Deep Learning for Data Architects

“Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning.

The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations.

By the end of the book, you will be able to use deep learning to solve real-world problems.

KEY FEATURES  

• Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks.
• Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
• Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems.

WHAT YOU WILL LEARN

• Develop a comprehensive understanding of neural networks' key concepts and principles.
• Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch.
• Build and implement predictive models using various neural networks
• Learn how to use Transformers for complex NLP tasks
• Explore techniques to enhance the performance of your deep learning models.

WHO THIS BOOK IS FOR

This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field.

1. Python for Data Science
2. Real-World Challenges for Data Professionals in Converting Data Into Insights
3. Build a Neural Network-Based Predictive Model
4. Convolutional Neural Networks
5. Optical Character Recognition
6. Object Detection
7. Image Segmentation
8. Recurrent Neural Networks
9. Generative Adversarial Networks
10. Transformers

Shekhar Khandelwal is a distinguished Senior AI & Data Scientist, residing in the bustling harbor city of Hamburg, Germany. His academic career shines bright with a Master’s degree in Data Science, achieving distinction for his thesis work in the realm of Computer Vision. His name can be spotted in top-tier research papers and publications, predominantly in the area of Deep Learning.

Bringing to the table over 15 years of experience, the author has an extensive professional background in the field of AI and machine learning. His journey ranges from coding and crafting enterprise-level AI products to leading data teams and mentoring future data science professionals. He has successfully developed numerous client solutions utilizing big cloud service platforms such as AWS, Google Cloud, Microsoft Azure, and IBM Cloud.

Despite his deep involvement in the tech industry, our author is also a fitness enthusiast. When he’s not making machines smarter, he’s likely to be found flexing his muscles at the gym, attending a crossfit class, cycling, or participating in marathon runs. His zeal for fitness is as strong as his passion for AI, making him a well-rounded professional in all respects.