Concept Enumeration

Prompt

Please generate a list of the 200 core concepts that need to be covered in this course and the order in which they should be taught.

Return numbered list of these core concepts using a single line per concept. For each concept, just return a short concept name and use acronyms or abbreviations when appropriate. They will be used as labels in a graph network. Make sure that the concepts are distinct not duplicated in the list.

Use title case for the concept names.

Start with the most basic foundational concepts and then proceed to the most complex concepts.

Workflow

Setting the stage

I'm compiling a list of 200 unique core concepts for a deep learning course. The order will start with basic concepts and gradually advance to more complex ones.

Expanding the list

I'm building upon the course outline to include topics like generative AI and RL, ensuring each is distinct and enumerated correctly while avoiding duplicates.

Organizing topics

Structuring the course into categories like basic foundations, Python tools, and advanced topics such as generative AI and domain-specific architectures to ensure a systematic progression.

Finalizing concepts

Wrapping up the course with distinctions like "AI Definition" and "ML Definition," ensuring clarity with concise, title-case concepts in 200 carefully enumerated lines.

Ensuring clarity

I'm refining concepts like "AI Definition" and "ML Definition," combining title case and potentially using acronyms for distinct clarity. The goal is to maintain precision and avoid redundancy.

Detailed line distribution

I'm assigning 10-line chunks to various topics, such as Python, ML fundamentals, neural networks, and advanced architectures, ensuring comprehensive coverage across all 200 lines.

Progressing through topics

I'm charting our course with concise lines to cover "NN Fundamentals" and "Training Processes," ensuring distinct and systematic progression across 200 meticulously controlled entries.

Venturing into complexity

I'm exploring advanced topics in neural networks, including special architectures like Capsule Networks and Tree-Augmented Naive Bayes, as well as Transformer techniques for natural language processing.

Unraveling generative AI

I'm analyzing advanced generative models and reinforcement learning techniques. This involves delving into diffusion models, denoising diffusion, principal components, and ethical considerations like fairness and privacy.

Mapping deployment and application

I'm covering GPU and TPU acceleration, memory management, model compression, and practical applications like computer vision and autonomous driving. This completes the cycle, ensuring comprehensive coverage.

Result

Thought about deep learning curriculum concepts for 1m 42s

AI Definition
ML Definition
History Of AI
Nobel Prizes
Key Figures
AI Branches
Symbolic AI
Data-Driven AI
NN Origins
Biological Inspiration
Python Setup
Conda Environment
Jupyter Notebooks
NumPy Basics
Pandas Basics
Matplotlib Basics
SciPy Basics
Scikit-Learn Overview
PyTorch Intro
TensorFlow Intro
Supervised Vs Unsupervised
Regression Vs Classification
Overfitting Vs Underfitting
Training-Validation-Test
Bias-Variance Tradeoff
Regularization Overview
Cross-Validation
Evaluation Metrics
Loss Functions
Gradient Descent
Data Preprocessing
Scaling And Normalization
Data Augmentation
Feature Engineering
Dimensionality Concepts
Data Splits
Data Visualization
Data Cleaning
Data Imbalance
Data Ethics
Perceptron Model
Multi-Layer Perceptron
Activation Functions
Forward Propagation
Backpropagation
Weights And Biases
Batch Training
Mini-Batch Training
Stochastic Training
Learning Rate
Loss Surface
Local Minima
Vanishing Gradients
Exploding Gradients
Initialization Methods
He Initialization
Xavier Initialization
Momentum Optimizer
AdaGrad
Adam Optimizer
Convolution Operation
Filters And Kernels
Pooling Layers
Stride And Padding
Fully Connected Layers
Batch Normalization
CNN Architectures
ImageNet
Transfer Learning Basics
Object Detection
Sequence Modeling
Vanilla RNN
LSTM Units
GRU Units
Backprop Through Time (BPTT)
Exploding Gradient Control
Language Modeling
Text Generation
Time-Series Forecasting
Attention Mechanism
Deep Architecture
Layer Stacking
Depth Vs Width
Residual Networks (ResNet)
Inception Modules
DenseNet
Skip Connections
Dropout
BatchNorm
Weight Decay
Capsule Networks
Graph Neural Networks (GNNs)
Transformers Intro
Vision Transformers (ViT)
Self-Attention
Positional Encoding
BERT Overview
GPT Overview
Sequence-To-Sequence
Neural Machine Translation
Generative Models
Autoencoders (AE)
Variational Autoencoders (VAE)
GAN Basics
Generator Vs Discriminator
Latent Space
Conditional GAN
CycleGAN
Style Transfer
Text-To-Image
Diffusion Models
Denoising Diffusion
Score Matching
Likelihood Estimation
Energy-Based Models
Flow-Based Models
Prompt Engineering
Text Generation Models
Large Language Models (LLMs)
ChatGPT Concept
PCA
t-SNE
UMAP
Autoencoder DR
Manifold Hypothesis
Feature Space
Principal Components
Nonlinear Embeddings
High-Dimensional Data
Data Visualization Tools
RL Definition
Markov Decision Process (MDP)
Value Functions
Policy Gradient
Q-Learning
Deep Q-Network (DQN)
Actor-Critic
Reward Shaping
Exploration Vs Exploitation
Baseline Algorithms
Model Interpretability
Explainable AI (XAI)
SHAP
LIME
Fairness
Bias Mitigation
Privacy
Security
Adversarial Examples
Responsible AI
GPU Acceleration
TPU Acceleration
Memory Management
Model Compression
Quantization
Pruning
Edge Deployment
Cloud Services
AutoML
MLOps
Hyperparameter Tuning
Grid Search
Random Search
Bayesian Optimization
Early Stopping
Learning Rate Scheduling
Data Parallelism
Model Parallelism
Checkpointing
Version Control
Accuracy
Precision
Recall
F1 Score
AUC-ROC
Confusion Matrix
Top-1 Vs Top-5 Accuracy
BLEU Score
Perplexity
Mean Squared Error (MSE)
Computer Vision
Natural Language Processing (NLP)
Speech Recognition
Medical Imaging
Drug Discovery
Protein Folding
Autonomous Driving
Robotics
Financial Forecasting
Recommender Systems
Project Scoping
Requirement Analysis
Data Acquisition
Data Annotation
Resource Allocation
Team Collaboration
Version Control Workflow
Agile Methods
Code Review
Documentation

Getting the CSV

Prompt

please return this list in a single csv file with the first column as ConceptID and the second column as ConceptLabel

import pandas as pd

# Define the list of concepts
concepts = [
    "AI Definition", "ML Definition", "History Of AI", "Nobel Prizes", "Key Figures", "AI Branches", "Symbolic AI",
    "Data-Driven AI", "NN Origins", "Biological Inspiration", "Python Setup", "Conda Environment", "Jupyter Notebooks",
    "NumPy Basics", "Pandas Basics", "Matplotlib Basics", "SciPy Basics", "Scikit-Learn Overview", "PyTorch Intro",
    "TensorFlow Intro", "Supervised Vs Unsupervised", "Regression Vs Classification", "Overfitting Vs Underfitting",
    "Training-Validation-Test", "Bias-Variance Tradeoff", "Regularization Overview", "Cross-Validation",
    "Evaluation Metrics", "Loss Functions", "Gradient Descent", "Data Preprocessing", "Scaling And Normalization",
    "Data Augmentation", "Feature Engineering", "Dimensionality Concepts", "Data Splits", "Data Visualization",
    "Data Cleaning", "Data Imbalance", "Data Ethics", "Perceptron Model", "Multi-Layer Perceptron",
    "Activation Functions", "Forward Propagation", "Backpropagation", "Weights And Biases", "Batch Training",
    "Mini-Batch Training", "Stochastic Training", "Learning Rate", "Loss Surface", "Local Minima",
    "Vanishing Gradients", "Exploding Gradients", "Initialization Methods", "He Initialization",
    "Xavier Initialization", "Momentum Optimizer", "AdaGrad", "Adam Optimizer", "Convolution Operation",
    "Filters And Kernels", "Pooling Layers", "Stride And Padding", "Fully Connected Layers", "Batch Normalization",
    "CNN Architectures", "ImageNet", "Transfer Learning Basics", "Object Detection", "Sequence Modeling",
    "Vanilla RNN", "LSTM Units", "GRU Units", "Backprop Through Time (BPTT)", "Exploding Gradient Control",
    "Language Modeling", "Text Generation", "Time-Series Forecasting", "Attention Mechanism", "Deep Architecture",
    "Layer Stacking", "Depth Vs Width", "Residual Networks (ResNet)", "Inception Modules", "DenseNet",
    "Skip Connections", "Dropout", "BatchNorm", "Weight Decay", "Capsule Networks", "Graph Neural Networks (GNNs)",
    "Transformers Intro", "Vision Transformers (ViT)", "Self-Attention", "Positional Encoding", "BERT Overview",
    "GPT Overview", "Sequence-To-Sequence", "Neural Machine Translation", "Generative Models", "Autoencoders (AE)",
    "Variational Autoencoders (VAE)", "GAN Basics", "Generator Vs Discriminator", "Latent Space", "Conditional GAN",
    "CycleGAN", "Style Transfer", "Text-To-Image", "Diffusion Models", "Denoising Diffusion", "Score Matching",
    "Likelihood Estimation", "Energy-Based Models", "Flow-Based Models", "Prompt Engineering", "Text Generation Models",
    "Large Language Models (LLMs)", "ChatGPT Concept", "PCA", "t-SNE", "UMAP", "Autoencoder DR", "Manifold Hypothesis",
    "Feature Space", "Principal Components", "Nonlinear Embeddings", "High-Dimensional Data", "Data Visualization Tools",
    "RL Definition", "Markov Decision Process (MDP)", "Value Functions", "Policy Gradient", "Q-Learning",
    "Deep Q-Network (DQN)", "Actor-Critic", "Reward Shaping", "Exploration Vs Exploitation", "Baseline Algorithms",
    "Model Interpretability", "Explainable AI (XAI)", "SHAP", "LIME", "Fairness", "Bias Mitigation", "Privacy",
    "Security", "Adversarial Examples", "Responsible AI", "GPU Acceleration", "TPU Acceleration", "Memory Management",
    "Model Compression", "Quantization", "Pruning", "Edge Deployment", "Cloud Services", "AutoML", "MLOps",
    "Hyperparameter Tuning", "Grid Search", "Random Search", "Bayesian Optimization", "Early Stopping",
    "Learning Rate Scheduling", "Data Parallelism", "Model Parallelism", "Checkpointing", "Version Control",
    "Accuracy", "Precision", "Recall", "F1 Score", "AUC-ROC", "Confusion Matrix", "Top-1 Vs Top-5 Accuracy",
    "BLEU Score", "Perplexity", "Mean Squared Error (MSE)", "Computer Vision", "Natural Language Processing (NLP)",
    "Speech Recognition", "Medical Imaging", "Drug Discovery", "Protein Folding", "Autonomous Driving", "Robotics",
    "Financial Forecasting", "Recommender Systems", "Project Scoping", "Requirement Analysis", "Data Acquisition",
    "Data Annotation", "Resource Allocation", "Team Collaboration", "Version Control Workflow", "Agile Methods",
    "Code Review", "Documentation"
]

# Create a DataFrame with ConceptID and ConceptLabel
df = pd.DataFrame({
    "ConceptID": range(1, len(concepts) + 1),
    "ConceptLabel": concepts
})

# Save the DataFrame to a CSV file
file_path = "/mnt/data/EE4940_Core_Concepts.csv"
df.to_csv(file_path, index=False)

file_path

Result

Deep Learning Concepts