Curriculum

14 Chapters · 14 Lessons

  1. 1

    Book Preview

    1. Ultimate Neural Network Programming with Python Free preview
  2. 2

    Book

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Understanding AI History

    1. (Included in full purchase)

      Understanding AI History

  4. 4

    Chapter 2 : Setting up Python Workflow for AI Development

    1. (Included in full purchase)

      Setting up Python Workflow for AI Development

  5. 5

    Chapter 3 : Python Libraries for Data Scientists

    1. (Included in full purchase)

      Python Libraries for Data Scientists

  6. 6

    Chapter 4 : Foundational Concepts for Effective Neural Network Training

    1. (Included in full purchase)

      Foundational Concepts for Effective Neural Network Training

  7. 7

    Chapter 5 : Dimensionality Reduction, Unsupervised Learning and Optimizations

    1. (Included in full purchase)

      Dimensionality Reduction, Unsupervised Learning and Optimizations

  8. 8

    Chapter 6 : Building Deep Neural Networks from Scratch

    1. (Included in full purchase)

      Building Deep Neural Networks from Scratch

  9. 9

    Chapter 7 : Derivatives, Backpropagation, and Optimizers

    1. (Included in full purchase)

      Derivatives, Backpropagation, and Optimizers

  10. 10

    Chapter 8 : Understanding Convolution and CNN Architectures

    1. (Included in full purchase)

      Understanding Convolution and CNN Architectures

  11. 11

    Chapter 9 : Understanding Basics of TensorFlow and Keras

    1. (Included in full purchase)

      Understanding Basics of TensorFlow and Keras

  12. 12

    Chapter 10 : Building End-to-end Image Segmentation Pipeline

    1. (Included in full purchase)

      Building End-to-end Image Segmentation Pipeline

  13. 13

    Chapter 11 : Latest Advancements in AI

    1. (Included in full purchase)

      Latest Advancements in AI

  14. 14

    Index

    1. (Included in full purchase)

      Index

About the course

This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries. Throughout the chapters, readers are engaged with the book through practice exercises, and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding. This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry.

About the Author

Vishal Rajput is a recognized 3 x top 50 AI writer on Medium and a fervent explorer and practitioner of Artificial Intelligence. He holds an advanced master’s in AI from the globally acclaimed KU Leuven, Belgium. Since venturing into AI in 2016, Vishal has balanced academic rigor with industrial applicability, contributing to eight research papers in international journals and book chapters and amassing over six years of experience. He has collaborated with distinguished research labs such as SONY R&D and MIRZ UZ Leuven, exploring and contributing to various aspects of AI. Outside his professional and academic life, Vishal is an active speaker at AI meetups and events, serves as a mentor in AI, and leads innovation and AI development at a drone-based startup. With a wealth of knowledge and a plethora of experiences, he extends a warm invitation to all, offering a collaborative exploration and enriched appreciation of the unprecedented field of Artificial Intelligence.