhang-ml.org/lt-book/lt-book.pdf
• Features: In-depth analysis of the mathematical principles of various machine learning algorithms, covering important content such as convex optimization and stochastic gradient descent. This book is suitable for readers with a certain mathematical foundation who want to deeply understand the principles of algorithms.
• Recommended reason: Through mathematical analysis, you can more thoroughly understand the essence of algorithms, laying the foundation for algorithm optimization and innovation.

4. Understanding Deep Learning

• Link: https://udlbook.github.io/udlbook/
• Features: This book is written by experts in the field of deep learning and systematically introduces the basic concepts, models, and algorithms of deep learning. It covers various common deep learning models, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer, etc.
• Recommended reason: Comprehensive, easy to understand, and suitable as an introductory textbook for deep learning.

5. Foundations of Machine Learning

• Link: https://cs.nyu.edu/~mohri/mlbook/

• Features: Covers the basic knowledge of machine learning, including supervised learning, unsupervised learning, reinforcement learning, etc. This book focuses on introducing the principles and applications of various machine learning algorithms.

• Recommended reason: Broad in content, suitable for establishing a complete machine learning knowledge system.6. Machine Learning Algorithms (Algorithms for ML)

• Link: https://algorithmsbook.com

• Features: Focuses on explaining machine learning algorithms, from basic linear regression to advanced deep learning algorithms, with detailed introductions and code examples.

• Recommended reason: Emphasizes practice and is suitable for learning algorithms through code.

7. Reinforcement Learning

• Link: https://andrew.cmu.edu/course/10-703/textbook/BartoSutton.pdf
• Features: A classic textbook in the field of reinforcement learning, systematically introducing the basic concepts, algorithms, and applications of reinforcement learning.
• Recommended reason: The content is authoritative and is an essential book for learning reinforcement learning.

2. Free Deep Learning Courses from Stanford University: From Beginner to Advanced

Stanford University is renowned in the field of artificial intelligence, and its free online courses cover all aspects of deep learning. Here are some recommended courses:

• CS221 - Artificial Intelligence: An overview course of artificial intelligence, laying the foundation for learning deep learning.
• CS229 - Machine Learning: A classic machine learning course covering the principles and applications of various machine learning algorithms.
• CS230 - Deep Learning: An introductory deep learning course that introduces the basic concepts, models, and algorithms of deep learning.
• CS234 - Reinforcement Learning: A reinforcement learning course covering the basic concepts, algorithms, and applications of reinforcement learning.
• CS231N - Convolutional Neural Networks for Visual Recognition: Focuses on convolutional neural networks and their applications in computer vision.
• CS336 - LLM from Scratch: Introduces the construction and training process of large language models.

Learning Suggestions:

1. Choose the right course: Choose the right course based on your foundation and interests.
2. Complete assignments carefully: Course assignments are an important part of consolidating knowledge, so be sure to complete them carefully.
3. Actively participate in discussions: Actively participate in course discussions and exchange experiences and insights with other learners.

3. Practical Tools and Techniques: Improving Learning Efficiency

In addition to theoretical learning and course learning, some practical tools and techniques can also help you learn deep learning more efficiently.

1. Use Google Colab or Kaggle Kernel:

• Google Colab and Kaggle Kernel provide free GPU resources, making it easy to run deep learning code.
• They also support online editing and collaboration, making it easy to learn together with others.

Sample Code (Training a simple CNN using TensorFlow on Google Colab):

import tensorflow as tf

# Define the model
model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
  tf.keras.layers.MaxPooling2D((2, 2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation='softmax')
])
``````python
# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0

# Train the model
model.fit(x_train, y_train, epochs=2)

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
print('Loss: %.2f' % loss)
print('Accuracy: %.2f' % accuracy)

2. Utilize Visualization Tools:

• Visualization tools like TensorBoard and Visdom can help you better understand the structure and training process of your models.
• By visualizing loss functions, activation values, etc., you can gain a more intuitive understanding of the model's behavior.

3. Participate in Open Source Projects:

• Participating in open source projects is an effective way to improve your deep learning skills.
• By reading and modifying open source code, you can learn excellent programming practices and design ideas.

4. Continuous Learning and Practice:

• Deep learning is a rapidly evolving field, requiring continuous learning of new knowledge and technologies.
• Through practical projects, applying what you've learned to solve real-world problems is the only way to truly master deep learning.

5. Learn the Transformer Architecture in NLP:

• Study Jay Alammar’s guide in depth (specific content needs to be searched in the corresponding article).
• Understand the concepts of FFN (Feed Forward Network), LayerNorm (Layer Normalization), and Residuals (residual connections).
• Try implementing a complete Transformer Encoder-Decoder block from scratch.

6. Learn Applications in Stock Trading (for reference only, at your own risk):

• Understand how to use deep learning for stock selection.
• Note: Such applications involve financial risks and require careful evaluation and operation.

7. Follow Industry Trends:

• Pay attention to the sharing of institutions such as DeepLearningAI to understand the latest developments in the field of deep learning.

IV. Overcoming Challenges in Learning

Learning deep learning may encounter some challenges, such as:

• Complex Concepts: Deep learning involves many complex mathematical and algorithmic concepts.
• Resource Scarcity: Lack of computing resources and data resources.
• Lack of Guidance: Lack of professional guidance and support.

To overcome these challenges, the following measures can be taken:

• Break Down Learning Goals: Break down complex learning goals into small, manageable goals.
• Find Free Resources: Utilize the free books, courses, and tools recommended in this article.
• Join a Learning Community: Join a deep learning learning community to exchange experiences and seek help from other learners.

As DeepLearningAI said on X/Twitter: “Every AI expert once Googled simple questions. Every beginner felt lost. If you’re learning and trying, you belong in the AI community.” Remember, every AI expert was once a beginner and felt lost. As long as you persist in learning and practice, you will definitely overcome difficulties and become a qualified deep learning engineer.In conclusion, this article provides a wealth of free deep learning self-study resources, including books, courses, and practical tools. I hope these resources can help you better get started and delve into deep learning, and achieve success in the field of artificial intelligence. Please remember that learning is a continuous process that requires constant effort and practice. I wish you a smooth learning journey!