Applied Deep Learning & Neural Network: Practical AI Mastery
Applied Deep Learning & Neural Network: Practical AI Mastery, available at $19.99, has an average rating of 4.17, with 81 lectures, based on 9 reviews, and has 4707 subscribers.
You will learn about Grasp Fundamental Concepts: Understand the foundational principles of machine learning, delve into popular methods, and explore the core ideas behind DL Hands-on Coding Experience: Gain practical coding skills using platforms like Jupyter Notebooks, Google Colab, and PyTorch, applying theoretical knowledge Neural Network Proficiency: Develop a deep understanding of neural networks, their structures, and applications, laying the groundwork for advanced topics Transfer Learning Applications: Explore transfer learning, applying pre-trained models to new tasks, and work with datasets like CIFAR-10 Text-Based Applications: Extend skills to text classification and generation, harnessing the power of convolutional neural networks, transformer architecture Text Translation and Beyond: Master text translation using encoder-decoder architectures and explore diverse applications, including tabular data prediction Real-World Project Implementation: Apply acquired knowledge to real-world projects, such as image classification and text translation, honing skills Practical Recommendations: Benefit from insights into best practices, recommendations, and efficient learning strategies, ensuring a productive understanding By the end of the course, students will possess the practical skills and theoretical knowledge necessary to navigate the dynamic landscape of deep learning This course is ideal for individuals who are Aspiring Data Scientists: Individuals seeking to enter the field of data science and machine learning, aiming to build practical skills and gain hands-on experience. or AI Enthusiasts: Those passionate about artificial intelligence, looking to deepen their understanding of deep learning, neural networks, and practical applications in real-world projects. or Programmers and Developers: Professionals with a programming background interested in expanding their expertise to include deep learning and neural network applications. or Tech Professionals: Individuals in technology-related fields who want to stay updated with the latest advancements in AI and enhance their proficiency in applied deep learning. or Students and Researchers: Students pursuing degrees in computer science, engineering, or related fields, as well as researchers interested in practical applications of deep learning techniques. or Professionals Seeking Career Transition: Individuals aiming to transition into roles focused on machine learning, AI, or data science, and wish to acquire practical skills for successful career shifts. It is particularly useful for Aspiring Data Scientists: Individuals seeking to enter the field of data science and machine learning, aiming to build practical skills and gain hands-on experience. or AI Enthusiasts: Those passionate about artificial intelligence, looking to deepen their understanding of deep learning, neural networks, and practical applications in real-world projects. or Programmers and Developers: Professionals with a programming background interested in expanding their expertise to include deep learning and neural network applications. or Tech Professionals: Individuals in technology-related fields who want to stay updated with the latest advancements in AI and enhance their proficiency in applied deep learning. or Students and Researchers: Students pursuing degrees in computer science, engineering, or related fields, as well as researchers interested in practical applications of deep learning techniques. or Professionals Seeking Career Transition: Individuals aiming to transition into roles focused on machine learning, AI, or data science, and wish to acquire practical skills for successful career shifts.
Enroll now: Applied Deep Learning & Neural Network: Practical AI Mastery
Summary
Title: Applied Deep Learning & Neural Network: Practical AI Mastery
Price: $19.99
Average Rating: 4.17
Number of Lectures: 81
Number of Published Lectures: 81
Number of Curriculum Items: 81
Number of Published Curriculum Objects: 81
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Grasp Fundamental Concepts: Understand the foundational principles of machine learning, delve into popular methods, and explore the core ideas behind DL
- Hands-on Coding Experience: Gain practical coding skills using platforms like Jupyter Notebooks, Google Colab, and PyTorch, applying theoretical knowledge
- Neural Network Proficiency: Develop a deep understanding of neural networks, their structures, and applications, laying the groundwork for advanced topics
- Transfer Learning Applications: Explore transfer learning, applying pre-trained models to new tasks, and work with datasets like CIFAR-10
- Text-Based Applications: Extend skills to text classification and generation, harnessing the power of convolutional neural networks, transformer architecture
- Text Translation and Beyond: Master text translation using encoder-decoder architectures and explore diverse applications, including tabular data prediction
- Real-World Project Implementation: Apply acquired knowledge to real-world projects, such as image classification and text translation, honing skills
- Practical Recommendations: Benefit from insights into best practices, recommendations, and efficient learning strategies, ensuring a productive understanding
- By the end of the course, students will possess the practical skills and theoretical knowledge necessary to navigate the dynamic landscape of deep learning
Who Should Attend
- Aspiring Data Scientists: Individuals seeking to enter the field of data science and machine learning, aiming to build practical skills and gain hands-on experience.
- AI Enthusiasts: Those passionate about artificial intelligence, looking to deepen their understanding of deep learning, neural networks, and practical applications in real-world projects.
- Programmers and Developers: Professionals with a programming background interested in expanding their expertise to include deep learning and neural network applications.
- Tech Professionals: Individuals in technology-related fields who want to stay updated with the latest advancements in AI and enhance their proficiency in applied deep learning.
- Students and Researchers: Students pursuing degrees in computer science, engineering, or related fields, as well as researchers interested in practical applications of deep learning techniques.
- Professionals Seeking Career Transition: Individuals aiming to transition into roles focused on machine learning, AI, or data science, and wish to acquire practical skills for successful career shifts.
Target Audiences
- Aspiring Data Scientists: Individuals seeking to enter the field of data science and machine learning, aiming to build practical skills and gain hands-on experience.
- AI Enthusiasts: Those passionate about artificial intelligence, looking to deepen their understanding of deep learning, neural networks, and practical applications in real-world projects.
- Programmers and Developers: Professionals with a programming background interested in expanding their expertise to include deep learning and neural network applications.
- Tech Professionals: Individuals in technology-related fields who want to stay updated with the latest advancements in AI and enhance their proficiency in applied deep learning.
- Students and Researchers: Students pursuing degrees in computer science, engineering, or related fields, as well as researchers interested in practical applications of deep learning techniques.
- Professionals Seeking Career Transition: Individuals aiming to transition into roles focused on machine learning, AI, or data science, and wish to acquire practical skills for successful career shifts.
Embark on a transformative learning experience that demystifies the complex world of deep learning. This hands-on course is designed to equip you with practical skills, enabling you to navigate the realms of machine learning and dive deep into the applications of neural networks.
Embark on a comprehensive exploration of deep learning in our hands-on course. Begin with an introduction to the practical aspects of deep learning, paving the way for a profound understanding of its applications.
Discover the fundamental principles of machine learning in Lecture 2, setting the stage for an in-depth journey into the intricacies of deep learning methodologies. Gain insights into popular machine learning methods and their relevance in real-world scenarios.
As you progress, delve into the core concepts of deep learning, understanding its definition, unique features, and widespread applications across various domains. Explore recommendations and best practices for effective learning in the realm of deep learning.
Delve into the basic concepts of deep learning, including perception and the structure of neural networks. Understand the universal approximations theorem, providing a theoretical foundation for the capabilities of deep neural networks.
Practical aspects come to the forefront as you explore where to write code, with a focus on Jupyter Notebooks, Google Colab, and the PyTorch library. Dive into the fundamentals of tensors, gradients, and their applications in machine learning.
Explore a hands-on example with the MNIST dataset, gaining practical experience in working with image data and building neural networks. Transition to transfer learning, understanding its principles and applying them to real-world datasets like CIFAR-10.
Conclude this section by delving into image classification using convolutional neural networks (CNNs) on datasets like CIFAR-10. From data preparation to model training and evaluation, develop the skills needed to apply deep learning to diverse image-based tasks.
Extend your knowledge to text-based applications, starting with text classification using CNNs. Continue with text generation using transformers, gaining insights into their architectures and applications in natural language processing.
Explore text translation using encoder-decoder architectures, covering essential components like attention mechanisms. Develop practical skills in training and evaluating models for various tasks, including tabular data prediction and collaborative filtering for recommendations.
In this comprehensive curriculum, each topic builds upon the last, ensuring a well-rounded understanding of deep learning principles and their practical applications across different domains.
Introduction to Hands-on Deep Learning (Lecture 1): Get ready to immerse yourself in the fascinating field of deep learning. This course goes beyond theoretical concepts, offering a hands-on approach that ensures you not only understand the principles but can apply them effectively.
Understanding Machine Learning (Lecture 2): Before delving into deep learning, lay the groundwork with a comprehensive overview of machine learning. Gain insights into popular methods that form the foundation for advanced concepts explored later in the course.
Foundations of Deep Learning (Lecture 4): Discover the essence of deep learning, unraveling its core principles and unique characteristics. Explore its broad applications, from image and speech recognition to recommendation systems and text processing.
Recommendations and Best Practices (Lecture 6): Benefit from valuable recommendations and best practices that guide your learning journey. Navigate the intricate landscape of deep learning with insights that ensure a fruitful and efficient learning experience.
Basic Concepts of Deep Learning (Lecture 7): Grasp the fundamental concepts that underpin deep learning, including the perception and structure of neural networks. Lay the theoretical foundation for hands-on exercises and practical applications.
Where to Write Code (Lecture 14): Enter the practical realm with guidance on where to write code. Explore platforms like Jupyter Notebooks, Google Colab, and dive into PyTorch, setting the stage for interactive and effective coding experiences.
Tensors, Gradients, and MNIST Example (Lectures 18-22): Build your coding proficiency with a focus on tensors, gradients, and practical examples using the MNIST dataset. Gain hands-on experience in manipulating data and constructing neural networks.
Transfer Learning and Image Classification (Lectures 25-38): Transition into transfer learning and apply it to real-world datasets, such as CIFAR-10. Move beyond theory to practical implementation, including data preparation, model building, and performance evaluation.
Text Classification and Generation (Lectures 47-63): Extend your skills to text-based applications, from classification to generation. Dive into convolutional neural networks for text classification and explore the transformative power of transformer architectures.
Text Translation and Beyond (Lectures 64-81): Master text translation using encoder-decoder architectures and delve into diverse applications, including tabular data prediction and collaborative filtering. The course concludes with a broad understanding of deep learning’s versatile applications.
Embark on this enriching journey, where theoretical understanding meets hands-on proficiency, ensuring you emerge with the skills to tackle real-world challenges in the dynamic field of deep learning. Welcome to a course that empowers your journey into the heart of artificial intelligence.
Course Curriculum
Chapter 1: Curriculum
Lecture 1: Introduction to Hands on Deeplearning
Lecture 2: What is Machine Learning
Lecture 3: Popular ML Methods
Lecture 4: What is Deep Learning
Lecture 5: Applications of Deeplearning
Lecture 6: Recommendations
Lecture 7: Basic Concept of Deeplearning
Lecture 8: Perception
Lecture 9: Neural Network
Lecture 10: Universal Approximations Theorem
Lecture 11: Deep Neural Network
Lecture 12: Deep Neural Network Continue
Lecture 13: Getting Started
Lecture 14: Where to write Code
Lecture 15: Jupiter Notebook
Lecture 16: Google Colab
Lecture 17: Pytorch
Lecture 18: Tensors
Lecture 19: Tensors Continue
Lecture 20: Gradients
Lecture 21: MNIST Example
Lecture 22: Check Sample
Lecture 23: Hidden Layer
Lecture 24: Interface on a Digit
Lecture 25: Transfer-Learning-Overview
Lecture 26: What is Transfer Learning
Lecture 27: CS231n Convolutional Neural Networks
Lecture 28: Download Dataset
Lecture 29: Transform the Data
Lecture 30: Visualize the Data
Lecture 31: Define the Model
Lecture 32: Add a Few Final Layers
Lecture 33: Train the Model
Lecture 34: Test the Model
Lecture 35: What About CIFAR
Lecture 36: Image Classifier on Cifar 10 Dataset
Lecture 37: Download and Load Our Dataset
Lecture 38: Train and Test Dataset
Lecture 39: Define Our Neural Network
Lecture 40: Working on Image
Lecture 41: Input and Output
Lecture 42: Define Our Loss Function
Lecture 43: Train Data in Enumerate
Lecture 44: Train Data in Enumerate Continue
Lecture 45: Test the Neural Network on the Test Image
Lecture 46: Intro to Text Classifier
Lecture 47: Text Classification Using CNN
Lecture 48: Prepare the Data
Lecture 49: Build the Model
Lecture 50: Build the Model Coninue
Lecture 51: More on Build the Model
Lecture 52: Define a Loss Function
Lecture 53: Define a Loss Function Continue
Lecture 54: More on Define a Loss Function
Lecture 55: Evaluate or Test the Model
Lecture 56: Intro to Text Generation
Lecture 57: Text Generation-Transformers
Lecture 58: Text Generation-Transformers Continue
Lecture 59: Transformers-Architectures
Lecture 60: Transformers-Architectures Cintinue
Lecture 61: Word-Generation
Lecture 62: Word-Generation Continue
Lecture 63: Text-Generation
Lecture 64: Intro to Text Translation
Lecture 65: Loading-Data
Lecture 66: Preparing-Data
Lecture 67: Encoder-Attention Part 1
Lecture 68: Encoder-Attention Part 2
Lecture 69: Encoder-Attention Part 3
Lecture 70: Decoder
Lecture 71: Train-Eval-Functions
Lecture 72: Train-Eval-Functions Continue
Lecture 73: Training-Fixes
Lecture 74: Training-Evaluation
Lecture 75: Prediction-Tabular-Data Part 1
Lecture 76: Prediction-Tabular-Data Part 2
Lecture 77: Prediction-Tabular-Data Part 3
Lecture 78: Prediction-Tabular-Data Part 4
Lecture 79: Collaborative Filtering
Lecture 80: Collaborative Filtering Continue
Lecture 81: Other Recommendation Approaches
Instructors
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EDUCBA Bridging the Gap
Learn real world skills online
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- 4 stars: 5 votes
- 5 stars: 2 votes
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