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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

Course Instructor: Mentor
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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

Video
00:10:06

Understanding Neural Networks: Weights, Multi-Neuron Networks,

Video
00:11:58

Dive Deep into Backpropagation

Video
00:10:53

Recurrent Neural Networks (RNNs)

Introduction to RNNs: The Intuition Behind RNNs and Different Cells

Video
00:10:25

Building RNNs with TensorFlow: Hands-on Multiple Neural Networks

Video
00:09:08

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

Video
00:07:19

Advanced Training Techniques

Optimizing Model Training: Model Training with Number of Epochs

Video
00:09:34

Sequence-to-Sequence Models: Encoder and Decoder Models

Video
00:10:13

LSTM Networks and Applications: Random Initialization and LSTM Intuition

Video
00:09:32

Convolutional Neural Networks (CNNs)

Implementing LSTMs with TensorFlow: Custom Implementation

Video
00:07:46

Introduction to Computer Vision: Pixel Idea and Conversion into Arrays

Video
00:05:26

Basics of Convolutional Neural Networks: Padding and Kernel

Video
00:07:18

Advanced CNN Techniques

Understanding Kernels in CNNs: Different Kernels

Video
00:09:55

Padding, Strides, and Pooling in CNNs

Video
00:10:46

Data Augmentation and Optimization in CNNs: Hands-on TensorFlow

Video
00:10:46

Implementing CNNs

Building and Training CNN Models

Video
00:11:06

Implementing LSTMs with TensorFlow: Preprocessing of Data

Video
00:07:25

New! Building Generative Models with LSTMs: Train Models with Hyperparameter Tuning

Video
00:01:12

Deep Learning for Computer Vision

Introduction to Computer Vision with Deep Learning: Preprocessing and Training with Mini-Batch Size

Video
00:01:21

Training Deep Learning Models for Image Data: 1500 Images on Training and Test Data

Video
00:01:24

Efficiently Handling Large Image Data: Training Samples

Video
00:01:31

Advanced Techniques in Image Processing

Advanced Image Processing Techniques: Cleaning and Preprocessing Data

Video
00:01:40

Classification with Deep Learning: 10 Classification Tasks

Video
00:06:21

Model Evaluation and Transfer Learning: Evaluating Models and Transformers

Video
00:07:23

Model Interpretation and Optimization

Interpreting Deep Learning Models: Geometric Intuition of VGG16 Models

Video
00:07:27

Optimizing Deep Learning Models: Gradient Descent and Stochastic Gradient Descent

Video
00:07:15

Advanced Optimization Techniques

Video
00:06:40

Deployment and Maintenance of Deep Learning Models

Practical Deployment of Deep Learning Models: Mathematical Equations

Video
00:07:11

Deploying Models with Flask: Understanding the Internals

Video
00:09:57

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

Video
00:06:27

Advanced Deployment Techniques

Scaling Deep Learning Models: Image CNN Animal in Action

Video
00:07:33

Ensuring Low Latency in Model Deployment: Getting Logs Flask Application

Video
00:07:07

ASSIGNMENT

DEEP LEARNING ASSIGNMENT

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Course Instructor

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Mentor

27 Courses   •   2698 Students