Deep Learning Resources – A Super Syllabus (2024 October)

Phase 1: Foundational Deep Learning Knowledge

1. Introduction to Machine Learning

• Resources:
  • Coursera’s “Machine Learning” by Andrew Ng: A classic course (not for free, anymore, though) that provides a strong foundation.
    • GitHub resources (no cost): Machine Learning Specialization Coursera (GitHub)
    • Mathematics for Machine Learning and Data Science Specialization – Coursera (GitHub)
  • “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron (GitHub resources): A practical book covering both classical ML and deep learning.

– Topics:

• Supervised vs. Unsupervised Learning
• Fundamental algorithms (Linear Regression, Logistic Regression)
• Introduction to Neural Networks

2. Mathematics for Machine Learning

– Resources:

• “Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: Focuses on the mathematical concepts underpinning ML.
• MIT OpenCourseWare: For supplementary learning in specific areas.

– Topics:

• Linear Algebra
• Calculus
• Probability and Statistics

3. Programming Skills

– Resources:

• Python Crash Course by Eric Matthes
• FreeCodeCamp: Interactive Python tutorials.

– Topics:

• Python Programming
• Essential Libraries: NumPy, Pandas, Matplotlib
• Basic Data Structures and Algorithms

Phase 2: Intermediate Concepts and Hands-on Practice

1. Supervised Learning

• Resources:
  • “Machine Learning Specialization” by DeepLearning.AI
  • “An Introduction to Statistical Learning” by Gareth James et al.

• Topics:
  • Advanced algorithms (Support Vector Machines, Decision Trees, Ensemble Methods)
  • Model Evaluation and Cross-Validation

• Projects:
  • Build classification/regression models on datasets like the UCI Machine Learning Repository.

2. Unsupervised Learning

– Resources:

• “Unsupervised Learning with Python” by Giuseppe Bonaccorso (not for free)

– Topics:

• Clustering Techniques (K-Means, DBSCAN)
• Dimensionality Reduction (PCA, t-SNE, UMAP)
• Association Rule Learning

– Projects:

• Customer segmentation analysis
• Anomaly detection in network data

3. Natural Language Processing (NLP)

– Resources:

• “Natural Language Processing with Python” by Steven Bird, Ewan Klein, and Edward Loper

– Hugging Face Tutorials: Practical guides using transformer models.

– Topics:

• Text Preprocessing (Tokenization, Stemming, Lemmatization)
• Word Embeddings (Word2Vec, GloVe)
• Transformer Models (BERT, GPT)

– Projects:

• Sentiment analysis on Twitter data.
• Develop a simple question-answering system.

Phase 3: Advanced Topics and Specialization

1. Deep Learning

– Resources:

• Coursera’s “Deep Learning Specialization” (not for free) by Andrew Ng
• “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

– Topics:

• Neural Network Architectures
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and LSTMs

– Projects:

• Image classification with CNNs.
• Time series forecasting with RNNs.

2. Generative AI and Large Language Models (LLMs)

– Resources:

• OpenAI’s Learning Resources
• “Generative Deep Learning” by David Foster

– Topics:

• Generative Adversarial Networks (GANs)
• Transformer Models and Attention Mechanisms
• Fine-Tuning Pre-trained Models

– Projects:

• Text generation with GPT models.
• Image synthesis using GANs.

3. Reinforcement Learning

– Resources:

• “Reinforcement Learning: An Introduction” by Sutton and Barto

• OpenAI’s Spinning Up in Deep RL

– Topics:

• Fundamentals of Reinforcement Learning
• Policy Gradient Methods
• Deep Reinforcement Learning

– Projects:

• Develop an agent for OpenAI Gym environments.
• Implement algorithms like Deep Q-Networks (DQNs).

4. Graph-Based Learning

– Resources:

• “Graph Representation Learning” by William L. Hamilton
• Stanford’s CS224W: Machine Learning with Graphs

– Topics:

• Graph Theory Basics
• Graph Neural Networks (GNNs)
• Applications in Social Networks and Bioinformatics

– Projects:

• Node classification and link prediction tasks.
• Recommendation systems using graph embeddings.

Phase 4: Research and Application

1. Literature Review and Research Papers

– Resources:

– arXiv.org: Preprints of the latest research.

– Papers with Code: Links papers to code implementations.

– Activities:

• Stay updated with cutting-edge research.
• Identify potential areas for contribution.

2. Industry Applications

• Resources:
  • Case Studies from tech companies (Google AI, Facebook AI Research).
  • Industry Reports from Gartner, McKinsey.

– Topics:

• Scalability and Deployment of ML Models.
• Ethical and Legal Considerations in AI.

– Projects:

• Deploy a model using cloud services like AWS SageMaker.
• Explore ML Ops tools for model monitoring.

3. Capstone Project

– Project Ideas:

• Develop an AI-driven application addressing a real-world problem (e.g., healthcare diagnostics, financial forecasting).

• Collaborate with an academic or industry partner for applied research.

– Considerations:

• End-to-end project development from data collection to deployment.
• Incorporate user feedback and iterative improvements.

Phase 5: Continuous Learning and Contribution

1. Stay Updated

• – Resources:
  • Newsletters: The Batch (deeplearning.ai), Import AI.
  • Podcasts: Lex Fridman Podcast, TWIML AI Podcast.
• – Activities:
  • Regularly read and summarize new research papers.
  • Experiment with new frameworks and tools.

2. Contribute to the Community

– Activities:

• Write articles or tutorials on platforms like Medium or GitHub Pages.
• Participate in open-source projects.
• Engage in Kaggle competitions to solve real-world problems.

3. Networking

– Activities:

• Attend conferences (NeurIPS, ICML, ACL) virtually or in person.
• Join online forums (Reddit’s r/MachineLearning, Stack Overflow).
• Participate in local meetups or hackathons.

Additional Tools and Topics

– Programming Frameworks:

• TensorFlow and PyTorch: For deep learning projects.
• Scikit-learn: For classical machine learning algorithms.

– Version Control:

• Git and GitHub: For code collaboration and sharing.

– Data Engineering Basics:

• Understanding databases, data pipelines, and big data tools (Hadoop, Spark).

– Ethics and Fairness in AI

• Resources:
  • “Fairness and Machine Learning” by Solon Barocas, Moritz Hardt, and Arvind Narayanan (pdf)

– Topics:

• Bias and fairness in algorithms.
• Transparency and explainability.

Final Recommendations

• Customize Your Path: Focus on areas that align with your career goals or research interests. (A few of our Prism14 favorites listed:)
  • Production: Writing, storyboarding, video creation
  • Strategy: Engagement, quality, impact
  • Technical Skills: Materials, digital, workflow optimization
  • Materials
    • Chemistry
  • Life and Living
    • Healthcare
• Practical Experience: Apply your knowledge through internships or collaborative projects.
  • Prism14 – How to successfully connect with internships
  • Prism14 – How to launch and maintain wildly-successful collabs
• Mentorship and Guidance: Seek out mentors in academia or industry who can provide advice and feedback.
  • Prism14 – How to cultivate mentors