IBM
IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate
IBM

IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate

Fast-track your deep learning engineering career. Build the deep learning expertise employers are looking for in just 3 months

Wojciech 'Victor' Fulmyk
Ricky Shi
Romeo Kienzler

Instructors: Wojciech 'Victor' Fulmyk

3,888 already enrolled

Included with Coursera Plus

Earn a career credential that demonstrates your expertise
4.4

(102 reviews)

Intermediate level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
4.4

(102 reviews)

Intermediate level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Job-ready deep learning skills using PyTorch, Keras, and TensorFlow employers are looking for - in just 3 months!

  • How to create shareable projects, deep learning models, and neural networks using Keras and PyTorch.

  • How to train linear and logistic regression models, optimize with gradient descent using PyTorch, and create custom models with Keras.

  • How to build advanced CNNs and transformer models and build CNNs with effective layers and activations… and more.

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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Advance your career with in-demand skills

  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM

Professional Certificate - 5 course series

What you'll learn

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Neural Networks
Category: Deep Learning
Category: Tensorflow
Category: Natural Language Processing
Category: Performance Tuning
Category: Generative AI
Category: Time Series Analysis and Forecasting
Category: Keras (Neural Network Library)
Category: Image Analysis
Category: Artificial Intelligence
Category: Unsupervised Learning
Category: Reinforcement Learning

What you'll learn

  • 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

Skills you'll gain

Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Neural Networks
Category: PyTorch (Machine Learning Library)
Category: Deep Learning
Category: Supervised Learning
Category: Computer Vision
Category: Regression Analysis
Category: Applied Machine Learning

What you'll learn

  • Job-ready PyTorch skills employers need in just 6 weeks

  • How to implement and train linear regression models from scratch using PyTorch’s functionalities

  • Key concepts of logistic regression and how to apply them to classification problems

  • How to handle data and train models using gradient descent for optimization 

Skills you'll gain

Category: PyTorch (Machine Learning Library)
Category: Artificial Neural Networks
Category: Deep Learning
Category: Tensorflow
Category: Natural Language Processing
Category: Computer Vision
Category: Keras (Neural Network Library)
Category: Machine Learning Algorithms
Category: Machine Learning
Category: Image Analysis
Category: Unsupervised Learning
Deep Learning with PyTorch

Deep Learning with PyTorch

Course 420 hours

What you'll learn

  • Key concepts on Softmax regression and understand its application in multi-class classification problems.

  • How to develop and train shallow neural networks with various architectures.

  • Key concepts of deep neural networks, including techniques like dropout, weight initialization, and batch normalization.

  • How to develop convolutional neural networks, apply layers and activation functions.

Skills you'll gain

Category: Artificial Neural Networks
Category: Deep Learning
Category: PyTorch (Machine Learning Library)
Category: TensorFlow
Category: Data Manipulation
Category: Predictive Modeling
Category: Regression Analysis
Category: Probability & Statistics
Category: Machine Learning
Category: Linear Algebra

What you'll learn

  • Build a deep learning model to solve a real problem.

  • Execute the process of creating a deep learning pipeline.

  • Apply knowledge of deep learning to improve models using real data.

  • Demonstrate ability to present and communicate outcomes of deep learning projects.

Skills you'll gain

Category: Artificial Neural Networks
Category: Deep Learning
Category: PyTorch (Machine Learning Library)
Category: Data Manipulation
Category: Predictive Modeling
Category: Computer Vision
Category: Data Processing
Category: Keras (Neural Network Library)
Category: Scientific Visualization
Category: Applied Machine Learning
Category: Verification And Validation
Category: Image Analysis

Earn a career certificate

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Instructors

Wojciech 'Victor' Fulmyk
IBM
6 Courses54,372 learners
Ricky Shi
IBM
1 Course40,337 learners

Offered by

IBM

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Frequently asked questions

¹Based on Coursera learner outcome survey responses, United States, 2021.