Publisher's Synopsis
Deep Learning with Python: Build Neural Networks and AI Models from Scratch is a comprehensive, hands-on guide to mastering deep learning and neural network training using Python. Whether you're a beginner looking to dive into AI or an experienced practitioner seeking to improve your skills, this book will walk you through the concepts and tools needed to build deep learning models from scratch.
Focusing on frameworks like TensorFlow and PyTorch, this book provides practical insights into developing, training, and deploying powerful neural networks. With clear explanations, step-by-step instructions, and real-world examples, you'll learn how to implement advanced AI models that can be applied to a wide range of problems in industries such as healthcare, finance, and more.
Inside, you'll discover:
- Introduction to Deep Learning and Neural Networks: Learn the fundamentals of deep learning, neural networks, and the key components involved in building AI models. Understand the differences between shallow learning and deep learning, and the advantages of using deep neural networks for complex tasks.
- Setting Up Python for Deep Learning: Get started with the necessary tools and libraries, including TensorFlow, PyTorch, and Keras. Learn how to install and configure the tools, and understand the basics of Python for machine learning and deep learning.
- Building Your First Neural Network: Learn how to design and implement a simple feedforward neural network using TensorFlow and PyTorch. Discover how to train your network using backpropagation and gradient descent techniques.
- Activation Functions and Optimization: Explore the role of activation functions like ReLU, Sigmoid, and Tanh in neural networks, and learn how to optimize your models with techniques such as stochastic gradient descent, Adam, and more.
- Convolutional Neural Networks (CNNs): Dive into CNNs and learn how they are used for image recognition and computer vision tasks. Implement a CNN for tasks like object detection and image classification using TensorFlow and PyTorch.
- Recurrent Neural Networks (RNNs) and LSTMs: Understand how RNNs and Long Short-Term Memory (LSTM) networks are used for sequence data, such as time series forecasting and natural language processing. Learn how to implement and train these models for tasks like sentiment analysis and speech recognition.
- Transfer Learning and Pre-trained Models: Discover the power of transfer learning and how to leverage pre-trained models to build deep learning applications with less data and faster training times. Learn how to fine-tune models like VGG16, ResNet, and BERT for your specific needs.
- Regularization and Avoiding Overfitting: Learn techniques like dropout, batch normalization, and early stopping to prevent overfitting in your models. Understand how to improve the generalization of your neural networks for real-world applications.
- Model Evaluation and Fine-Tuning: Master the art of model evaluation using metrics like accuracy, precision, recall, and F1-score. Learn how to tune hyperparameters and optimize your deep learning models for better performance.
- Deploying Deep Learning Models: Learn how to deploy your trained deep learning models into production environments. Explore techniques for model saving, serving, and using cloud platforms like AWS and Google Cloud for model deployment.
- Practical Applications of Deep Learning: Gain hands-on experience with real-world deep learning applications, including image classification, sentiment analysis, stock price prediction, and healthcare diagnostics.
By the end of this book, you'll have the skills to build and train complex neural networks and AI models from scratch. You'll be ready to apply deep learning to solve real-world problems and explore new AI possibilities.
