Deep Learning - Artificial Neural Networks with TensorFlow

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Deep Learning - Artificial Neural Networks with TensorFlow

• Course Info
• Curriculum
• Instructors

Description

What you'll learn

• Apply techniques to build and train artificial neural networks using TensorFlow.
• Analyze the performance of ANN models in various real-world problems like image classification and regression.
• Evaluate and compare advanced techniques for optimizing deep learning models.
• Create and optimize ANN models using various optimization algorithms and loss functions.

What you'll learn in this course?

• Build your subject-matter expertise
• This course is part of the Deep Learning with TensorFlow Specialization
• When you enroll in this course, you'll also be enrolled in this Specialization.

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool

Course Curriculum

Welcome

• Introduction
• Outline

Machine Learning and Neurons

• What Is Machine Learning
• Code Preparation (Classification Theory)
• Classification Notebook
• Code Preparation (Regression Theory)
• Regression Notebook
• The Neuron
• How Does a Model 'Learn'.
• Making Predictions
• Saving and Loading a Model
• Why Keras
• Suggestion Box

Feedforward Artificial Neural Networks

• Artificial Neural Networks Section Introduction
• Forward Propagation
• The Geometrical Picture
• Activation Functions
• Multiclass Classification
• How to Represent Images
• Code Preparation (Artificial Neural Networks)
• ANN for Image Classification
• ANN for Regression
• How to Choose Hyperparameters

In-Depth: Loss Functions

• Mean Squared Error
• Binary Cross Entropy
• Categorical Cross Entropy

In-Depth: Gradient Descent

• Gradient Descent
• Stochastic Gradient Descent
• Momentum
• Variable and Adaptive Learning Rates
• Adam Optimization (Part 1)
• Adam Optimization (Part 2)

Instructors

Dhanvika Y

Experienced instructor passionate about teaching and sharing knowledge.