For a basic introduction to deep learning, please give a learning outline

化羽2015

For a basic introduction to deep learning, please give a learning outline [Copy link]

 

For a basic introduction to deep learning, please give a learning outline

tyloo820

When you first learn deep learning, it is recommended to follow the following learning outline:

Phase 1: Basics

1. Python Programming Basics :

   • Learn Python's basic syntax and common libraries, such as NumPy, Pandas, and Matplotlib.

2. Machine Learning Basics :

   • Understand the basic concepts, main tasks and common algorithms of machine learning, such as supervised learning, unsupervised learning, reinforcement learning, etc.

3. Deep Learning Basics :

   • Understand the basic principles, common models and algorithms of deep learning, such as neural networks, convolutional neural networks, recurrent neural networks, etc.

Phase 2: Deep Learning Models and Algorithms

1. Neural Network Model :

   • Learn the structure, training and optimization methods of neural networks, such as fully connected neural networks, multi-layer perceptrons, etc.

2. Convolutional Neural Networks (CNN) :

   • Understand the principles and applications of CNN, including image classification, object detection, and image segmentation.

3. Recurrent Neural Networks (RNNs) :

   • Understand the principles and applications of RNN, including sequence generation, language modeling, and time series prediction.

Phase 3: Deep Learning Tools and Frameworks

1. TensorFlow :

   • Learn to use TensorFlow to build, train, and evaluate deep learning models, and master the basic operations and advanced functions of TensorFlow.

2. PyTorch :

   • Learn to use PyTorch to implement deep learning models, and master PyTorch's tensor operations, automatic differentiation, and model building techniques.

Phase 4: Practical Projects

1. Select Project :

   • Choose a project related to deep learning, such as image classification, text classification, speech recognition, etc.

2. Project Practice :

   • Design and implement the selected project, including steps such as data preprocessing, model building, model training, and model evaluation.

Phase 5: Advanced Learning and Application

1. Model optimization and parameter adjustment :

   • Learn model optimization methods and parameter tuning techniques, such as learning rate adjustment, regularization, batch normalization, etc.

2. Transfer learning and model distillation :

   • Understand the principles and applications of transfer learning and model distillation, and master the application skills in practical projects.

Through the above study outline, you can systematically learn and master the basic knowledge and application skills of deep learning, laying a solid foundation for engaging in related projects and further studies.

很简单

The following is a study outline for a basic introduction to deep learning:

1. Basic concepts and principles :

   • Understand the basic concepts and principles of deep learning, including neural networks, forward propagation, back propagation, etc.
   • Master the basic terminology of deep learning, such as neurons, weights, biases, etc.

2. Neural network structure :

   • Learn common neural network structures, such as multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), etc.
   • Understand the hierarchical structure and parameter composition of neural networks, such as input layer, hidden layer, output layer, etc.

3. Activation function :

   • Understand common activation functions, such as Sigmoid, ReLU, Tanh, etc., as well as their functions and characteristics.
   • Master the application scenarios and selection methods of activation functions in neural networks.

4. optimization :

   • Learn common optimization algorithms, such as gradient descent, stochastic gradient descent, Adam, etc.
   • Understand the principles and parameter tuning techniques of optimization algorithms, as well as their applications in neural network training.

5. Deep Learning Frameworks :

   • Master common deep learning frameworks, such as TensorFlow, PyTorch, etc.
   • Learn to build, train, and deploy neural network models using deep learning frameworks.

6. Practical projects :

   • Participate in practical projects of deep learning, such as image classification, text classification, etc.
   • Apply the knowledge learned to solve practical problems and perform model tuning and performance evaluation.

Through the above learning outline, you can systematically learn the basic concepts, principles, and techniques of deep learning, and improve your practical ability and application level through practical projects.

qq8174350

When you, as an electronic engineer, want to get started with deep learning and machine learning, here is a learning outline to help you learn and master step by step:

1. Mathematical foundation

• Review the basics of mathematics such as linear algebra, calculus and probability theory, including vectors, matrices, derivatives, gradients, probability distributions, etc.
• Learn how mathematics is used in machine learning and deep learning, such as optimization algorithms, model evaluation, etc.

2. Python Programming Basics

• Learn Python's basic syntax and data structures.
• Familiar with Python's scientific computing libraries, such as NumPy, Pandas, Matplotlib, etc.

3. Machine Learning Basics

• Understand the basic concepts and main tasks of machine learning, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
• Learn common machine learning algorithms such as linear regression, logistic regression, decision trees, support vector machines, etc.

4. Deep Learning Basics

• Understand the basic principles and development history of deep learning, including neural network structure, forward propagation, back propagation, etc.
• Learn the model structures and algorithms commonly used in deep learning, such as multi-layer perceptron, convolutional neural network, recurrent neural network, etc.

5. Data processing and feature engineering

• Learn how to process and prepare data, including data cleaning, feature selection, feature transformation, etc.
• Master common feature engineering techniques, such as standardization, normalization, one-hot encoding, etc.

6. Model training and evaluation

• Learn how to train machine learning and deep learning models, including choosing appropriate loss functions, optimization algorithms, and parameter tuning techniques.
• Master the methods and indicators of model evaluation, such as accuracy, precision, recall, F1-score, etc.

7. Practical Projects

• Complete some practical machine learning and deep learning projects, such as house price prediction, handwritten digit recognition, image classification, etc.
• Through practical projects, students can deepen their understanding and mastery of machine learning and deep learning algorithms, and enhance their practical application capabilities.

8. Continuous learning and practice

• Both deep learning technology and machine learning technology are constantly evolving and require continuous learning and practice.
• Pay attention to the latest research results, technological advances and open source projects in related fields, and continuously improve your skills and level.

Through this study outline, you can systematically learn and master the basic knowledge and skills of machine learning and deep learning, laying a solid foundation for further in-depth research and application. I wish you good luck in your study!