A comprehensive TensorFlow And PyTorch Resources

• In this blog post, I am sharing resources to achieve proficiency in TensorFlow and PyTorch, the most commonly used frameworks in deep learning.

1. TensorFlow

• TensorFlow is an open-source machine learning framework developed by the Google Brain team. It is widely used for building and deploying machine learning and deep learning models
• TensorFlow_Resources Link

Contents:

1. Fundamentals:

• TF Fundamentals
• Neural Network Regression with TF
• Neural Network Classification with TF

2. Computer Vision:

• CNN and Computer Vision with TF

3. Transfer Learning:

• Transfer Learning with TensorFlow Part 1: Feature Extraction
• Transfer Learning with TensorFlow Part 2: Fine-tuning
• Transfer Learning with TensorFlow Part 3: Scaling up
• Milestone Project 1

4. Natural Language Processing:

• NLP with TensorFlow
• Milestone Project 2: SkimLit

5. Time Series:

• Milestone Project 3: Time series forecasting in TensorFlow

2. PyTorch

• PyTorch is an open-source machine learning library developed by Facebook’s AI Research Lab (FAIR). It is widely used for deep learning applications, including computer vision and natural language processing
• PyTorch_Resources Link

Contents:

1. PyTorch Fundamentals
2. PyTorch Workflow Fundamentals
3. PyTorch Neural Network Classification
4. PyTorch Computer Vision
5. PyTorch Custom Datasets
6. PyTorch Going Modular
7. PyTorch Transfer Learning
8. PyTorch Experiment Tracking
9. PyTorch Paper Replicating
10. PyTorch Model Deployment

Note: All credit goes to Daniel Bourke for his excellent courses on TensorFlow and PyTorch. I highly recommend completing his courses.