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AI Engineering
Python

Deep Learning and AI Superhero

Mastering TensorFlow, Keras, and PyTorch

This guide unlocks the secrets of deep learning frameworks like TensorFlow, Keras, and PyTorch, enabling you to build, train, and deploy cutting-edge AI models. This book will transform your understanding of deep learning, providing you with the tools to tackle the AI challenges of tomorrow.

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Improve your programming skills

What You'll Get from This Book

9 chapters spanning over 690 pages

More than 450 explanatories blocks of code

More than 35 practical exercises

3 Quizzes to test your knowledge

5 Practical "Real World" Projects

About thIS book

Mastering TensorFlow for Real-World AI

TensorFlow stands as one of the most powerful tools in the AI toolkit, offering versatility and scalability for both research and production. "Deep Learning and AI Superhero" provides a comprehensive exploration of TensorFlow 2.x, focusing on its enhanced features that simplify model design, debugging, and deployment. From understanding the basics of tensors and data flow to mastering complex neural architectures, this book covers every angle.

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Through detailed tutorials and examples, you’ll learn how to leverage TensorFlow for projects ranging from image recognition systems to natural language processing applications. Additionally, the book dives into advanced topics such as customizing models with TensorFlow’s flexible API and optimizing computational efficiency with TensorFlow’s runtime features.

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By the end of this section, you’ll be equipped not only with theoretical knowledge but also with practical skills that you can apply in your AI projects.

Deep Dive into Keras and PyTorch: From Basics to Mastery

Keras and PyTorch are pivotal to the democratization of deep learning technologies, providing intuitive frameworks that speed up the experimentation cycle and enhance the creativity of AI solutions. "Deep Learning and AI Superhero" thoroughly details how to harness the straightforward yet powerful features of Keras for designing and training deep learning models with minimal hassle.

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Concurrently, it explores PyTorch’s dynamic computation graphs that offer an optimal environment for cutting-edge research and prototyping. This section includes practical guides on creating custom deep learning layers and architectures, effectively using callbacks and checkpoints in Keras, and implementing PyTorch’s advanced optimization algorithms for large-scale applications.

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Case studies in this book show real applications, such as autonomous vehicle systems and advanced healthcare diagnostics, underlining the transformative potential of mastering both Keras and PyTorch.

The journey through deep learning and AI doesn’t stop here. "Deep Learning and AI Superhero" takes you beyond the frameworks into the realm of real-world application and innovation. It discusses ethical AI, ensuring that as we advance our technology, we also consider the societal impacts.

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This book will not just make you proficient in technical skills but will also prepare you to be a thought leader in the AI community, ready to address and solve complex challenges that lie ahead.

Deep Learning is driving the most significant advancements in Artificial Intelligence, powering applications such as self-driving cars, natural language processing, image recognition, and recommendation systems. Deep Learning and AI Superhero provides a structured and hands-on approach to mastering deep learning using TensorFlow, Keras, and PyTorch. As industries increasingly rely on AI-driven solutions, understanding and implementing deep learning models is essential for developers and data scientists looking to stay at the forefront of AI innovation.
This book teaches you how to design, train, and optimize deep neural networks for real-world AI applications. You will gain practical experience in working with convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence modeling, and transformers for NLP. Through hands-on coding exercises and full-scale projects, you will learn how to fine-tune models, improve accuracy, and deploy AI solutions efficiently, making you a more skilled and job-ready AI engineer.
Unlike many deep learning books that focus solely on theory, Deep Learning and AI Superhero emphasizes practical implementation, guiding you through coding deep learning models from scratch. It covers not only fundamental architectures but also cutting-edge techniques such as generative models, transfer learning, and hyperparameter tuning. With real-world projects and interactive exercises, this book ensures that you don’t just understand deep learning concepts—you know how to apply them in real AI applications.
A basic understanding of Python and Machine Learning is recommended, but no prior deep learning experience is required. The book starts with foundational concepts and gradually progresses to more advanced techniques, making it accessible to both beginners and experienced practitioners looking to expand their deep learning expertise.
Access to the Cuantum Technologies VIP customer service, with a dedicated team of developers ready to answer all your questions. A code repository with fully working examples and pre-tested, production-ready code. The Success University e-learning platform, where you can access additional resources and free video content to reinforce your learning. Regular updates and additional materials to stay updated with new advancements in deep learning and AI.

Table of contents

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Chapter 1: Introduction to Neural Networks and Deep Learning

1.1 Perceptron and Multi-Layer Perceptron (MLP)

1.2 Backpropagation, Gradient Descent, and Optimizers

1.3 Overfitting, Underfitting, and Regularization Techniques

1.4 Loss Functions in Deep Learning

Practical Exercises Chapter 1

Chapter 2: Deep Learning with TensorFlow 2.x

2.1 Introduction to TensorFlow 2.x

2.2 Building, Training, and Fine-Tuning Neural Networks in TensorFlow

2.3 Using TensorFlow Hub and Model Zoo for Pretrained Models

2.4 Saving, Loading, and Deploying TensorFlow Models

Practical Exercises Chapter 2

Chapter 3: Deep Learning with Keras

3.1 Introduction to Keras API in TensorFlow 2.x

3.2 Building Sequential and Functional Models with Keras

3.3 Model Checkpointing, Early Stopping, and Callbacks in Keras

3.4 Deploying Keras Models to Production

Practical Exercises Chapter 3

Quiz Part 1: Neural Networks and Deep Learning Basics

1. Introduction to Neural Networks and Deep Learning (Chapter 1)

2. Deep Learning with TensorFlow 2.x (Chapter 2)

3. Deep Learning with Keras (Chapter 3)

Answers to the Quiz

Chapter 4: Deep Learning with PyTorch

4.1 Introduction to PyTorch and its Dynamic Computation Graph

4.2 Building and Training Neural Networks with PyTorch

4.3 Transfer Learning and Fine-Tuning Pretrained PyTorch Models

4.4 Saving and Loading Models in PyTorch

4.5 Deploying PyTorch Models with TorchServe

Chapter 5: Convolutional Neural Networks (CNNs)

5.1 Introduction to CNNs and Image Processing

5.2 Implementing CNNs with TensorFlow, Keras, and PyTorch

5.3 Advanced CNN Techniques (ResNet, Inception, DenseNet)

5.4 Practical Applications of CNNs (Image Classification, Object Detection)

Practical Exercises Chapter 5

Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs

6.1 Introduction to RNNs, LSTMs, and GRUs

6.2 Implementing RNNs and LSTMs in TensorFlow, Keras, and PyTorch

6.3 Applications of RNNs in Natural Language Processing

6.4 Transformer Networks for Sequence Modeling

Practical Exercises Chapter 6

Quiz Part 2: Advanced Deep Learning Frameworks

Chapter 4: Deep Learning with PyTorch

Chapter 5: Convolutional Neural Networks (CNNs)

Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs

Answers

Chapter 7: Advanced Deep Learning Concepts

7.1 Autoencoders and Variational Autoencoders (VAEs)

7.2 Generative Adversarial Networks (GANs) and Their Applications

7.3 Transfer Learning and Fine-Tuning Pretrained Networks

7.4 Self-Supervised Learning and Foundation Models

Practical Exercises Chapter 7

Chapter 8: Machine Learning in the Cloud and Edge Computing

8.1 Running Machine Learning Models in the Cloud (AWS, Google Cloud, Azure)

8.2 Introduction to TensorFlow Lite and ONNX for Edge Devices

8.3 Deploying Models to Mobile and Edge Devices

Practical Exercises Chapter 8

Summary Chapter 8

Chapter 9: Practical Projects

9.1 Project 1: Predicting House Prices with Regression

9.2 Project 2: Sentiment Analysis Using Transformer-based Models

9.3 Project 3: Image Classification with CNNs

9.4 Project 4: Time Series Forecasting with LSTMs (Improved)

9.5 Project 5: GAN-based Image Generation

Quiz Part 3: Cutting-Edge AI and Practical Applications

Questions

Answers

Reviews

What our readers are saying about this book

Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.

Recommended by dozens of people
Review from Amazon

Miles

If you’re diving into AI, Deep Learning and AI Superhero is a must-read. This book breaks down complex concepts using TensorFlow, Keras, and PyTorch, making advanced topics like CNNs, RNNs, and Transformers accessible. The included hands-on projects, such as image classification and text generation, help reinforce key lessons. With free access to an e-learning platform and code repositories, it’s an excellent resource for mastering AI. As I’m working to build my own AI skills, this comprehensive guide has been invaluable in understanding neural networks and applying them to real-world challenges—perfect timing with Halloween projects on the horizon! HAHA

Review from Amazon

Joseph

This book offers an in-depth exploration of three pivotal frameworks: TensorFlow, Keras, and PyTorch, making complex concepts accessible and engaging. What sets this guide apart is its practical approach—each chapter is filled with step-by-step tutorials, real-world examples, and exercises that not only illustrate the theories but allow you to apply them immediately.

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