Deep Learning with PyTorch : Image Segmentation

 

Deep Learning with PyTorch: Unveiling Image Segmentation

Mastering Image Segmentation Using PyTorch through Hands-on Learning

Introduction

Image segmentation is a critical task in computer vision that goes beyond classifying images. Instead of recognizing an object as a whole, segmentation involves identifying individual pixels belonging to each object, enabling applications in autonomous vehicles, medical imaging, and beyond. In hands-on project “Deep Learning with PyTorch: Image Segmentation,” learners explore the concepts and implementation of semantic segmentation using the power of PyTorch, a popular deep learning framework.

This blog takes you through the key highlights of the course and the insights you'll gain from participating in the project.

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What is Image Segmentation?

At its core, image segmentation is about partitioning an image into multiple segments, where each pixel is assigned to a specific class or object. It generally comes in two primary types:

• Semantic segmentation: Assigns a label to every pixel, but objects of the same class (e.g., all cars) are treated identically.
• Instance segmentation: Differentiates individual objects, even if they belong to the same class.

Some real-world applications include:

• Autonomous vehicles: Identifying roads, pedestrians, and obstacles.
• Medical diagnosis: Locating tumors or abnormalities in MRI or X-ray images.
• Satellite imagery: Distinguishing between forests, cities, and water bodies.

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Overview of the Course

This project provides a beginner-friendly introduction to building an image segmentation model using PyTorch. In just two hours, you’ll go through the entire process of preparing data, building the segmentation network, and training it for meaningful results.

What You'll Learn

1. PyTorch Basics:

   • Getting comfortable with PyTorch operations and tensors.
   • Understanding how neural networks are defined and trained.

2. Building a Segmentation Network:

   • Using U-Net, a well-known architecture for image segmentation tasks. U-Net is known for its ability to capture both global and local features, making it suitable for medical imaging and other pixel-based predictions.

3. Training and Evaluation:

   • Implementing the loss function to quantify segmentation errors.
   • Measuring accuracy using metrics like Intersection-over-Union (IoU).

4. Data Preparation:

   • Loading and preprocessing images and labels.
   • Working with image masks where each pixel’s value represents a class.

5. Visualizing Results:

   • Generating segmentation masks to compare predicted vs. actual outputs visually.

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Why PyTorch for Image Segmentation?

PyTorch stands out for its flexibility, dynamic computation graphs, and strong support from the research community. For image segmentation, PyTorch offers several advantages:

• Customizability: Build and tweak models without extensive boilerplate code.
• Pre-trained Models: Access to pre-trained segmentation models via TorchVision.
• Rich Ecosystem: PyTorch integrates well with tools like TensorBoard for visualization and Hugging Face for additional resources.

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Hands-on Approach to Learning

This project emphasizes practical, hands-on learning through a guided interface. You’ll build and train a model directly in your browser using cloud workspace—no need for separate installations! This project is especially helpful for those looking to:

• Get a quick introduction to PyTorch without diving into lengthy tutorials.
• Understand real-world workflows for image segmentation tasks.
• Explore how to prepare custom datasets for pixel-wise predictions.

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Key Takeaways

By the end of this project, you will:

• Understand the concepts behind image segmentation and its applications.
• Know how to build a segmentation model from scratch using PyTorch.
• Be equipped with the knowledge to train and evaluate deep learning models for pixel-based tasks.

Whether you're a data science enthusiast, a student, or a professional exploring computer vision, this project provides a solid introduction to image segmentation and PyTorch fundamentals. With the knowledge gained, you can take on more advanced tasks like object detection, instance segmentation, and multi-class semantic segmentation.

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Next Steps

Once you complete the project, consider:

• Exploring advanced architectures such as Mask R-CNN for instance segmentation.
• Working with larger datasets like COCO or Cityscapes.
• Building your own end-to-end computer vision applications using PyTorch.

“Deep Learning with PyTorch: Image Segmentation” serves as a launching pad into the fascinating world of computer vision. If you're ready to dive in, enroll now and start your journey toward mastering segmentation!

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Final Thoughts

Image segmentation is not just a technical task—it’s an essential component in making AI systems understand the world at a granular level. This project will enable you to explore the magic of deep learning applied to computer vision, paving the way for both academic research and industry projects. With PyTorch in hand, the only limit is your imagination!

Join Free: Deep Learning with PyTorch : Image Segmentation

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DeepLearning.AI TensorFlow Developer Professional Certificate

 

Master AI Development with TensorFlow: A Guide to Coursera's TensorFlow in Practice Professional Certificate 🧠🚀

Introduction

Machine learning is revolutionizing industries, and TensorFlow has become one of the most widely used frameworks for building intelligent systems. If you’re ready to dive deep into AI and enhance your machine learning skills, the TensorFlow in Practice Professional Certificate on Coursera is a great place to start. Whether you're a data enthusiast or aspiring ML engineer, this certificate equips you with the right skills to build, train, and deploy cutting-edge neural networks.

In this blog, we’ll take a closer look at what this certificate offers, why it matters, and how it can boost your career. 👇

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📋 What is the TensorFlow in Practice Certificate?

This four-course series, developed by deeplearning.ai, focuses on mastering TensorFlow—a powerful open-source platform for building machine learning models. You’ll learn how to apply deep learning algorithms, work with large datasets, and design AI models that can handle real-world tasks like image recognition and natural language processing (NLP).

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📚 What You’ll Learn

1. Introduction to TensorFlow for AI, ML, and DL

   • Start with the fundamentals of TensorFlow.
   • Learn to implement basic neural networks.
   • Explore computer vision concepts for image recognition.

2. Convolutional Neural Networks (CNNs) in TensorFlow

   • Understand how CNNs power applications like facial recognition and image classification.
   • Build CNNs for practical projects, including data from real-world images.

3. Natural Language Processing in TensorFlow

   • Explore Recurrent Neural Networks (RNNs) and LSTMs to handle sequential data.
   • Apply NLP techniques to sentiment analysis, text generation, and more.

4. Sequences, Time Series, and Prediction

   • Work with time-series data for forecasting and predictive models.
   • Build LSTM networks and other advanced models to capture temporal patterns.

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💼 Why Should You Take This Course?

This certification not only teaches TensorFlow, but also covers essential deep learning concepts that are in high demand today. Here are a few benefits:

• Hands-on Projects: Work with real datasets and practical AI scenarios.
• Career Boost: TensorFlow is widely used by Google, Uber, Twitter, and more—making this a valuable skill.
• Job-Ready Skills: Prepare for roles like Machine Learning Engineer or Data Scientist.

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🕒 Time Commitment

• 4 Courses (About 1 month per course, working part-time)
• Completely Online: Learn at your own pace with flexible deadlines.

With a total commitment of approximately 4 months, this program is ideal for busy professionals and students alike.

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🌟 Who is it For?

This certificate is for anyone interested in building AI systems using TensorFlow, including:

• Data Scientists and Machine Learning Engineers
• Software Developers expanding into AI
• AI enthusiasts looking to build real-world projects

Basic Python programming skills are recommended before starting. If you’re already familiar with neural networks, you’ll get a chance to deepen your understanding and apply your knowledge more effectively.

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🎯 How to Enroll

Enrollment is open year-round on Coursera, and financial aid is available. Upon completing the program, you’ll earn a professional certificate from Coursera and deeplearning.ai—a valuable credential to showcase on LinkedIn.

🔗 Enroll Now: TensorFlow in Practice Certificat

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✨ Final Thoughts

As AI and machine learning become increasingly essential across industries, mastering TensorFlow gives you a competitive edge. The TensorFlow in Practice Professional Certificate offers a perfect blend of theory and practice, empowering you to create AI solutions for real-world challenges.

Whether you’re an aspiring ML engineer or a developer looking to enhance your skills, this certificate will set you on the right track for success.

Join Free: DeepLearning.AI TensorFlow Developer Professional Certificate

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Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

 

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

- Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models

- Build solutions for real-world computer vision problems using PyTorch

- All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production.

By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

- Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer

- Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks

- Implement multi-object detection and segmentation

- Leverage foundation models to perform object detection and segmentation without any training data points

- Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

Table of Contents

- Artificial Neural Network Fundamentals

- PyTorch Fundamentals

- Building a Deep Neural Network with PyTorch

- Introducing Convolutional Neural Networks

- Transfer Learning for Image Classification

- Practical Aspects of Image Classification

- Basics of Object Detection

- Advanced Object Detection

- Image Segmentation

- Applications of Object Detection and Segmentation

- Autoencoders and Image Manipulation

- Image Generation Using GANs

SOFT Copy: Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Hard Copy: Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition by V Kishore Ayyadevara (Author), Yeshwanth Reddy (Author)

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Mastering PyTorch - Second Edition: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

 

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples

Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features:

- Understand how to use PyTorch to build advanced neural network models

- Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker

- Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Book Description:

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most from your data and build complex neural network models.

You'll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You'll deploy PyTorch models to production, including mobile devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What You Will Learn:

- Implement text, vision, and music generating models using PyTorch

- Build a deep Q-network (DQN) model in PyTorch

- Deploy PyTorch models on mobile devices (Android and iOS)

- Become well-versed with rapid prototyping using PyTorch with fast.ai

- Perform neural architecture search effectively using AutoML

- Easily interpret machine learning models using Captum

- Design ResNets, LSTMs, and graph neural networks (GNNs)

- Create language and vision transformer models using Hugging Face

Who this book is for:

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Hard Copy: Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Deep Learning with Python: A Comprehensive guide to Building and Training Deep Neural Networks using Python and popular Deep Learning Frameworks

 

Deep Learning with Python is a comprehensive guide to building and training deep neural networks using Python and popular deep learning frameworks. Whether you are a beginner or an experienced data scientist, this book provides a detailed understanding of the theory and practical implementation of deep learning.

Starting with an introduction to deep learning, the book covers essential topics such as neural network architecture, training and optimization, regularization, and transfer learning. It also covers popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.

The book includes practical examples and step-by-step instructions to help you build and train deep neural networks for a variety of applications, including image and speech recognition, natural language processing, and time series analysis. You will also learn how to use advanced techniques such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

With its comprehensive coverage of deep learning and practical examples, this book is an essential resource for anyone interested in building and training deep neural networks using Python and popular deep learning frameworks.

Hard Copy: Deep Learning with Python: A Comprehensive guide to Building and Training Deep Neural Networks using Python and popular Deep Learning Frameworks

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Deep Learning with Python, Second Edition

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn:

    Deep learning from first principles

    Image classification & image segmentation

    Timeseries forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science. 

About the book

Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications.

What's inside

    Deep learning from first principles

    Image classification and image segmentation

    Time series forecasting

    Text classification and machine translation

    Text generation, neural style transfer, and image generation

About the reader

For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the author

François Chollet is a software engineer at Google and creator of the Keras deep-learning library.

Table of Contents

1  What is deep learning?

2 The mathematical building blocks of neural networks

3 Introduction to Keras and TensorFlow

4 Getting started with neural networks: Classification and regression

5 Fundamentals of machine learning

6 The universal workflow of machine learning

7 Working with Keras: A deep dive

8 Introduction to deep learning for computer vision

9 Advanced deep learning for computer vision

10 Deep learning for timeseries

11 Deep learning for text

12 Generative deep learning

13 Best practices for the real world

14 Conclusions

Hard Copy: Deep Learning with Python, Second Edition

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AI for Medicine Specialization

 

What you'll learn

Diagnose diseases from x-rays and 3D MRI brain images

Predict patient survival rates more accurately using tree-based models

Estimate treatment effects on patients using data from randomized trials

Automate the task of labeling medical datasets using natural language processing

Join Free:AI for Medicine Specialization

Specialization - 3 course series

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases.  If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the 

Deep Learning Specialization

Applied Learning Project

Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates. You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.

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Linear Regression with Python

 

What you'll learn

Create a linear model, and implement gradient descent.

Train the linear model to fit given data using gradient descent.

Join Free:Linear Regression with Python

About this Guided Project

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.

Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Neural Networks and Deep Learning

 

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Neural Networks and Deep Learning

There are 4 modules in this course

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. 

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

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The Little Book of Deep Learning François Fleuret (Free PDF)

 

Unlock the Power of Deep Learning Embark on an extraordinary journey into the realm of cutting-edge technology with The Little Book of Deep Learning . Discover the secrets behind one of the most revolutionary advancements of our time, and witness how it is transforming industries across the globe. Explore the intricate world of neural networks and artificial intelligence as you delve into the minds of experts and pioneers. Gain unparalleled insights into the principles, algorithms, and applications of deep learning, unraveling complex concepts with ease. From image recognition to natural language processing, uncover the limitless possibilities that await you within these pages. Witness how deep learning is reshaping medicine, finance, entertainment, and more, igniting a new era of innovation. Written by a leading authority in the field, this captivating book distills the essence of deep learning, providing a comprehensive yet accessible guide for both beginners and seasoned professionals. Its engaging narrative and practical examples will empower you to harness the true potential of this transformative technology. Don't miss your chance to join the ranks of those who have unlocked the power of deep learning. Whether you're a student, researcher, or industry enthusiast, The Little Book of Deep Learning is your gateway to a world of unlimited possibilities.

Buy : The Little Book of Deep Learning

PDF Download : The Little Book of Deep Learning Free PDF

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The Principles of Deep Learning Theory (Free PDF)

 

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus. informal probability theory. it can easily fill a semester long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning. 

Book Buy : The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks

Book Main page : https://arxiv.org/abs/2106.10165

PDF Link : https://arxiv.org/pdf/2106.10165.pdf

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Dive into Deep Learning (Free PDF)

 

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required―every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.

Buy : Dive into Deep Learning

PDF Download : Dive into Deep Learning (Free PDF)

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Deep Learning Specialization

 

What you'll learn

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications

Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data

Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Specialization - 5 course series

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. 

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Applied Learning Project

By the end you’ll be able to:

 • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

JOIN Free - Deep Learning Specialization

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