Mastering Deep Learning for Generative AI

Mastering Deep Learning for Generative AI, available at $54.99, has an average rating of 5, with 33 lectures, based on 1 reviews, and has 1192 subscribers.

You will learn about Machine Learning Enthusiasts: Expand your skillset by mastering deep learning techniques specifically used for generative models. AI Developers & Researchers: Gain the expertise to build and experiment with advanced Generative AI models for various applications. Data Scientists with Ambition: Sharpen your ability to design, train, and deploy cutting-edge Generative AI systems. Evaluate and improve the performance of deep learning models for generative AI. This course is ideal for individuals who are Aspiring Data Scientists: Those looking to specialize in deep learning and generative models. or Students and Researchers: Those pursuing advanced studies in AI and looking to expand their knowledge and skills in deep learning. or Tech Enthusiasts: Individuals eager to explore the cutting-edge field of generative AI. or Software Developers: Professionals aiming to integrate generative AI into their projects and enhance their technical skill set. It is particularly useful for Aspiring Data Scientists: Those looking to specialize in deep learning and generative models. or Students and Researchers: Those pursuing advanced studies in AI and looking to expand their knowledge and skills in deep learning. or Tech Enthusiasts: Individuals eager to explore the cutting-edge field of generative AI. or Software Developers: Professionals aiming to integrate generative AI into their projects and enhance their technical skill set.

Enroll now: Mastering Deep Learning for Generative AI

Summary

Title: Mastering Deep Learning for Generative AI

Price: $54.99

Average Rating: 5

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning Enthusiasts: Expand your skillset by mastering deep learning techniques specifically used for generative models.
AI Developers & Researchers: Gain the expertise to build and experiment with advanced Generative AI models for various applications.
Data Scientists with Ambition: Sharpen your ability to design, train, and deploy cutting-edge Generative AI systems.
Evaluate and improve the performance of deep learning models for generative AI.

Who Should Attend

Aspiring Data Scientists: Those looking to specialize in deep learning and generative models.
Students and Researchers: Those pursuing advanced studies in AI and looking to expand their knowledge and skills in deep learning.
Tech Enthusiasts: Individuals eager to explore the cutting-edge field of generative AI.
Software Developers: Professionals aiming to integrate generative AI into their projects and enhance their technical skill set.

Target Audiences

Aspiring Data Scientists: Those looking to specialize in deep learning and generative models.
Students and Researchers: Those pursuing advanced studies in AI and looking to expand their knowledge and skills in deep learning.
Tech Enthusiasts: Individuals eager to explore the cutting-edge field of generative AI.
Software Developers: Professionals aiming to integrate generative AI into their projects and enhance their technical skill set.

Dive into the transformative world of generative AI with “Mastering Deep Learning for Generative AI.” This comprehensive course is designed for aspiring data scientists, tech enthusiasts, and creative professionals eager to harness the power of deep learning to create innovative generative models.

What You’ll Learn:

Foundations of Deep Learning: Understand the core principles of neural networks, including supervised and unsupervised learning.
Generative Models: Master the building and training of advanced generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers.
Hands-On Projects: Engage in practical projects that guide you through creating applications in art, music, text, and design using generative AI.
Model Optimization: Learn techniques to evaluate, improve, and fine-tune the performance of your generative models for real-world applications.
Ethical Considerations: Explore the ethical implications and future impact of generative AI, ensuring responsible and informed application of these technologies.

Course Highlights:

Comprehensive Learning: From fundamentals to advanced concepts, gain a robust understanding of deep learning for generative AI.
Practical Experience: Hands-on projects provide real-world experience, enhancing your ability to apply what you learn.
Cutting-Edge Techniques: Stay ahead with the latest advancements in generative AI technologies.
Expert Guidance: Learn from experienced instructors who provide clear explanations and valuable insights.

Who Should Enroll:

Aspiring Data Scientists: Those looking to specialize in deep learning and generative models.
Tech Enthusiasts: Individuals keen to explore and innovate in the field of AI.
Creative Professionals: Artists, musicians, and designers wanting to integrate AI into their creative processes.
Students and Researchers: Those pursuing advanced studies in AI and seeking to expand their skill set.
Software Developers: Professionals aiming to implement generative AI in their projects and enhance their technical expertise.

Prerequisites:

Basic understanding of programming, preferably in Python.
Familiarity with fundamental machine learning concepts.
A computer with internet access to run deep learning frameworks and tools.
No prior experience with deep learning is required, but it will be beneficial.

Course Outcomes:

By the end of this course, you will:

Have a strong grasp of deep learning and generative AI concepts.
Be able to build, train, and optimize generative models using state-of-the-art frameworks.
Understand the ethical considerations and potential impacts of generative AI.
Be equipped to apply your skills in real-world projects and innovative applications.

Join “Mastering Deep Learning for Generative AI” today and embark on a journey that merges technology with creativity, empowering you to shape the future of AI-driven innovation.

Course Curriculum

Chapter 1: Introduction to Deep Learning Concepts

Lecture 1: The History of Deep Learning and Inspired by Neuroscience

Lecture 2: Understanding Neural Networks: Weights, Multi-Neuron Networks

Lecture 3: Dive Deep into Backpropagation

Chapter 2: Recurrent Neural Networks (RNNs)

Lecture 1: Introduction to RNNs: The Intuition Behind RNNs and Different Cells

Lecture 2: Building RNNs with TensorFlow: Hands-on Multiple Neural Networks

Lecture 3: Training RNNs in TensorFlow: Model Fit, Compile, and Execute

Chapter 3: Advanced Training Techniques

Lecture 1: Optimizing Model Training: Model Training with Number of Epochs

Lecture 2: Sequence-to-Sequence Models: Encoder and Decoder Models

Lecture 3: LSTM Networks and Applications: Random Initialization and LSTM Intuition

Chapter 4: Convolutional Neural Networks (CNNs)

Lecture 1: Implementing LSTMs with TensorFlow: Custom Implementation

Lecture 2: Introduction to Computer Vision: Pixel Idea and Conversion into Arrays

Lecture 3: Basics of Convolutional Neural Networks: Padding and Kernel

Chapter 5: Advanced CNN Techniques

Lecture 1: Understanding Kernels in CNNs: Different Kernels

Lecture 2: Padding, Strides, and Pooling in CNNs

Lecture 3: Data Augmentation and Optimization in CNNs: Hands-on TensorFlow

Chapter 6: Implementing CNNs

Lecture 1: Building and Training CNN Models

Lecture 2: Implementing LSTMs with TensorFlow: Preprocessing of Data

Lecture 3: New! Building Generative Models with LSTMs: Train Models with Hyperparameter Tun

Chapter 7: Deep Learning for Computer Vision

Lecture 1: Introduction to Computer Vision with Deep Learning: Preprocessing and Training

Lecture 2: Training Deep Learning Models for Image Data 1500 Images on Training & Test Data

Lecture 3: Efficiently Handling Large Image Data: Training Samples

Chapter 8: Advanced Techniques in Image Processing

Lecture 1: Advanced Image Processing Techniques: Cleaning and Preprocessing Data

Lecture 2: Classification with Deep Learning: 10 Classification Tasks

Lecture 3: Model Evaluation and Transfer Learning: Evaluating Models and Transformers

Chapter 9: Model Interpretation and Optimization

Lecture 1: Interpreting Deep Learning Models: Geometric Intuition of VGG16 Models

Lecture 2: Optimizing Deep Learning Models: Gradient Descent and Stochastic Gradient Descen

Lecture 3: Advanced Optimization Techniques

Chapter 10: Deployment and Maintenance of Deep Learning Models

Lecture 1: Practical Deployment of Deep Learning Models: Mathematical Equations

Lecture 2: Deploying Models with Flask: Understanding the Internals

Lecture 3: Handling Requests with Keras and Flask: Keras Models and Get/Post Methods

Chapter 11: Advanced Deployment Techniques

Lecture 1: Scaling Deep Learning Models: Image CNN Animal in Action

Lecture 2: Ensuring Low Latency in Model Deployment: Getting Logs Flask Application

Lecture 3: Flask Application Deploy Demo

Instructors

Akhil Vydyula
Data Scientist | Data & Analytics Specialist | Entrepreneur

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