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 EmployNV

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

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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
Category: Mathematical Theory & Analysis

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

Instructors

Wojciech 'Victor' Fulmyk
IBM
4 Courses34,469 learners
Ricky Shi
IBM
1 Course32,970 learners

Offered by

IBM

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