Deep Learning Specialization
- Offered byDeepLearning.AI
Deep Learning Specialization at DeepLearning.AI Overview
Duration | 6 months |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website |
Credential | Certificate |
Deep Learning Specialization at DeepLearning.AI Highlights
- Earn a certificate after completion of course
Deep Learning Specialization at DeepLearning.AI Course details
Professionals looking to advance their knowledge of deep learning techniques and applications
Developers interested in learning how to integrate deep learning models into software systems and applications
Individuals with a background in AI, machine learning, or data science who want to specialize in deep learning
Understand the fundamentals of deep learning, including the structure and function of neural networks, activation functions, forward and backward propagation, and the concept of gradient descent
Learn how to train and optimize deep neural networks (DNNs) using techniques like stochastic gradient descent (SGD), momentum, and adaptive learning rates
Learn how RNNs are used in applications like speech recognition, language modeling, machine translation, and text generation
The Deep Learning Specialization is an advanced, comprehensive program designed for professionals, engineers, and data scientists who wish to master the techniques and algorithms behind deep learning and neural networks
This specialization delves into the core principles of deep learning, offering a hands-on approach to learning through practical assignments and real-world case studies
This program will provide you with the foundational knowledge and skills required to design, train, and deploy deep learning models for a variety of applications
Deep Learning Specialization at DeepLearning.AI Curriculum
Neural Networks and Deep Learning
Introduction to Deep Learning
Neural Networks Basics
Shallow Neural Networks
Deep Neural Networks
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
Practical Aspects of Deep Learning
Optimization Algorithms
Hyperparameter tuning, Batch Normalization, and Programming Frameworks
Structuring Machine Learning Projects
ML Strategy (1)
ML Strategy (2)
Convolutional Neural Networks
Foundations of Convolutional Neural Networks
Deep Convolutional Models: Case Studies
Object Detection
Special Applications: Face Recognition and Neural Style Transfer
Sequence Models
Recurrent Neural Networks
Natural Language Processing and Word Embeddings
Sequence Models and the Attention Mechanism
Transformers