AI Course - Week2

# DAY8-10: Neural networks and deep learning ideas

# 1. Specific requirements

To watch the video, ask:

1. The principles of learning perceptron and multi-layer perceptron

   • Video link:Multilayer perceptron + code implementation - hands-on learning of deep learningopen in new window

2. Linear regression, logistic regression, softmax regression

   • Video link:Linear regression + basic optimization algorithmopen in new window

3. Learn softmax regression, loss functions, and image classification datasets.

   • Video link:softmax regression + loss function + image classification data setopen in new window

4. Deep neural network, forward propagation, back propagation, activation function, loss function, overfitting, underfitting, regularization constraints, parameters, hyperparameters, data set division (can be combined with notes)

   • Video link:Andrew Ng’s deep learning course videoopen in new window
   • Multi-layer neural network representation P25-P35
   • Deep Neural Network P36-P43
   • Improving deep neural networks, hyperparameter tuning, regularization and optimization P47-P80

# 2. Display of learning results

Select some knowledge points for targeted exercises

1. Kaggle neural network exercises:Kaggle Neural Network Exerciseopen in new window

# 3. Reference materials

1. Zhou Zhihua "Machine Learning"

2. Andrew Ng’s courses:

   • Andrew Ng Machine Learning Courseopen in new window
   • Ng Enda Machinery Other Coursesopen in new window
   • Andrew Ng Videoopen in new window

3. Shanghai Jiao Tong University Coursesopen in new window

4. Introduction to iEDA Artificial Intelligence Basicsopen in new window

# DAY11-14: Programming framework learning

# 1. Specific requirements

1. inHow to eat Pytorch in 20 daysopen in new windowChoose to download "Eat That Pytorch in 20 Days" or "Eat That TensorFlow2 in 30 Days" from the website github
2. Choose any 2-3 code models to learn, try to adjust parameters for learning or change the model

# 2. Display of learning results

Show the parameter adjustment process or model replacement process, and try to train a better model

# 3. Reference materials

1. Getting started with PyTorchopen in new window
2. Getting started with TensorFlowopen in new window
3. PyTorch exercisesopen in new window
4. TensorFlow exercisesopen in new window