Summary of study done this week (H29.2.13 ~ 2.24)

Other days: I studied tensorflow at udemy.

[Experience in 3 days] Deep learning learned with TensorFlow x Python 3 https://www.udemy.com/tensorflow/learn/v4/overview

I had a bit of a bite on pyhton so I thought it would be easy, but I could understand how to do it, but the content was refreshing. To briefly explain, it was an explanation of the TensorFlow tutorial and an application problem. The purpose was to implement it without worrying about the content of the code. For the time being, I did everything from implementing TensorFlow to assuming images. First, the tutorial MNIST For ML Beginners https://www.tensorflow.org/get_started/mnist/beginners This is to calculate the probability of hitting a number from handwritten characters. next Deep MNIST for Experts https://www.tensorflow.org/get_started/mnist/pros Here, I entered various codes to increase the probability and popped out more accurate values than MNIST for ML Beginners.

Finally Image Recognition https://www.tensorflow.org/tutorials/image_recognition This is to implement a certain image recognition program in advance and go to see the image prepared by yourself.

Impressions

I was a complete beginner, but the explanation was very easy to understand. There were many things I didn't understand, but the first one was perfect for beginners to learn image recognition and TensorFlow. I just wondered if I needed mathematical knowledge such as matrices and probabilities and statistics to learn more about this.

What i learned

1. How to enter the code in the browser with Tensorflow from Anaconda in the terminal
2. Overview of image recognition (deep learning?)

I don't know here and

1. There are various anaconda, python, jupyter, terminal, but why do you need them?
2. High school mathematics mainly matrix, probability

2/18 ~ 2/20: I couldn't study at all on my employee trip.