Deep Learning for Generative AI: 2025 Mastery Course

2025 Mastering Deep Learning for Generative AI

Learn to build and optimize generative models with deep learning. Explore GANs, VAEs, and transformers. Hands-on project

All Level

Created by : Rated : 4.3 100 views

Last Updated: 2025-01-31 05:01:12 English Course Type: Self Learning

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.

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.

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.

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.

Section 1 : Introduction to Deep Learning Concepts 4 Lectures 00:32:57
Lecture 1 :
The History of Deep Learning and Inspired by Neuroscience Preview
Lecture 2 :
Understanding Neural Networks: Weights, Multi-Neuron Networks,
Lecture 3 :
Dive Deep into Backpropagation
Lecture 4 :
Resources
Section 2 : Recurrent Neural Networks (RNNs) 3 Lectures 00:26:52
Lecture 1 :
Introduction to RNNs: The Intuition Behind RNNs and Different Cells
Lecture 2 :
Introduction to RNNs: The Intuition Behind RNNs and Different Cells
Lecture 3 :
Training RNNs in TensorFlow: Model Fit, Compile, and Execute
Section 3 : Advanced Training Techniques 3 Lectures 00:29:19
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
Section 4 : Convolutional Neural Networks (CNNs) 3 Lectures 00:20:31
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
Section 5 : Advanced CNN Techniques 3 Lectures 00:31:27
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
Section 6 : Implementing CNNs 3 Lectures 00:24:40
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
Section 7 : Deep Learning for Computer Vision 3 Lectures 00:03:57
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
Section 8 : Advanced Techniques in Image Processing 3 Lectures 00:15:15
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
Section 9 : Model Interpretation and Optimization 3 Lectures 00:21:22
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
Section 10 : Deployment and Maintenance of Deep Learning Models 3 Lectures 00:23:35
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
Section 11 : Advanced Deployment Techniques 4 Lectures 00:21:01
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
Lecture 4 :
Resources

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AKHIL VYDYULA,

2125 Course Views

19 Courses

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.

Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.

I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!