Unlock the “Black Box” of Deep Learning: Mastering Math, Architectures, and Practical Implementation

Dive deep into the mathematical foundations and architectural intricacies of deep learning with this comprehensive resource, combining an in-depth ebook and a detailed video course. Designed for aspiring and practicing data scientists and engineers, this material demystifies the complex world of deep learning, enabling you to customize, maintain, and truly understand your models.

What You’ll Gain:

• A Solid Mathematical Foundation:
  • Explore the essential mathematical concepts underpinning deep learning, including linear algebra, vector calculus, multivariate statistics, probability distributions, and Bayesian inference.   
  • Understand how these mathematical paradigms translate into practical applications within deep learning models.
• Architectural Clarity:
  • Gain a thorough understanding of the structure and function of neural networks, from basic principles to advanced architectures.
  • Learn how to implement these architectures effectively using Python and PyTorch.   
• Practical Implementation with Python and PyTorch:
  • Follow along with well-annotated Python code and downloadable Jupyter notebooks, bridging the gap between theory and practice.   
  • Learn to design, implement, and troubleshoot your own deep learning models.
• Advanced Concepts and Applications:
  • Delve into advanced topics such as convolutions, image classification, object detection, manifolds, homeomorphism, Bayesian model parameter estimation, latent spaces, generative modeling, autoencoders, and variational autoencoders.
  • Understand the latest research and how to apply it.
• Troubleshooting and Optimization:
  • Learn to diagnose and address issues with underperforming models, including techniques for regularization and optimization.   
  • Gain the ability to improve the performance of your deep learning projects.
• Bridging the Gap Between Theory and Practice:
  • Move beyond “black box” understanding and develop a deep, intuitive grasp of how deep learning models work.
  • Learn to comprehend cutting-edge research.

Course and Book Structure:

Both the ebook and the 17-hour, 14-minute video course, taught by Krishnendu Chaudhury, follow a consistent structure, covering:

• An overview of machine learning and deep learning.
• Vectors, matrices, and tensors in machine learning.
• Classifiers and vector calculus.   
• Linear algebraic tools in machine learning.  
• Probability distributions in machine learning.
• Bayesian tools for machine learning.
• Function approximation: How neural networks model the world.
• Training neural networks: Forward propagation and backpropagation.   
• Loss, optimization, and regularization.
• Convolutions in neural networks.
• Neural networks for image classification and object detection.  
• Manifolds, homeomorphism, and neural networks.
• Fully Bayes model parameter estimation.
• Latent space and generative modeling, autoencoders, and variational autoencoders.   

Who This Is For:

• Individuals with a basic understanding of Python programming.
• Those familiar with the fundamental concepts of algebra and calculus.
• Anyone seeking to gain a deeper understanding of the mathematical and architectural underpinnings of deep learning.
• Beginner to advanced deep learning students.

About the Author:

Krishnendu Chaudhury, co-founder and CTO of Drishti Technologies, brings extensive experience from Google and Adobe to this comprehensive learning resource.   

By combining the ebook and e-course, you’ll gain a holistic and practical understanding of the math and architectures that power deep learning, empowering you to build and optimize your own sophisticated models.