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IBM: Deep Learning with TensorFlow and Keras

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Become proficient with tensor operations in PyTorch. Learn how to build and train linear regression models from scratch, apply logistic regression for classification tasks, and handle data efficiently while optimizing models using gradient descent techniques.

Deep Learning with TensorFlow and Keras
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Deep Learning with TensorFlow and Keras

Sobre este curso

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According to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000.

Gain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges.

You’ll begin by learning about Keras's advanced features, including its functional API used to design complex models. You’ll then learn how to create custom layers and models to tailor solutions to unique challenges and seamlessly integrate Keras with TensorFlow 2.x for enhanced functionality.

Next, you’ll use Keras to develop advanced convolutional neural networks (CNNs) that can solve complex computer vision tasks. You’ll apply data augmentation to improve model generalization, implement transfer learning with pre-trained models, and leverage TensorFlow for advanced image processing. You’ll also explore transpose convolution

Then, learn how to build and train advanced Transformers using Keras for sequential data tasks, including time series prediction. You’ll gain hands-on experience developing Transformer-based models for text generation and explore how to utilize TensorFlow to manage sequential data effectively.

Then you’ll dive into unsupervised learning with Keras. You’ll build and train autoencoders, experiment with cutting-edge diffusion models, and develop generative adversarial networks (GANs). You’ll also learn to integrate TensorFlow for advanced unsupervised learning tasks and expand your expertise in generative modeling techniques.

You’ll master advanced Keras techniques for model development by creating custom training loops and optimizing model performance. You’ll explore hyperparameter tuning using Keras Tuner and leverage TensorFlow for enhanced model optimization and custom training workflows.

In the final module, you’ll explore reinforcement learning and its applications in Keras. You’ll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks, gaining practical experience with this powerful AI technique.

By the end of this course, you’ll have the knowledge and skills to build and optimize advanced models using Keras and TensorFlow 2.x, tackling challenges in computer vision, NLP, reinforcement learning, and generative modeling.

Premios

Deep Learning with TensorFlow

De un vistazo

  • Institution IBM
  • Subject Análisis de datos
  • Level Intermediate
  • Prerequisites
    • Basic/intermediate experience in Python programming, machine learning
    • Fundamentals of Deep Learning with Keras
  • Associated programs
  • Language English
  • Video Transcript English
  • Associated skillsCurve Fitting, Numerical Analysis, Time Series, Forecasting, Keras (Neural Network Library), Image Processing, PyTorch (Machine Learning Library), Deep Learning, Transformers (Electrical), Unsupervised Learning, Experimentation, Artificial Neural Networks, Computer Vision, Natural Language Processing, Machine Learning, Autoencoders, Transfer Learning, Reinforcement Learning, Algorithms, TensorFlow, Artificial Intelligence, Convolutional Neural Networks, Nodes (Networking), Application Programming Interface (API), Unstructured Data, Q Learning, Logistic Regression, Dataflow, Workflow Management, Linear Regression

Lo que aprenderás

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  • Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x
  • Develop advanced convolutional neural networks (CNNs) using Keras
  • Develop Transformer models for sequential data and time series prediction
  • Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning

Plan de estudios

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Module 1: Advanced Keras Functionalities

  • Welcome to the Course
  • Video: Course Introduction
  • Reading: Course Overview
  • Advanced Keras Functional API
  • Video: Introduction to Advanced Keras
  • Video: Keras Functional API and Subclassing API
  • Lab: Implementing the Functional API in Keras
  • Practice Quiz: Advanced Keras Functional API
  • Custom Layers with Keras
  • Video: Creating Custom Layers in Keras
  • Video: Overview of TensorFlow 2.x
  • Lab: Creating Custom Layers and Models
  • Practice Quiz: Custom Layers with Keras
  • Advanced Keras Functionalities Summary
  • Reading: Summary and Highlights: Advanced Keras Functionalities
  • Reading: Glossary: Advanced Keras Functionalities
  • Graded Quiz: Advanced Keras Functionalities
  • Discussion Prompt: Meet and Greet [ ungraded]

Module 2: Advanced CNNs in Keras

  • Advanced CNNs and Data Augmentation
  • Video: Advanced CNNs in Keras
  • Video: Data Augmentation Techniques
  • Lab: Advanced Data Augmentation with Keras
  • Practice Quiz: Advanced CNNs and Data Augmentation
  • Transfer Learning on Pre-trained Models and Image Processing
  • Video: Transfer Learning in Keras
  • Video: Using Pre-trained Models
  • Lab: Transfer Learning Implementation
  • Video: TensorFlow for Image Processing
  • Reading:Tips for Transfer Learning Implementation
  • Practice Quiz: Transfer Learning on Pre-trained Models and Image Processing
  • Introducing Transpose Convolution
  • Video: Introducing Transpose Convolution
  • Lab: Practical Application of Transpose Convolution
  • Practice Quiz: Introducing Transpose Convolution
  • Advanced CNNs in Keras Summary
  • Reading: Summary and Highlights: Advanced CNNs in Keras
  • Reading: Glossary: Advanced CNNs in Keras
  • Graded Quiz: Advanced CNNs in Keras
  • Discussion Prompt: Data Augmentation and Transfer Learning

Module 3: Transformers in Keras

  • Transformers in Keras
  • Video: Introduction to Transformers in Keras
  • Video: Building Transformers for Sequential Data
  • Lab: Building Advanced Transformers
  • Practice Quiz: Transformers in Keras
  • Advanced Transformers and Sequential Data using TensorFlow
  • Video: Advanced Transformer Applications
  • Video: Transformers for Time Series Prediction
  • Video: TensorFlow for Sequential Data
  • Lab: Implementing Transformers for Text Generation
  • Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow
  • Transformers in Keras Summary
  • Reading: Summary and Highlight: Transformers in Keras
  • Reading: Glossary: Transformers in Keras
  • Graded Quiz: Transformers in Keras
  • Discussion Prompt: Transforming Sequential Data with Transformers

Module 4: Unsupervised Learning and Generative Models in Keras

  • Unsupervised Learning, Autoencoders, and Diffusion Models
  • Video: Introduction to Unsupervised Learning in Keras
  • Video: Building Autoencoders in Keras
  • Lab: Building Autoencoders
  • Video: Diffusion Models
  • Lab: Implementing Diffusion Models
  • Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models
  • GANs and TensorFlow
  • Video: Generative Adversarial Networks (GANs)
  • Video: TensorFlow for Unsupervised Learning
  • Lab: Develop GANs using Keras

Practice Quiz: GANs and TensorFlow

  • Unsupervised Learning and Generative Models in Keras Summary
  • Reading: Summary and Highlight: Unsupervised Learning and Generative Models in Keras
  • Reading: Glossary: Unsupervised Learning and Generative Models in Keras
  • Graded Quiz: Unsupervised Learning and Generative Models in Keras
  • Discussion Prompt: Exploring Autoencoders and GANs

Module 5: Advanced Keras Techniques

  • Advanced Keras techniques and Custom Training Loops
  • Video: Advanced Keras Techniques
  • Video: Custom Training Loops in Keras
  • Lab: Custom Training Loops in Keras
  • Practice Quiz: Advanced Keras techniques and Custom Training Loops
  • Hyperparameter and Model Optimization
  • Video: Hyperparameter Tuning with Keras Tuner
  • Lab: Hyperparameter Tuning with Keras Tuner
  • Video: Model Optimization
  • Video: TensorFlow for Model Optimization
  • Practice Quiz: Hyperparameter and Model Optimization
  • Advanced Keras Techniques Summary
  • Reading: Summary and Highlight: Advanced Keras Techniques
  • Reading: Glossary: Advanced Keras Techniques
  • Graded Quiz: Advanced Keras Techniques and Custom Training Loops
  • Discussion Prompt: Custom Training Loops and Hyperparameter Optimization

Module 6: Introduction to Reinforcement Learning with Keras

  • Reinforcement Learning, Q-Learning, Q-Networks (DQNs)
  • Video: Introduction to Reinforcement Learning
  • ideo: Q-Learning with Keras
  • Lab: Implementing Q-Learning in Keras
  • Video: Deep Q-Networks (DQNs) with Keras
  • Lab: Building a Deep Q-Network with Keras
  • Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs)
  • Module Summary
  • Reading: Summary and Highlight: Introduction to Reinforcement Learning with Keras
  • Reading: Glossary: Introduction to Reinforcement Learning with Keras
  • Graded Quiz: Introduction to Reinforcement Learning with Keras
  • Discussion Prompt: The Promise and Challenge of Reinforcement Learning

Module 7: Final Project and Assignment

  • Reading: Practice Project Overview: Fruit Classification Using Transfer Learning
  • Lab: Practice Project: Fruit Classification Using Transfer Learning
  • Reading: Final Project: Classify Waste Products Using Transfer Learning
  • Final Project: Classify Waste Products Using Transfer Learning
  • Project: Peer-graded Assignment: Classify Waste Products Using Transfer Learning

Course Wrap Up

  • Video: Course Wrap-up
  • Reading: Congratulations and Next Steps
  • Reading: Thanks from the Course Team

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

Este curso es parte del programa Deep Learning Professional Certificate

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5 cursos de capacitación
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7 meses
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