Bolded all sections

README.md

@@ -39,15 +39,15 @@ The book covers foundational concepts of machine learning. It also introduces th
 
 ### Chapter 1: Machine Learning Basics
 
- 1.1 Machine Learning
+ 1.1 **Machine Learning**
 
   1.1.1 Experience
 
   1.1.2 Task
 
   1.1.3 Performance Measure
 
- 1.2 Machine Learning Paradigms
+ 1.2 **Machine Learning Paradigms**
 
   1.2.1 Supervised Learning
 
@@ -57,9 +57,9 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   1.2.4 Reinforcement Learning 
 
- 1.3 Maximum Likelihood Estimation
+ 1.3 **Maximum Likelihood Estimation**
 
- 1.4 Elements of a Machine Learning Algorithm
+ 1.4 **Elements of a Machine Learning Algorithm**
 
   1.4.1 Data
 
@@ -71,21 +71,21 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   1.4.5 Regularizer
 
- 1.5 Bias and Variance Trade-Off
+ 1.5 **Bias and Variance Trade-Off**
 
- 1.6 Why Deep Learning?
+ 1.6 **Why Deep Learning?**
 
   1.6.1 Curse of Dimensionality
 
   1.6.2 Invalid Smoothness Assumption
 
   1.6.3 Deep Learning Advantages
 
- 1.7 Summary
+ 1.7 **Summary**
 
 ### Chapter 2: Essential Math
 
- 2.1 Linear Algebra
+ 2.1 **Linear Algebra**
 
   2.1.1 Matrices and Vectors
 
@@ -95,7 +95,7 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   2.1.4 Norms
 
- 2.2 Probability Theory
+ 2.2 **Probability Theory**
 
   2.2.1 Joint Probability
 
@@ -107,7 +107,7 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   2.2.5 Bayes Rule
 
- 2.3 Differential Calculus
+ 2.3 **Differential Calculus**
 
   2.3.1 Function
 
@@ -119,23 +119,23 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   2.3.5 Differentiation of Matrix Function
 
- 2.4 Summary
+ 2.4 **Summary**
 
 ### Chapter 3: Differentiable Programming
 
- 3.1 Swift is Everywhere
+ 3.1 **Swift is Everywhere**
 
- 3.2 Swift for TensorFlow
+ 3.2 **Swift for TensorFlow**
 
- 3.3 Algorithmic Differentiation
+ 3.3 **Algorithmic Differentiation**
 
   3.3.1 Programming Approaches
 
   3.3.2 Accumulation Modes
 
   3.3.3 Implementation Approaches
 
- 3.4 Swift Language
+ 3.4 **Swift Language**
 
   3.4.1 Values
 
@@ -155,33 +155,33 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   3.4.9 Differentiation
 
- 3.5 Python Interoperability
+ 3.5 **Python Interoperability**
 
- 3.6 Summary
+ 3.6 **Summary**
 
 ### Chapter 4: TensorFlow Basics
 
- 4.1 Tensor
+ 4.1 **Tensor**
 
- 4.2 Dataset Loading
+ 4.2 **Dataset Loading**
 
   4.2.1 Epochs and Batches
 
- 4.3 Defining Model
+ 4.3 **Defining Model**
 
   4.3.1 Neural Network Protocols
 
   4.3.2 Sequence of Layers
 
- 4.4 Training and Testing
+ 4.4 **Training and Testing**
 
   4.4.1 Checkpointing
 
   4.4.2 Model Optimization
 
   4.4.3 TrainingLoop
 
- 4.5 From Scratch for Research
+ 4.5 **From Scratch for Research**
 
   4.5.1 Layer
 
@@ -191,29 +191,29 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   4.5.4 Optimizer
 
- 4.6 Summary
+ 4.6 **Summary**
 
 ### Chapter 5: Neural Networks
 
- 5.1 Gradient-Based Optimization
+ 5.1 **Gradient-Based Optimization**
 
   5.1.1 Maxima, Minima, and Saddle Points
 
   5.1.2 Input Optimization
 
   5.1.3 Parameters Optimization
 
- 5.2 Linear Models
+ 5.2 **Linear Models**
 
   5.2.1 Regression
 
   5.2.2 Classification
 
- 5.3 Deep Neural Network
+ 5.3 **Deep Neural Network**
 
   5.3.1 Dense Neural Network
 
- 5.4 Activation Functions
+ 5.4 **Activation Functions**
 
   5.4.1 Sigmoid
 
@@ -227,21 +227,21 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   5.4.6 SELU
 
- 5.5 Loss Functions
+ 5.5 **Loss Functions**
 
   5.5.1 Sum of Squares
 
   5.5.2 Sigmoid Cross-Entropy
 
   5.5.3 Softmax Cross-Entropy
 
- 5.6 Optimization
+ 5.6 **Optimization**
 
   5.6.1 Gradient Descent
 
   5.6.2 Momentum
 
- 5.7 Regularization
+ 5.7 **Regularization**
 
   5.7.1 Dataset
 
@@ -251,11 +251,11 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   5.7.4 Optimization
 
- 5.8 Summary
+ 5.8 **Summary**
 
 ### Chapter 6: Computer Vision
 
- 6.1 Convolutional Neural Network
+ 6.1 **Convolutional Neural Network**
 
   6.1.1 Convolution Layer
 
@@ -265,19 +265,19 @@ The book covers foundational concepts of machine learning. It also introduces th
 
   6.1.4 Upsampling
 
- 6.2 Prominent Features
+ 6.2 **Prominent Features**
 
   6.2.1 Local Connectivity
 
   6.2.2 Parameter Sharing
 
   6.2.3 Translation Equivariance
 
- 6.3 Shortcut Connection
+ 6.3 **Shortcut Connection**
 
- 6.4 Image Recognition
+ 6.4 **Image Recognition**
 
- 6.5 Conclusion
+ 6.5 **Conclusion**
 
 ### References