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

Sponsored by Connors State College

Earn a career credential that demonstrates your expertise
Intermediate level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
Intermediate level

Recommended experience

2 months
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
Recently updated!

November 2024

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Prepare for a career in Data Science

  • Receive professional-level training from IBM
  • Demonstrate your proficiency in portfolio-ready projects
  • Earn an employer-recognized certificate from IBM
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Professional Certificate - 5 course series

Introduction to Deep Learning & Neural Networks with Keras

Course 18 hours4.7 (1,642 ratings)

What you'll learn

Skills you'll gain

Category: Algorithms
Category: Artificial Neural Networks
Category: Deep Learning
Category: Human Learning
Category: Machine Learning
Category: Machine Learning Algorithms
Category: Network Model
Category: Applied Machine Learning
Category: Network Architecture
Category: Python Programming

Deep Learning with Keras and Tensorflow

Course 223 hours4.4 (864 ratings)

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 Neural Networks
Category: Deep Learning
Category: Human Learning
Category: Machine Learning
Category: Applied Machine Learning
Category: Machine Learning Algorithms
Category: Network Model

Introduction to Neural Networks and PyTorch

Course 317 hours4.4 (1,730 ratings)

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: Human Learning
Category: Machine Learning
Category: Deep Learning
Category: Python Programming
Category: Artificial Neural Networks
Category: Machine Learning Algorithms
Category: Applied Machine Learning
Category: Algorithms
Category: Regression
Category: Mathematics

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.

AI Capstone Project with Deep Learning

Course 516 hours4.5 (586 ratings)

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: Deep Learning
Category: Machine Learning
Category: Python Programming
Category: Artificial Neural Networks
Category: Machine Learning Algorithms
Category: Applied Machine Learning
Category: Data Analysis
Category: Data Visualization
Category: Human Learning

Instructors

Wojciech 'Victor' Fulmyk
IBM
4 Courses34,616 learners
Ricky Shi
IBM
1 Course33,023 learners

Offered by

IBM

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