How to Get Started with Deep Learning Using Free Resources: A Practical Guide

Deep learning, as a core component of the field of artificial intelligence, is changing our lives and work at an unprecedented rate. From self-driving cars to medical diagnostics to natural language processing, deep learning applications are everywhere. However, for beginners, the theoretical knowledge and practical operation of deep learning can seem a bit daunting. Fortunately, there are a wealth of free resources on the internet that can help us get started easily. This article will be based on discussions on X/Twitter to compile a practical guide to deep learning for beginners, helping you to gradually master the core concepts and skills of deep learning from scratch.

1. Understand the Basics of Deep Learning

Before diving into practice, it is crucial to understand the basic concepts of deep learning. As @@techhybrindia points out, AI is not just about data and algorithms, it also requires powerful computing power. Deep learning models require a lot of GPU or TPU resources, as well as massive amounts of memory and high-speed computing power to train. Therefore, understanding these hardware fundamentals is essential to understanding the scale and complexity of deep learning.

Key Concepts:

• Neural Networks: The foundation of deep learning, mimicking the way human brain neurons connect.
• Depth: Refers to the number of layers in a neural network. The more layers, the more complex the features the model can learn.
• Backpropagation: The core algorithm for training neural networks, used to update the weights in the network.
• Activation Functions: Introduce non-linearity, allowing neural networks to learn complex patterns. Examples include ReLU, Sigmoid, Tanh, etc.
• Loss Functions: Measure the difference between the model's predictions and the actual results, used to optimize the model parameters. Examples include Mean Squared Error (MSE), Cross-Entropy Loss, etc.
• Optimizers: Used to update model parameters and reduce the value of the loss function. Examples include Gradient Descent, Adam, SGD, etc.

Free Learning Resources:

• Books:

  • @@khushabu_27, @@swapnakpanda, @@Shruti_0810 shared free AI & ML books provided by MIT, among which "Understanding Deep Learning" is a very good introductory reading.
    • Understanding Deep Learning: This book introduces all aspects of deep learning in a simple and easy-to-understand way, covering everything from basic concepts to advanced techniques.
    • Foundations of Machine Learning: This book covers the basic theory of machine learning, which is very helpful for understanding the principles of deep learning.
  • @@KirkDBorne recommended "Why Machines Learn — The Elegant Math Behind Modern AI" and "Deep Learning Foundations and Concepts", which can help you understand deep learning from a mathematical perspective.

• Online Courses:

  • @@shamimai1 recommended free courses provided by Google, such as "Understanding machine learning" and "Introduction to Large Language Models", which can help you quickly understand the basic concepts of deep learning and LLM.
  • @@mehmetsongur_ shared MIT's Deep Learning course videos, which can be watched on Youtube. MIT Deep Learning Course## 2. Setting Up a Deep Learning Environment

To practice deep learning, you first need to set up a suitable development environment. Commonly used deep learning frameworks include TensorFlow and PyTorch.

Steps:

1. Install Python: Deep learning is mainly developed using the Python language. It is recommended to install Python 3.6 or later.
2. Install TensorFlow or PyTorch:
   • TensorFlow:

     pip install tensorflow
     # If your machine has an NVIDIA GPU and CUDA and cuDNN are already installed, you can install the GPU version of TensorFlow
     # pip install tensorflow-gpu
     

   • PyTorch:

     # Choose the appropriate installation command according to your operating system and CUDA version, for example:
     pip install torch torchvision torchaudio
     # It is recommended to visit the PyTorch official website (https://pytorch.org/) to get the latest installation command
     

3. Install other necessary libraries: Such as NumPy, Pandas, Matplotlib, etc.

   pip install numpy pandas matplotlib scikit-learn
   

4. Use Jupyter Notebook or Google Colab: Jupyter Notebook provides an interactive programming environment, which is very suitable for deep learning experiments and learning. Google Colab provides free GPU resources, allowing you to perform deep learning training in the cloud.

3. Hands-on Practice: Building Your First Deep Learning Model

Theoretical learning is important, but hands-on practice is even more important. Here is a simple example of building a deep learning model for image classification using Keras (TensorFlow's high-level API):

Steps:1. Import necessary libraries:

```python
import tensorflow as tf
from tensorflow import keras
from tensorflow import layers
import matplotlib.pyplot as plt
```

2. Load the dataset: Use the Keras built-in MNIST dataset (handwritten digit images). python (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() 3. Preprocess the data: Normalize the image data to between 0-1. python x_train = x_train.astype("float32") / 255.0 x_test = x_test.astype("float32") / 255.0 4. Build the model: Use the Keras Sequential API to build a simple CNN model. python model = keras.Sequential( [ keras.Input(shape=(28, 28, 1)), layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dropout(0.5), layers.Dense(10, activation="softmax"), ] ) model.summary() # Print the model structure 5. Compile the model: Configure the optimizer, loss function, and evaluation metrics. python model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) 6. Train the model: python batch_size = 128 epochs = 10 model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) 7. Evaluate the model: python score = model.evaluate(x_test, y_test, verbose=0) print("Test loss:", score[0]) print("Test accuracy:", score[1]) 8. Display Results python # Visualize some prediction results of the test set predictions = model.predict(x_test[:10]) predicted_labels = [tf.argmax(prediction).numpy() for prediction in predictions] plt.figure(figsize=(15, 5)) for i in range(10): plt.subplot(1, 10, i+1) plt.imshow(x_test[i], cmap='gray') plt.title(f"Predicted: {predicted_labels[i]}") plt.axis('off') plt.show() ```

4. In-depth Learning: Exploring Advanced Topics

Once you have mastered the basics of deep learning, you can start exploring some advanced topics, such as:

• Convolutional Neural Networks (CNNs): Used for image processing and computer vision.
• Recurrent Neural Networks (RNNs): Used for processing sequence data, such as text and time series.
• Long Short-Term Memory Networks (LSTMs) and GRUs: Improved RNN structures that can better handle long-term dependencies.
• Generative Adversarial Networks (GANs): Used to generate new data, such as images, audio, and text.
• Transformer Models: Used for natural language processing, such as BERT, GPT, etc.

Free Learning Resources:

• Paper Reading: Read the latest deep learning papers to learn about the latest research progress. You can use search engines such as Google Scholar to find papers.
• Blogs and Tutorials: There are many high-quality deep learning blogs and tutorials, such as the TensorFlow official website, PyTorch official website, Machine Learning Algorithm Engineer, etc.
• Open Source Projects: Read and participate in open source deep learning projects, such as TensorFlow Models, PyTorch Examples, etc.
• Transfer Learning: As @@DSWithDennis pointed out, transfer learning can accelerate the training of deep learning models. You can use pre-trained models, such as ResNet, VGG, etc., and fine-tune them to adapt to your specific task.

5. Precautions and Tips

• Persist in Practice: Deep learning is a very practical subject, and you can only truly master it through continuous practice.
• Make Good Use of Debugging Tools: As @@humble_ulzzang mentioned, learning from debugging code can be more effective than learning directly.
• Pay Attention to the Latest Developments: The field of deep learning is developing rapidly, so you should constantly pay attention to the latest research progress.
• Participate in the Community: Join the deep learning community and exchange experiences and knowledge with other learners. For example, TensorFlow Forum, PyTorch Discuss, etc.
• Pay Attention to Ethics: When conducting deep learning research and applications, pay attention to related ethical issues, such as data privacy, algorithm fairness, etc.

SummaryDeep learning is a field full of opportunities and challenges. By leveraging free resources, setting up a suitable development environment, and persisting in practice, you can master the core concepts and skills of deep learning and apply them to real-world problems. I hope this article can help you get started with deep learning smoothly and go further and further on the road of artificial intelligence!