1. 3- Activation Functions
1.1. What is Activation Functions?
1.2. Types of Activation Functions:
1.2.1. 1- Binary Step Function
1.2.2. 2- Non-Linear Activation Functions
1.2.2.1. [Advantages,Disadvantages] Sigmoid / Logistic ,...,...,...,...,...,Softmax
1.2.3. 3- Linear Activation Functions
1.2.4. 4- Gradients of Activation Functions
2. 4-Bias Neuron in NN
2.1. How do the neurons work?
2.2. Where are the bias neurons?
2.3. Bias-Variance Tradeoff
2.4. Overfitting and Underfitting
2.4.1. Methods to Avoid Overfitting and Underfitting in NN
3. 5-Hyperparameters
3.1. Model parameters
3.1.1. What is the Difference Between a Model Parameter and a Hyperparameter?
3.2. List of Hyperparameters
3.2.1. Hyperparameters related to NN structure
3.2.1.1. Number of hidden layers,2. Dropout...etc
3.2.2. Hyperparameters related to the training algorithm
3.2.2.1. Learning rate....etc
3.3. Hyperparameter Optimization
4. 7-Regression
4.1. What is Regression Analysis?
4.2. Types of Regression Analysis:
4.2.1. Linear Regression,Logistic Regression....etc
4.3. Should Neural Networks be Used to Run Regression Models?
5. 1-Perceptrons and Multi-Layer Perceptrons
5.1. What Is a Perceptron?
5.2. What Is a Multilayer Perceptron?
5.3. Structure of a Perceptron and steps work:
6. What Is a Neural Network?
6.1. Neural network basics
6.1.1. neurons or perceptrons, layers, weights and activations
6.2. Concepts in NN
6.2.1. Inputs ,Training Set.,Outputs, neuron perceptron,Weight Space,Forward Pass, Error Function, Backpropagation ,bias , Hyperparam
6.3. Artificial Neural Networks (ANN)
6.4. Deep Neural Networks
6.5. 6 Stages of Neural Network Learning
7. 2-Backpropagation
7.1. What and how it works Backpropagation in NN?
7.1.1. The forward pass
7.1.2. Backward Pass
7.2. Why Do We Need Backpropagation in NN?
8. 6-Classification
8.1. What Is Classification in ML and DL?
8.2. Types of Classification Algorithms:
8.2.1. Logistic Regression,Decision Tree,Random Forest,Naive Bayes,k-Nearest Neighbor
8.3. Neural Network Classification
8.3.1. Strengths and Weaknesses: