Interview Reflection and Deep Learning Study Plan (面试的反思和深度学习计划)

经历了几次面试后，我深切感受到了我对于深度学习知识的不扎实。我觉得我能在候选人中胜出的部分是我的解决问题的能力和我的与人相处的方式。可是在面试的部分，我对于一些基础知识常常没法很好给出回答，我能很明显地感到面试官的失望😥。当我看到很多解决问题能力很差的同学都有了offer，我会很难过，因为我觉得我可以做得比他们更好。当然最重要的是我快三月份才开始找实习，接到的面试太少，失败的经验不太够。但是，我很庆幸现在我很清楚我要做什么，我目前也有这几年来所不具备的执行力和专注度，我觉得我一定可以做好的。

After a few interviews, I deeply felt my lack of solid knowledge about deep learning. Part of what I feel like I’m going to outshine the candidates is my problem-solving skills and my way of getting along with people. However, in the interview part, I often can’t give a good answer to some basic knowledge, and I can obviously feel the disappointment of the interviewer😥. When I see many students with poor problem-solving skills have offers, I feel sad because I think I can do better than them. Of course, the most important thing is that I only started looking for an internship in March, and I received too few interviews and not enough experience of failure. However, I am very fortunate that now I am very clear about what I want to do, and I also have the execution and focus that I have not had in the past few years. I think I can definitely do it well.

• 我要把吴恩达的Deep Learning最近给刷完
  • 在这个页面记录过程，并且会把证书放上来！
• 我要多准备机器学习和深度学习面试的问题
• 我要不断做有价值的项目，并替换掉简历里一些没意思的
  • 之后把在上交做的事情合并一下，不用占用太多行
• I want to finish Andrew Ng’s Deep Learning recently
  • Record the process on this page, and the certificate will be posted!
• I need to prepare more questions for machine learning and deep learning interviews
• I’m going to keep doing valuable projects and replace some boring ones in my resume
  • Afterwards, merge what you have done in the handover without taking up too many lines

吴恩达的Deep Learning 内容，包含5门课，预计要5个月完成，看看我多久能完成吧！我期待是15天以内。(从3月27开始)

Andrew Ng’s Deep Learning includes 5 courses, is expected to take 5 months to complete, let’s see how long I can finish it! I expect it to be within 15 days. (from 3.27)

• C1 Neural Networks and Deep Learning
  • W1 Introduction to Deep Learning 3.27
  • W2 Deep Neural Networks 3.27
  • W3 Shallow Neural Networks 3.29
  • W4 Deep Neural Networks 3.29
• C2 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
  • W1 Practical Aspects of Deep Learning
  • W2 Optimization Algorithms
  • W3 Hyperparameter Tuning, Batch Normalization and Programming Frameworks
• C3 Structuring Machine Learning Projects
  • W1 ML Strategy (1)
  • W2 ML Strategy (2)
• C4 Convolutional Neural Networks
  • W1 Foundations of Convolutional Neural Networks
  • W2 Deep Convolutional Models: Case Studies
  • W3 Object Detection
  • W4 Special Applications: Face recognition & Neural Style Transfer
• C5 Sequence Models
  • W1 Recurrent Neural Networks
  • W2 Natural Language Processing & Word Embeddings
  • W3 Sequence Models & Attention Mechanism
  • W4 Transformer Network

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