How to get started with deep learning from scratch

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How to get started with deep learning from scratch [Copy link]

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Even if you are starting from scratch, you can get started by following these steps:

Understand the basic concepts of deep learning :
- Understand that deep learning is an artificial intelligence technology that learns and predicts data by simulating the structure and function of human neural networks.
- Understand the basic components of neural networks, such as neurons, layers, activation functions, etc., as well as the basic principles of forward propagation and backpropagation.
Learn math and programming basics :
- Review basic linear algebra, calculus, and probability and statistics, which are the basis for understanding the principles of deep learning.
- Master a programming language, such as Python, and common scientific computing libraries, such as NumPy, Pandas, and Matplotlib.
Learn Deep Learning Frameworks :
- Choose a popular deep learning framework such as TensorFlow, PyTorch, or Keras and learn its basic usage and API.
- You can get started quickly by reading the official documentation, tutorials, and online resources.
Take an online course or tutorial :
- Sign up for some free or paid online courses, such as the Deep Learning Specialization on Coursera and the Deep Learning Basics on Udacity. These courses are usually taught by industry experts or academic researchers and are systematic and comprehensive.
- Participate in some training courses or workshops organized by the deep learning community to exchange experiences with other learners and solve problems.
Completed practical projects :
- Choose some simple deep learning projects, such as handwritten digit recognition (MNIST dataset), cat and dog image classification (Kaggle competition), etc., and use existing tutorials and resources to implement them.
- Through hands-on practice, you can deepen your understanding of deep learning principles and frameworks, and improve your programming and debugging skills.
Continuous learning and exploration :
- Read classic books and papers in the field of deep learning in depth to understand the development history and latest progress of deep learning.
- Participate in seminars, lectures and online events in the field of deep learning, communicate with experts and peers in the field, and discuss problems and ideas.
Join the community and team :
- Join online communities and forums related to deep learning, such as GitHub, Stack Overflow, etc., ask questions to others, and share learning experiences and project experiences.
- Participate in some deep learning team projects or open source projects, collaborate with other team members to solve challenges and improve your skills.

Through the above steps, you can gradually get started with deep learning, and continuously improve your abilities in practice to become an excellent deep learning practitioner.

生命之树

As an electronic engineer, you already have a good foundation in mathematics and programming, which is very helpful for learning deep learning. The following is a detailed getting started guide to help you learn deep learning from scratch:

1. Understand the basic concepts

1.1 What is Deep Learning?

Deep learning is a subfield of machine learning that uses multi-layered neural networks to learn features and patterns from data.
Widely used: image recognition, natural language processing, speech recognition, autonomous driving, etc.

1.2 Basic terminology

Neural Network : A network consisting of an input layer, hidden layers, and an output layer.
Activation function : such as ReLU, Sigmoid, Tanh, etc., used to introduce nonlinearity.
Loss function : It is used to measure the difference between the predicted value and the actual value.
Optimizer : Such as SGD, Adam, used to adjust the parameters of the model to minimize the loss function.

2. Learn the basics of mathematics

2.1 Linear Algebra

Vector and matrix operations: addition, multiplication, transpose, inverse matrix, etc.
Recommended resource: Linear Algebra and Its Applications by Gilbert Strang.

2.2 Calculus

Derivatives and Integrals: Understanding Gradient Descent and Backpropagation.
Recommended resource: Calculus by James Stewart.

2.3 Probability and Statistics

Probability distribution, expectation, variance, Bayes' theorem.
推荐资源：《Probability and Statistics for Engineers and Scientists》 by Ronald E. Walpole。

3. Learn programming skills

3.1 Python Programming

Basic syntax: variables, loops, conditional statements, functions.
Data processing: Use NumPy and Pandas for data manipulation.

3.2 Deep Learning Framework

TensorFlow : A deep learning framework developed by Google, suitable for building and training neural networks.
PyTorch : A deep learning framework developed by Facebook that is popular among researchers and developers.
Beginners can choose one of them for in-depth learning.

4. Choose learning resources

4.1 Online Courses

Coursera：
- Andrew Ng的《Deep Learning Specialization》。
edX：
- MIT的《Introduction to Deep Learning》。
Udacity：
- Deep Learning Nanodegree.

4.2 Books

《Deep Learning》 by Ian Goodfellow, Yoshua Bengio, Aaron Courville。
《Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow》 by Aurélien Géron。

5. Practice and Projects

5.1 Basic Project

Image Classification : Train a neural network using the MNIST handwritten digits dataset.
Text Classification : Sentiment analysis using the IMDB movie review dataset.

5.2 Advanced Projects

Object Detection : Train a YOLO or SSD model using the COCO dataset.
Natural Language Processing : Text generation or question answering systems using BERT or GPT.

6. Learn the principles of deep learning

6.1 Neural Network Architecture

Convolutional Neural Network (CNN) : Mainly used for image processing.
Recurrent Neural Network (RNN) : Mainly used for sequence data, such as time series and text data.
Generative Adversarial Networks (GANs) : Used for generating new data, image generation, etc.

6.2 Model Training and Tuning

Overfitting and Underfitting : Understand and solve the overfitting and underfitting problems of the model.
Regularization techniques : such as Dropout, L2 regularization, etc.
Hyperparameter tuning : Use methods such as Grid Search, Random Search, and Bayesian optimization.

7. Participate in the community and continue learning

7.1 Online Forums and Communities

Stack Overflow : Answers to programming and technical questions.
Reddit : r/MachineLearning and r/deeplearning communities.
GitHub : Follow and participate in open source projects, and learn from other people's code and projects.

7.2 Participation in the Contest

Kaggle : Participate in data science and machine learning competitions to solve real-world problems and improve your skills.
DrivenData : A competition project that engages in social and environmental issues.

By following the above steps, you will be able to systematically learn deep learning, from basic concepts to advanced applications. Continuous practice and participation in community exchanges will help you improve your skills faster and become an expert in the field of deep learning.

地天泰

Getting started with deep learning requires step-by-step learning and practice. Here is a guide to getting started from scratch:

1. Master basic mathematics and programming knowledge

Linear algebra : basic concepts such as matrix operations, vectors, and systems of linear equations.
Calculus : basic concepts such as gradient and partial derivative.
Python Programming : Learn the Python language and become familiar with commonly used libraries such as NumPy and Pandas.

2. Learn the basics of deep learning

Basic theory of deep learning : Understand the basic principles of neural networks, forward propagation and back propagation algorithms, etc.
Common model architectures : Understand common deep learning models, such as fully connected neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc.

3. Learn deep learning tools and frameworks

TensorFlow or PyTorch : Choose a mainstream deep learning framework and learn its basic usage and API.
Learning resources : Learn how to use deep learning frameworks through official documentation, tutorials, or online courses.

4. Complete projects and practices

Choose a project : Choose a simple deep learning project such as image classification, text generation, etc.
Hands-on projects : Hands-on projects, from data collection and preprocessing to model building and training, and finally evaluating the performance of the model.

5. In-depth learning and practice

Advanced Theory : Learn advanced theories in the field of deep learning, such as transfer learning, natural language processing, etc.
Participate in competitions and projects : Participate in deep learning competitions or open source projects to collaborate with others and improve your skills.

6. Continue to learn and keep up with the latest developments

Pay attention to the latest developments : Pay attention to the latest research and technological advances in the field of deep learning, and maintain a continuous learning attitude.

Possible learning resources:

书籍：《深度学习》（Ian Goodfellow等著）、《动手学深度学习》（阿斯顿·张等著）等。
Online courses : Deep Learning courses on Coursera and Udacity.
Blogs and Papers : Read blogs and research papers related to deep learning to stay up to date with the latest research trends.

Deep learning is a large and complex field. Getting started may take some time and effort, but through continuous learning and practice, you will gradually master the core concepts and skills of deep learning and become an excellent deep learning practitioner.