Seven new tutorials:
- Introduction to TensorFlow and Keras application development
- Zero, preface,
- Introduction to Neural networks and deep learning
- Second, model architecture
- 3. Model evaluation and optimization
- Fourth, productization
- TensorFlow Practical guide to deep learning for images
- Zero, preface,
- Machine learning toolkit
- 2. Picture data
- Classical neural network
- A practical guide to learning Python meta
- Zero, preface,
- Introduction to meta-learning
- Face and audio recognition using connected networks
- Prototype networks and their variants
- Use TensorFlow relationships and matching networks
- 5. Memory enhancement neural network
- MAML and its variants
- Meta-sgd and Reptile
- 8. Gradient consistency as an optimization objective
- Ix. Latest progress and follow-up steps
- Ten, the answer
- A practical guide to Python reinforcement learning
- Zero, preface,
- Introduction to reinforcement learning
- Introduction to OpenAI and TensorFlow
- Markov decision process and dynamic programming
- Monte Carlo method for games
- Five, time difference learning
- Six, the multi-arm slot machine problem
- 7. Deep learning foundation
- Deep Q network and Atari games
- 9. Play Doom with deep Loop Q Network
- Asynchronous advantage Actor critic network
- Strategy gradient and optimization
- Capstone project — DQN for racing cars
- Latest progress and follow-up steps
- Xiv. Answer
- Python Smart Project
- Zero, preface,
- First, the foundation of artificial intelligence system
- Second, transfer learning
- Neural machine translation
- Fashion industry style transfer using GAN
- 5. Video subtitle application
- 6. Intelligent recommendation system
- 7. Mobile app for movie review emotion analysis
- Conversational AI chatbots for customer service
- Autonomous autonomous vehicles using reinforcement learning
- Captcha from the perspective of deep learning
- Proficient in Sklearn and TensorFlow predictive analysis
- Zero, preface,
- Integration method of regression and classification
- Two, cross verification and parameter adjustment
- Three, the use of characteristics
- Introduction to Artificial neural Networks and TensorFlow
- Fifth, TensorFlow and deep neural network are used for predictive analysis
- New features in TensorFlow 2.0
- Zero, preface,
- Part 1: TensorFlow 2.0 — Architecture and API changes
- Introduction to TensorFlow 2.0
- Keras default integration and eager implementation
- Part 2: TensorFlow 2.0 – Data and model training pipeline
- Design and build the input data pipeline
- Iv. Model training and use of TensorBoard
- Part 3: TensorFlow 2.0 — Model inference and deployment and AIY
- 5. Model inference pipeline – Multi-platform deployment
- AIY Project and TensorFlow Lite
- Part 4: TensorFlow 2.0 – Migration, summary
- Migrate from TensorFlow 1.x to 2.0