LearnFlat · 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

Introduction to AI Frameworks for Machine Learning and Deep Learning

Image Classification with CNNs using Kaggle and Colab

Seq2Seq Model Decoding with TrainingSampler in TensorFlow

Deep Learning Foundations: Perceptrons as Logical Operators

Setting the Learning Rate in Neural Network Training

TensorFlow Feature Columns for Scalable Machine Learning

Visualizing Gradient Descent with PyTorch and NumPy

ImageNet and Convolutional Neural Networks for Beginners

Backpropagation and the Chain Rule in Neural Networks

Saving TensorFlow Models for Production Inference

Foundations of Deep Learning: Why It Matters and How It Works

Foundations of PyTorch: Creating and Manipulating Tensors

Introduction to Neural Networks and Deep Learning

Coding the Perceptron Forward Propagation with NumPy

Building a Digit Recognition Neural Network from Scratch

Data Exploration and Preprocessing for Keras Deep Learning

Understanding Linear Limitations in Machine Learning

Foundations of Gradient Descent and Linear Regression

Neural Network Training with Matrix Backpropagation

Deep Learning Hardware Acceleration with PyTorch and CUDA

Gradient Descent Optimization: Batch vs Stochastic Methods

Deep Learning with ML.NET for .NET Developers

Deep Learning Fundamentals: Multilayer Perceptrons and Backpropagation

Node Classification with PyTorch Geometric and Graph Neural Networks