PickAClass · AI, Tech & Engineering · AI & Machine Learning

Deep Learning

Dive into the world of artificial neural networks. Learn to build, train, and optimize deep learning models for complex tasks using frameworks like TensorFlow and PyTorch.

Deep Learning

Under the Hood of Neural Networks: Practical AI Mechanics

Practical Machine Learning with TensorFlow and Keras

TensorFlow Input Layers: Structuring Tabular Data with Feature Columns

Neural Network Training: Backpropagation and Gradients in Python

PyTorch Pipeline Design: From Raw Data to Model Training

ResNet Architectures: Solving Deep Learning Degradation

Optimizing Gradient Descent and Learning Rates in Machine Learning

Deep Learning Model Evaluation with Keras

Neural Networks and PyTorch: Guided Theory and Practice Exercises

Image Classification with ResNet50 and Transfer Learning

Preparing and Rescaling Target Arrays for Neural Networks in Python

Introduction to JAX for High-Performance Deep Learning

JAX Core Concepts: Key Terms for Deep Learning and Optimization

PyTorch Model Visualization with TensorBoard Locally and on Binder

PyTorch Model Training: Building a Structured Step-by-Step Pipeline

Neural Network Interpretability: Backward Queries in Python

LSTM Networks for Sequence Modeling in Deep Learning

Deep Learning Foundations: Training Neural Networks and Optimization

The Neural Network Development Cycle and Data Splitting

Demystifying Backpropagation: How Neural Networks Learn

PyTorch Training Loops with Higher-Order Functions

Transfer Learning Foundations: Reuse Pre-Trained Models in AI

Introduction to PyTorch for Deep Learning Projects

Class-Based PyTorch Pipelines for Clean Model Training