IBM Deep Learning with PyTorch, Keras and Tensorflow

LinkedIn ranked AI Engineer as the fastest-growing job title in the United States, with postings rising 143% year-over-year. This IBM course builds the foundational PyTorch skills you need to launch that career path—starting with tensors and progressing to fully trained classification models. You will master tensor operations, build custom datasets, and implement linear regression models using PyTorch's nn.Module and autograd system. Then, you will progress through gradient descent, stochastic and mini-batch training, loss functions, and training/validation workflows. Further, you will build logistic regression classifiers, apply cross-entropy loss, and implement advanced optimization and regularization techniques. Through interactive labs, instructional videos, and an AI-assisted dialogue, you will practice building, training, and evaluating models using real PyTorch code patterns. By the end, you will create a portfolio-ready project that demonstrates your ability to perform PyTorch classification and gradient-based optimization tasks. Enroll now to build a project you can confidently showcase and stand out in the AI-driven job market.

This course introduces deep learning and neural networks with the Keras library. In this course, you’ll be equipped with foundational knowledge and practical skills to build and evaluate deep learning models. You’ll begin this course by gaining foundational knowledge of neural networks, including forward and backpropagation, gradient descent, and activation functions. You will explore the challenges of deep network training, such as the vanishing gradient problem, and learn how to overcome them using techniques like careful activation function selection. The hands-on labs in this course allow you to build regression and classification models, dive into advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and autoencoders, and utilize pretrained models for enhanced performance. The course culminates in a final project where you’ll apply what you’ve learned to create a model that classifies images and generates captions. By the end of the course, you’ll be able to design, implement, and evaluate a variety of deep learning models and be prepared to take your next steps in the field of machine learning.

Ready to apply your AI skills in a real-world scenario you can showcase in your portfolio? During this project, you’ll work with the deep learning skills you’ve acquired throughout the Professional Certificate, and we recommend that you have completed all the previous courses before starting this one. For this project, you’ll build and compare deep learning models using Keras and PyTorch, and work through a full development pipeline from data loading and augmentation to model training, evaluation, and deployment. You’ll apply convolutional neural networks (CNNs) and vision transformers to domain-specific challenges. Then, finally, you’ll assess performance using metrics like accuracy, precision, and inference speed. By the end of the project, you’ll be able to demonstrate your skills in building and comparing models using Keras and PyTorch. Plus, you’ll be able to showcase that you can implement CNNs and vision transformers and evaluate your model’s performance. If you’re ready to complete a portfolio-worthy capstone project, enroll today!

Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry. You will learn to create custom layers and models in Keras and integrate Keras with TensorFlow 2.x for enhanced functionality. You will develop advanced convolutional neural networks (CNNs) using Keras. You will also build transformer models for sequential data and time series using TensorFlow with Keras. The course also covers the principles of unsupervised learning in Keras and TensorFlow for model optimization and custom training loops. Finally, you will develop and train deep Q-networks (DQNs) with Keras for reinforcement learning tasks (an overview of Generative Modeling and Reinforcement Learning is provided). You will be able to practice the concepts learned using hands-on labs in each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Classification Model using transfer learning. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires a working knowledge of Python programming and basic mathematical concepts such as gradients and matrices, as well as fundamentals of Deep Learning using Keras.