Ementorhub

What you'll learn

Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

Explain the core concepts and components of neural networks and the challenges of training deep networks

Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Skills you'll gain

Deep Learning

Keras (Neural Network Library)

Natural Language Processing

Tensorflow

Image Analysis

PyTorch (Machine Learning Library)

Computer Vision

Network Architecture

Artificial Neural Networks

Regression Analysis

There are 5 modules in this course

You’ll begin this course by gaining foundational knowledge of neural networks, including forward and backpropagation, gradient descent, and activation functions. You will explore the challenges of deep network training, such as the vanishing gradient problem, and learn how to overcome them using techniques like careful activation function selection. The hands-on labs in this course allow you to build regression and classification models, dive into advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and autoencoders, and utilize pretrained models for enhanced performance. The course culminates in a final project where you’ll apply what you’ve learned to create a model that classifies images and generates captions. By the end of the course, you’ll be able to design, implement, and evaluate a variety of deep learning models and be prepared to take your next steps in the field of machine learning.

Basics of Deep Learning

Keras and Deep Learning Libraries

Deep Learning Models

Final Project and Course Wrap-Up