How long does it take to complete the course?

The course includes approximately 15 hours of video content. The time needed to complete the course will depend on your pace and the amount of time spent on hands-on projects and exercises. On average, you can expect to complete the course in a few weeks if you allocate several hours per week for watching videos and working on projects.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Deep Learning & Modern AI Architectures

Deep Learning & Modern AI Architectures

This course is part of AI Engineering Masterclass: From Zero to AI Hero Specialization

Instructor: Packt - Course Instructors

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build and train neural networks using TensorFlow, Keras, and PyTorch.
Implement and optimize CNN architectures for image classification tasks.
Apply RNNs and LSTMs for sequence modeling tasks such as text generation and sentiment analysis.
Utilize Transformer models and pre-trained models for advanced NLP applications.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the AI Engineering Masterclass: From Zero to AI Hero 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
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course will introduce you to the cutting-edge techniques and architectures in deep learning and AI. You will start by mastering the fundamentals of neural networks and deep learning, including key concepts like forward propagation, backpropagation, and gradient descent. From there, you will advance to Convolutional Neural Networks (CNNs) for image classification tasks and Recurrent Neural Networks (RNNs) for sequence modeling tasks such as time series prediction and text generation. As you progress, you will explore the revolutionary Transformer architecture, its self-attention mechanism, and its application in Natural Language Processing (NLP) tasks like text summarization and translation. This course will also cover transfer learning, allowing you to fine-tune pre-trained models for your own tasks, saving time and improving model accuracy. With hands-on projects using frameworks like TensorFlow, Keras, and PyTorch, you will apply your skills to real-world challenges. The course is designed for intermediate learners with prior knowledge of machine learning or neural networks. If you're a machine learning enthusiast or aspiring AI engineer looking to deepen your understanding of deep learning models and their real-world applications, this course will take your skills to the next level. By the end of the course, you will be able to design and implement advanced deep learning models, including CNNs, RNNs, and Transformers, and use transfer learning techniques to fine-tune models for specific tasks such as image classification, text generation, and more.

In this module, we will introduce you to neural networks and deep learning, exploring their foundational concepts and optimization techniques. You’ll learn how to build models with popular frameworks like TensorFlow, Keras, and PyTorch, and apply your skills to a hands-on image classification project using the CIFAR-10 dataset.

What's included

8 videos2 readings1 assignment

8 videos Total 137 minutes

Introduction to Week 9 Neural Networks and Deep Learning Fundamentals 1 minute
Day 1: Introduction to Deep Learning and Neural Networks 16 minutes
Day 2: Forward Propagation and Activation Functions 15 minutes
Day 3: Loss Functions and Backpropagation 16 minutes
Day 4: Gradient Descent and Optimization Techniques 22 minutes
Day 5: Building Neural Networks with TensorFlow and Keras 19 minutes
Day 6: Building Neural Networks with PyTorch 26 minutes
Day 7: Neural Network Project – Image Classification on CIFAR-10 22 minutes

2 readings Total 20 minutes

Introduction to the Course 'Deep Learning & Modern AI Architectures' 10 minutes
Full Specialization Resources 10 minutes

1 assignment Total 15 minutes

Week 9: Neural Networks and Deep Learning Fundamentals - Assessment 15 minutes

In this module, we will dive deep into CNNs, focusing on their unique architecture designed for image classification tasks. You will learn how to implement CNNs using Keras, TensorFlow, and PyTorch, while enhancing model performance through regularization and data augmentation techniques. Finally, apply your knowledge in an image classification project.

What's included

8 videos1 assignment

8 videos Total 161 minutes

Introduction to Week 10 Convolutional Neural Networks (CNNs) 1 minute
Day 1: Introduction to Convolutional Neural Networks 26 minutes
Day 2: Convolutional Layers and Filters 24 minutes
Day 3: Pooling Layers and Dimensionality Reduction 24 minutes
Day 4: Building CNN Architectures with Keras and TensorFlow 18 minutes
Day 5: Building CNN Architectures with PyTorch 22 minutes
Day 6: Regularization and Data Augmentation for CNNs 19 minutes
Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10 28 minutes

1 assignment Total 15 minutes

Week 10: Convolutional Neural Networks (CNNs) - Assessment 15 minutes

In this module, we will explore RNNs and their ability to process sequential data. You will learn about advanced RNN architectures like LSTMs and GRUs, apply text preprocessing and word embeddings, and build sequence-to-sequence models. The module culminates with a project focused on either text generation or sentiment analysis.

What's included

8 videos1 assignment

8 videos Total 166 minutes

Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling 1 minute
Day 1: Introduction to Sequence Modeling and RNNs 34 minutes
Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT) 25 minutes
Day 3: Long Short-Term Memory (LSTM) Networks 15 minutes
Day 4: Gated Recurrent Units (GRUs) 7 minutes
Day 5: Text Preprocessing and Word Embeddings for RNNs 24 minutes
Day 6: Sequence-to-Sequence Models and Applications 43 minutes
Day 7: RNN Project – Text Generation or Sentiment Analysis 18 minutes

1 assignment Total 15 minutes

Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling - Assessment 15 minutes

In this module, we will explore the cutting-edge Transformer architecture and attention mechanisms that have revolutionized NLP. You will gain hands-on experience working with pre-trained models like BERT and GPT, learning how to fine-tune them for real-world tasks like text summarization and translation.

What's included

8 videos1 assignment

8 videos Total 135 minutes

Introduction to Week 12 Transformers and Attention Mechanisms 1 minute
Day 1: Introduction to Attention Mechanisms 15 minutes
Day 2: Introduction to Transformers Architecture 18 minutes
Day 3: Self-Attention and Multi-Head Attention in Transformers 21 minutes
Day 4: Positional Encoding and Feed-Forward Networks 20 minutes
Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT 20 minutes
Day 6: Advanced Transformers – BERT Variants and GPT-3 21 minutes
Day 7: Transformer Project – Text Summarization or Translation 19 minutes

1 assignment Total 15 minutes

Week 12: Transformers and Attention Mechanisms - Assessment 15 minutes

In this module, we will dive into transfer learning, teaching you how to leverage pre-trained models for faster and more efficient model development. You will explore fine-tuning techniques for both computer vision and NLP tasks, addressing domain adaptation challenges, and apply your skills to a project focused on fine-tuning a model for a custom task.

What's included

8 videos1 reading3 assignments

8 videos Total 140 minutes

Introduction to Week 13 Transfer Learning and Fine-Tuning 1 minute
Day 1: Introduction to Transfer Learning 15 minutes
Day 2: Transfer Learning in Computer Vision 26 minutes
Day 3: Fine-Tuning Techniques in Computer Vision 22 minutes
Day 4: Transfer Learning in NLP 17 minutes
Day 5: Fine-Tuning Techniques in NLP 26 minutes
Day 6: Domain Adaptation and Transfer Learning Challenges 15 minutes
Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task 18 minutes

1 reading Total 10 minutes

Conclusion to the Course 'Deep Learning & Modern AI Architectures'New Reading 10 minutes

3 assignments Total 90 minutes

Week 13: Transfer Learning and Fine-Tuning - Assessment 15 minutes
Full Course Assessment 60 minutes
Full Course Practice Assessment 15 minutes

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Frequently asked questions

The "Deep Learning & Modern AI Architectures" course offers a thorough exploration of deep learning techniques and architectures, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers. This course is highly relevant for anyone looking to dive into cutting-edge AI, as deep learning and modern architectures power many of today’s most advanced applications in fields like computer vision, natural language processing, and speech recognition.

This course covers fundamental to advanced deep learning topics. Week 9 introduces the basics of neural networks, including concepts like forward propagation, loss functions, and backpropagation. Week 10 focuses on Convolutional Neural Networks (CNNs) for image-based tasks, while Week 11 delves into Recurrent Neural Networks (RNNs) for sequence data like text. Week 12 explores Transformers, a breakthrough architecture used in modern NLP tasks. Finally, Week 13 teaches transfer learning and fine-tuning techniques, helping you leverage pre-trained models for your own AI tasks.

After completing this course, you'll be proficient in building and training deep learning models using frameworks like TensorFlow, Keras, and PyTorch. You’ll be able to design and implement CNNs for image classification, RNNs for sequence modeling, and use Transformers for NLP tasks. You'll also understand how to apply transfer learning techniques to improve model efficiency. The course equips you with the skills needed to tackle a wide range of modern AI challenges.

This course assumes a basic understanding of machine learning concepts, as well as some familiarity with Python programming. If you're comfortable with the fundamentals of neural networks and have worked with libraries like TensorFlow or PyTorch, you'll find this course accessible. It’s designed for learners who already have an introductory knowledge of AI and want to deepen their expertise in advanced deep learning architectures.

This course is aimed at anyone interested in advancing their deep learning skills, including data scientists, machine learning engineers, and AI researchers. It is perfect for learners who already have a foundational understanding of machine learning and want to explore modern AI architectures in more depth. If you're looking to build expertise in cutting-edge techniques used in computer vision, NLP, and other AI applications, this course is for you.