Mastering Deep Learning for Generative AI
This comprehensive course is designed to equip you with the knowledge and skills necessary to master deep learning for generative AI, enabling you to build creative applications using machine learning. Spanning 11 sections and 32 detailed videos, the course covers foundational concepts to advanced techniques in deep learning, providing a deep dive into neural networks, recurrent neural networks (R
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Course Overview
Schedule of Classes
Course Curriculum
1 Subject
Mastering Deep Learning for Generative AI
33 Learning Materials
Introduction to Deep Learning Concepts
The History of Deep Learning and Inspired by Neuroscience
Understanding Neural Networks: Weights, Multi-Neuron Networks,
Dive Deep into Backpropagation
Recurrent Neural Networks (RNNs)
Introduction to RNNs: The Intuition Behind RNNs and Different Cells
Building RNNs with TensorFlow: Hands-on Multiple Neural Networks
Training RNNs in TensorFlow: Model Fit, Compile, and Execute
Advanced Training Techniques
Optimizing Model Training: Model Training with Number of Epochs
Sequence-to-Sequence Models: Encoder and Decoder Models
LSTM Networks and Applications: Random Initialization and LSTM Intuition
Convolutional Neural Networks (CNNs)
Implementing LSTMs with TensorFlow: Custom Implementation
Introduction to Computer Vision: Pixel Idea and Conversion into Arrays
Basics of Convolutional Neural Networks: Padding and Kernel
Advanced CNN Techniques
Understanding Kernels in CNNs: Different Kernels
Padding, Strides, and Pooling in CNNs
Data Augmentation and Optimization in CNNs: Hands-on TensorFlow
Implementing CNNs
Building and Training CNN Models
Implementing LSTMs with TensorFlow: Preprocessing of Data
New! Building Generative Models with LSTMs: Train Models with Hyperparameter Tuning
Deep Learning for Computer Vision
Introduction to Computer Vision with Deep Learning: Preprocessing and Training with Mini-Batch Size
Training Deep Learning Models for Image Data: 1500 Images on Training and Test Data
Efficiently Handling Large Image Data: Training Samples
Advanced Techniques in Image Processing
Advanced Image Processing Techniques: Cleaning and Preprocessing Data
Classification with Deep Learning: 10 Classification Tasks
Model Evaluation and Transfer Learning: Evaluating Models and Transformers
Model Interpretation and Optimization
Interpreting Deep Learning Models: Geometric Intuition of VGG16 Models
Optimizing Deep Learning Models: Gradient Descent and Stochastic Gradient Descent
Advanced Optimization Techniques
Deployment and Maintenance of Deep Learning Models
Practical Deployment of Deep Learning Models: Mathematical Equations
Deploying Models with Flask: Understanding the Internals
Handling Requests with Keras and Flask: Keras Models and Get/Post Methods
Advanced Deployment Techniques
Scaling Deep Learning Models: Image CNN Animal in Action
Ensuring Low Latency in Model Deployment: Getting Logs Flask Application
ASSIGNMENT
DEEP LEARNING ASSIGNMENT
Course Instructor
Ganta Srinath Reddy
1 Courses • 23 Students
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