Fundamentals of Deep Learning using Keras

Yes. Since the course emphasizes deep learning with Keras, learners should have working knowledge of Python. Familiarity with libraries such as NumPy and Pandas will also make the labs and projects easier to follow.

The course introduces both supervised models, such as regression and classification, and unsupervised models, such as clustering and autoencoders. Learners gain hands-on experience in implementing these with Keras while reinforcing their understanding of deep learning fundamentals with Keras.

In this deep learning fundamentals with Keras course, you will study a range of architectures including shallow vs. deep neural networks, CNNs for image tasks, RNNs for sequence data, transformers for AI applications, and autoencoders. These are compared in terms of strengths, challenges, and applications in deep learning Keras.

Yes. The course features practical labs throughout and concludes with a capstone project where learners design, build, and test their own model using Keras for deep learning.

CNNs are introduced for tasks such as image processing and recognition, while RNNs are applied to sequential data like text and time series. Both are implemented through step-by-step labs using deep learning with Keras.

Yes. These optimization techniques are core to the curriculum. Learners explore gradient descent, activation functions, and the vanishing gradient problem through videos, labs, and quizzes, gaining a strong foundation in deep learning Keras concepts.

The final project challenges learners to build a deep learning model for classification and captioning. It combines all key elements of the Fundamentals of Deep Learning using Keras Course, allowing participants to demonstrate their ability to create real-world AI solutions.

Yes. Completing this program equips you with practical experience in AI and deep learning for data science. Skills in Keras and neural network architectures are highly sought after in roles such as AI developer, machine learning engineer, and data scientist.

The course teaches learners to use metrics such as accuracy, precision, recall, and F1-score. Youll also explore optimization strategies and hyperparameter tuning, ensuring models are both accurate and efficient.

Yes. Learners are introduced to autoencoders as a form of unsupervised learning, useful for dimensionality reduction and anomaly detection. Labs provide hands-on practice with autoencoders in deep learning with Keras.

Yes. Transformers are covered as part of advanced AI applications. Labs guide learners in implementing transformers with Keras, reinforcing their role in natural language processing and generative AI tasks.

Since it is self-paced, completion time varies. On average, most learners finish within 68 weeks depending on their schedule and practice time.

Yes. Learners who successfully complete the course will earn an IBM Certificate in Fundamentals of Deep Learning using Keras, which validates their ability to design, train, and test models with Keras for deep learning.

Yes. From image recognition with CNNs to sequence modeling with RNNs and text processing with transformers, learners explore real-world applications that demonstrate how to learn deep learning with Keras effectively.