Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

Explain the core concepts and components of neural networks and the challenges of training deep networks

Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Describe the applications of computer vision across different industries.

Apply image processing and analysis techniques to computer vision problems.

Utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection.

Create an image classifier using Supervised learning techniques.

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 

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

Demonstrate your hands-on skills in building deep learning models using Keras and PyTorch to solve real-world image classification problems

Showcase your expertise in designing and implementing a complete deep learning pipeline, including data loading, augmentation, and model validation

Highlight your practical skills in applying CNNs and vision transformers to domain-specific challenges like geospatial land classification

Communicate your project outcomes effectively through a model evaluation