Python Deep Learning: Understand how deep neural networks work and apply them to real-world tasks

[LeakGod]

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python
Key Features
Understand the theory, mathematical foundations and the structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them on various computer vision and natural language processing problems
Book Description
The field of deep learning has developed rapidly in the past years and today covers a broad range of applications. This makes it challenging to navigate and difficult to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.
The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.
The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We'll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.
By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
What you will learn
Establish theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Become well versed with natural language processing and recurrent networks
Explore the attention mechanism and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who this book is for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

Learn how to effectively navigate neural networks, including convolutions and transformers, to solve computer vision and NLP problems using Python
Key features
Understand the theory, mathematics, and structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them to solving various problems in computer vision and natural language processing
Book Description
The field of deep learning has developed rapidly in recent years and today covers a wide range of applications. This makes it difficult to navigate and difficult to understand without a solid foundation. This book will take you from the basics of neural networks to the modern models of large languages used today.
The first part of the book introduces the basic concepts and paradigms of machine learning. It covers the mathematical foundations, structure and training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We will learn how to solve problems in image classification, object detection, instance segmentation, and image generation.
The third part is devoted to the attention mechanism and transformers - the basic network architecture of large language models. We'll discuss the new types of advanced problems they can solve, such as chatbots and text-to-image conversion.
By the end of this book, you will have a thorough understanding of the inner workings of deep neural networks. You will have the opportunity to develop new models or adapt existing ones to solve your problems. You will also have enough knowledge to continue your research and keep up to date with the latest developments in the field.
What you will learn
Create the theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Have a good understanding of natural language processing and recurrent networks
Explore attention mechanisms and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples using PyTorch, Keras and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who is this book for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians and anyone who is interested in deep learning. Prior Python programming experience is a must.​

To see this hidden content, you must reply and react with one of the following reactions : Like, Love, Haha, Wow

 

[karpy]

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python
Key Features
Understand the theory, mathematical foundations and the structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them on various computer vision and natural language processing problems
Book Description
The field of deep learning has developed rapidly in the past years and today covers a broad range of applications. This makes it challenging to navigate and difficult to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.
The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.
The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We'll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.
By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
What you will learn
Establish theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Become well versed with natural language processing and recurrent networks
Explore the attention mechanism and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who this book is for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

Learn how to effectively navigate neural networks, including convolutions and transformers, to solve computer vision and NLP problems using Python
Key features
Understand the theory, mathematics, and structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them to solving various problems in computer vision and natural language processing
Book Description
The field of deep learning has developed rapidly in recent years and today covers a wide range of applications. This makes it difficult to navigate and difficult to understand without a solid foundation. This book will take you from the basics of neural networks to the modern models of large languages used today.
The first part of the book introduces the basic concepts and paradigms of machine learning. It covers the mathematical foundations, structure and training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We will learn how to solve problems in image classification, object detection, instance segmentation, and image generation.
The third part is devoted to the attention mechanism and transformers - the basic network architecture of large language models. We'll discuss the new types of advanced problems they can solve, such as chatbots and text-to-image conversion.
By the end of this book, you will have a thorough understanding of the inner workings of deep neural networks. You will have the opportunity to develop new models or adapt existing ones to solve your problems. You will also have enough knowledge to continue your research and keep up to date with the latest developments in the field.
What you will learn
Create the theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Have a good understanding of natural language processing and recurrent networks
Explore attention mechanisms and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples using PyTorch, Keras and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who is this book for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians and anyone who is interested in deep learning. Prior Python programming experience is a must.​

[Hidden content]

 

[kappaunvaible]

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python
Key Features
Understand the theory, mathematical foundations and the structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them on various computer vision and natural language processing problems
Book Description
The field of deep learning has developed rapidly in the past years and today covers a broad range of applications. This makes it challenging to navigate and difficult to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.
The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.
The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We'll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.
By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
What you will learn
Establish theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Become well versed with natural language processing and recurrent networks
Explore the attention mechanism and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who this book is for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

Learn how to effectively navigate neural networks, including convolutions and transformers, to solve computer vision and NLP problems using Python
Key features
Understand the theory, mathematics, and structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them to solving various problems in computer vision and natural language processing
Book Description
The field of deep learning has developed rapidly in recent years and today covers a wide range of applications. This makes it difficult to navigate and difficult to understand without a solid foundation. This book will take you from the basics of neural networks to the modern models of large languages used today.
The first part of the book introduces the basic concepts and paradigms of machine learning. It covers the mathematical foundations, structure and training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We will learn how to solve problems in image classification, object detection, instance segmentation, and image generation.
The third part is devoted to the attention mechanism and transformers - the basic network architecture of large language models. We'll discuss the new types of advanced problems they can solve, such as chatbots and text-to-image conversion.
By the end of this book, you will have a thorough understanding of the inner workings of deep neural networks. You will have the opportunity to develop new models or adapt existing ones to solve your problems. You will also have enough knowledge to continue your research and keep up to date with the latest developments in the field.
What you will learn
Create the theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Have a good understanding of natural language processing and recurrent networks
Explore attention mechanisms and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples using PyTorch, Keras and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who is this book for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians and anyone who is interested in deep learning. Prior Python programming experience is a must.​

[Hidden content]

 

[hhghghg]

gg

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python
Key Features
Understand the theory, mathematical foundations and the structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them on various computer vision and natural language processing problems
Book Description
The field of deep learning has developed rapidly in the past years and today covers a broad range of applications. This makes it challenging to navigate and difficult to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.
The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.
The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We'll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.
By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
What you will learn
Establish theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Become well versed with natural language processing and recurrent networks
Explore the attention mechanism and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who this book is for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

Learn how to effectively navigate neural networks, including convolutions and transformers, to solve computer vision and NLP problems using Python
Key features
Understand the theory, mathematics, and structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks
Learn how to apply them to solving various problems in computer vision and natural language processing
Book Description
The field of deep learning has developed rapidly in recent years and today covers a wide range of applications. This makes it difficult to navigate and difficult to understand without a solid foundation. This book will take you from the basics of neural networks to the modern models of large languages used today.
The first part of the book introduces the basic concepts and paradigms of machine learning. It covers the mathematical foundations, structure and training algorithms of neural networks and dives into the essence of deep learning.
The second part of the book introduces convolutional networks for computer vision. We will learn how to solve problems in image classification, object detection, instance segmentation, and image generation.
The third part is devoted to the attention mechanism and transformers - the basic network architecture of large language models. We'll discuss the new types of advanced problems they can solve, such as chatbots and text-to-image conversion.
By the end of this book, you will have a thorough understanding of the inner workings of deep neural networks. You will have the opportunity to develop new models or adapt existing ones to solve your problems. You will also have enough knowledge to continue your research and keep up to date with the latest developments in the field.
What you will learn
Create the theoretical foundations of deep neural networks
Understand convolutional networks and apply them in computer vision applications
Have a good understanding of natural language processing and recurrent networks
Explore attention mechanisms and transformers
Apply transformers and large language models for natural language and computer vision
Implement coding examples using PyTorch, Keras and Hugging Face Transformers
Use MLOps to develop and deploy neural network models
Who is this book for
This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians and anyone who is interested in deep learning. Prior Python programming experience is a must.​

[Hidden content]

gg