Deep Learning with Scikit-learn and PyTorch: Master the Two Giants: Deep Learning with Scikit-learn andPyTorch
Deep Learning with Scikit-learn and PyTorch: Master the Two Giants: Deep Learning with Scikit-learn andPyTorch (Even if You're New).YourStep-by-StepGuide … and PyTorch (Python Trailblazer’s Bible) by Katie Millie
English | March 3, 2024 | ISBN: N/A | ASIN: B0CX1K1K24 | 144 pages | EPUB | 0.24 Mb
English | March 3, 2024 | ISBN: N/A | ASIN: B0CX1K1K24 | 144 pages | EPUB | 0.24 Mb
Delve into the Cutting Edge: Deep Learning with Scikit-learn and PyTorch
Unleash the transformative power of Deep Learning and unlock a world of possibilities with "Deep Learning with Scikit-learn and PyTorch," your comprehensive guide to mastering this revolutionary technology.
Whether you're a seasoned programmer seeking to expand your skillset or a curious beginner eager to explore the future of artificial intelligence, this book empowers you to build intelligent applications and tackle complex problems across diverse domains.
Why choose this book?
- Unique Synergy: Leverage the complementary strengths of Scikit-learn for data preprocessing and model evaluation, and PyTorch for building and training deep learning models.
- Beginner-Friendly Approach: We break down complex concepts into manageable steps, ensuring a smooth learning experience, even for those new to deep learning.
- Hands-on Learning: Dive headfirst into practical projects, building your skills by tackling real-world challenges in various fields like computer vision, natural language processing, and time series forecasting.
- Solid Foundation: Gain a comprehensive understanding of the fundamental principles of deep learning, preparing you for further exploration and innovation.
- Future-Proof Your Skills: Stay ahead of the curve by exploring advanced topics like transfer learning and generative models.
- The Foundations of Deep Learning: Demystify deep learning concepts, understand its applications, and compare it to traditional machine learning approaches.
- Harnessing Scikit-learn: Explore Scikit-learn's role in deep learning pipelines, from data preprocessing and feature engineering to model evaluation.
- Building with Scikit-learn: Implement simple deep learning models using Scikit-learn's neural network modules and fine-tune pre-trained models for specific tasks.
- Introducing PyTorch: Grasp the fundamentals of PyTorch, a powerful and flexible deep learning framework, and learn its core concepts like tensors and building neural networks from scratch.
- Architecting Deep Learning Models: Implement popular architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) using PyTorch's built-in modules.
- Training and Optimization: Understand the training process in PyTorch, including forward pass, backward pass, and gradient descent. Explore various optimization algorithms and techniques to prevent overfitting.
- Leveraging Pre-trained Models: Accelerate development and improve performance by utilizing pre-trained models like ImageNet and BERT for transfer learning.
- Building Real-World Projects: Apply your knowledge by constructing practical deep learning projects that address real-world challenges in various fields.
- A Glimpse into the Future: Explore advanced topics like reinforcement learning and generative models, and stay updated with the latest advancements in deep learning.