Python Deep Learning Solutions
Over 20 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python
Created by Indra den Bakker
Explore practical ways to apply deep learning in real-world scenarios using Python. You'll work with neural networks, reinforcement learning, and transfer learning to solve challenges in areas like computer vision and time series analysis. Build your skills by implementing and fine-tuning models with popular frameworks.
Packt | Jun 2018 | 105 min
What You Will Learn
You'll learn by working through practical solutions to common and advanced deep learning problems. Each topic is explained with clear examples, showing how to choose the right framework and adapt code to your needs. Along the way, you'll see the pros and cons of different approaches and how to improve model performance.
Key Features
- Build and optimize neural network models for real-world applications
- Apply deep learning techniques using TensorFlow, PyTorch, and Keras
- Evaluate and adapt solutions to fit different machine learning challenges
Target Audience
If you're a machine learning professional or Python developer with experience in libraries like NumPy and scikit-learn, this is for you. It's ideal if you want to deepen your understanding of deep learning and apply it to real-world projects. A solid grasp of machine learning basics and some math background will help you get the most out of the material.
