动手学深度学习V2___截图

Before diving into deep learning research, it’s beneficial to have a solid foundation in several key areas. Here are some topics you should consider mastering:

1. Mathematics: Deep learning relies heavily on mathematical concepts such as linear algebra, calculus, and probability theory1. Understanding these topics will help you grasp the underlying principles of deep learning algorithms and models.

2. Machine Learning: Familiarity with machine learning concepts and techniques is essential as deep learning is a subset of machine learning2. It’s recommended to have a good understanding of supervised and unsupervised learning, model evaluation, and feature engineering2.

3. Python Programming: Python is widely used in the field of deep learning due to its simplicity and extensive libraries such as TensorFlow and PyTorch2. Proficiency in Python programming will enable you to implement and experiment with deep learning models effectively.

4. Neural Networks: Deep learning heavily relies on neural networks, so it’s crucial to have a solid understanding of their architecture, activation functions, and optimization algorithms2. This knowledge will help you design and train effective deep learning models.

5. Data Preprocessing: Deep learning models require clean and well-prepared data for training. Understanding data preprocessing techniques such as normalization, feature scaling, and handling missing values is essential2.

6. Statistics: A good grasp of statistical concepts will help you interpret the results of your deep learning experiments and make informed decisions1. Topics such as hypothesis testing, confidence intervals, and regression analysis are particularly relevant.

7. Research Papers: Reading research papers in the field of deep learning will expose you to the latest advancements and help you understand the state-of-the-art techniques3. It’s important to stay up-to-date with current research trends.

Remember that deep learning is a rapidly evolving field, so continuous learning and staying updated with the latest research are essential for success. Good luck with your deep learning research journey!

以上内容来自New Bing

01 课程安排

02 深度学习介绍

注意课程的时效性。chatgpt现在已经起飞了！

03 安装

04 数据操作 + 数据预处理

注意广播机制！

05 线性代数

09 Softmax

10 多层感知机 + 代码实现

此处hidden layer的层数为多层，先胖后瘦！

ReLU简单易用，故没有特殊用途的话，最常用的激活函数就是ReLU。

11 模型选择 + 过拟合和欠拟合

12 权重衰退

13 丢弃法

14 数值稳定性 + 模型初始化和激活函数

学习深度学习，数学的重要性：
Coding能力类似CPU。coding能力强，解决问题速度快。
数学类似内存。数学不好，能做的任务就少，看不懂公式就没法用。数学好，内存就大，越好越能解决复杂的问题。

如何突破数学能力：上课！线性代数 + 概率论。

16 PyTorch 神经网络基础