For an introduction to deep learning computing basics, please give a study outline

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For an introduction to deep learning computing basics, please give a study outline [Copy link]

 

For an introduction to deep learning computing basics, please give a study outline

逍遥游侠

Here is a study outline of the computational foundations of deep learning to help you get started:

Phase 1: Python Programming Basics

1. Python Basics :

   • Learn Python's basic syntax, data types, control flow, and other basic knowledge.
   • Familiar with common Python libraries and tools, such as NumPy, Pandas, Matplotlib, etc.

2. NumPy Library :

   • Learn to use NumPy for numerical calculations and master operations such as array creation, indexing, and slicing.
   • Understand the concepts of broadcasting and vectorized operations in NumPy.

3. Matplotlib Library :

   • Learn to use Matplotlib for data visualization and master common drawing functions and parameter settings.

Phase 2: Linear Algebra and Calculus Basics

1. Linear Algebra :

   • Review or learn the basic concepts of linear algebra, such as vectors, matrices, systems of linear equations, etc.
   • Be familiar with important concepts such as matrix operations, eigenvalues and eigenvectors.

2. calculus :

   • Review or learn basic concepts of calculus such as derivatives, integrals, limits, etc.
   • Understand the importance and applications of gradients (derivatives) in deep learning.

Phase 3: Deep Learning Computational Foundations

1. Neural Network Basics :

   • Learn the basic structure and working principles of artificial neural networks, including perceptrons, multi-layer perceptrons, etc.
   • Understand the forward propagation and backpropagation algorithms of neural networks.

2. Deep Learning Frameworks :

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

3. optimization :

   • Learn common optimization algorithms, such as stochastic gradient descent (SGD), momentum, Adam, etc.
   • Understand the principles of optimization algorithms and parameter tuning techniques.

Phase 4: Practical projects and advanced learning

1. Project Practice :

   • Choose a deep learning project like image classification, text classification, object detection, etc.
   • Design and implement projects, including steps such as data preparation, model building, training, and evaluation.

2. Advanced Learning :

   • Dive into advanced content of deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), attention mechanisms, etc.
   • Explore the applications of deep learning in different fields, such as natural language processing, computer vision, reinforcement learning, etc.

Through the above learning outline, you can gradually master the basics of deep learning computing and lay a solid foundation for further in-depth learning and application.

见贤思奇异果

The following is a study outline for getting started with deep learning computing basics:

1. Introduction to Deep Learning Computing :

   • Basic concepts and background of deep learning computing.
   • Applications of deep learning computing in electronics and other fields.

2. Tensors and tensor operations :

   • Definition and basic properties of tensors.
   • Tensor operations such as addition, multiplication, transpose, etc.

3. Matrix operations in deep learning :

   • Principles and applications of matrix multiplication.
   • Basic operations such as matrix inverse, transpose and determinant.

4. Automatic differentiation :

   • The concepts and principles of automatic differentiation.
   • How to use automatic differentiation to calculate gradients in deep learning.

5. Hardware Acceleration :

   • The role of hardware accelerators such as GPU and TPU in deep learning computing.
   • How to use hardware accelerators to speed up the training and inference of deep learning models.

6. Computational optimization of deep learning frameworks :

   • Computational optimization methods for deep learning frameworks such as TensorFlow and PyTorch.
   • How to use deep learning frameworks for efficient computing.

7. Practical projects :

   • Complete practical projects based on deep learning calculations, such as image classification, object detection, etc.
   • Learn how to optimize the computational performance of deep learning models.

The above learning outline can help beginners build the basic knowledge and skills of deep learning computing, laying the foundation for further in-depth learning and application of deep learning.

王小兵

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

1. Mathematical foundation

• Review basic math including linear algebra, calculus, and probability theory.
• Familiarity with mathematical concepts such as vectors, matrices, derivatives, gradients, etc. plays an important role in deep learning.

2. Python Programming Basics

• Learn Python's basic syntax and data structures.
• Master Python's commonly used libraries in deep learning, such as NumPy, Pandas, etc.

3. Deep Learning Basics

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

4. Deep Learning Computing Framework

• Learn to use deep learning computing frameworks such as TensorFlow, PyTorch, Keras, etc.
• Learn how to build, train, and evaluate deep learning models using these frameworks.

5. Hardware Acceleration

• Learn how to accelerate deep learning models on different hardware, such as GPU, TPU, etc.
• Learn how to leverage hardware acceleration to increase the training and inference speed of deep learning models.

6. Distributed Computing

• Learn distributed training methods for deep learning models to improve training efficiency and scale.
• Master distributed computing frameworks, such as TensorFlow Distribute, PyTorch Distributed, etc.

7. Practical Projects

• Complete some practical deep learning projects such as image classification, object detection, text generation, etc.
• Apply what you have learned in practical projects to deepen your understanding and mastery of the basics of deep learning computing.

8. Continuous learning and practice

• The computing foundations of deep learning involve multiple fields and require continuous learning and practice.
• Pay attention to the latest developments and research results in the field of deep learning, and continuously improve your skills and level.

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