DeepLearningZeroToAll

DeepLearningZeroToAll is a comprehensive educational resource and implementation collection focused on deep learning and machine learning. It provides a structured learning path using TensorFlow to move from foundational linear models to complex neural network architectures.

The project is distinguished by its practical implementations of various network types, including multilayer perceptrons for logic problems, convolutional neural networks for spatial data and image recognition, and recurrent neural networks using LSTM cells for time-series forecasting and character sequence prediction. It also includes detailed demonstrations of model regularization through batch normalization and dropout techniques.

The repository covers a broad range of capabilities, including supervised machine learning with linear and logistic regression, data engineering for tensor manipulation and scaling, and model optimization through gradient descent and manual backpropagation calculations. It further includes tools for model evaluation, weight persistence, and training observability via cost function visualization and metric logging.

The content is delivered through a series of Jupyter Notebooks.

Features

Convolutional Neural Networks - Implements convolutional neural networks with max-pooling to recognize handwritten digits and image data.
Machine Learning Learning Paths - Provides a structured educational path for learning deep learning fundamentals through practical TensorFlow tutorials.
Backpropagation - Implements manual gradient calculations using the chain rule to demonstrate weight update mechanics.
Batch Normalization - Implements batch normalization to stabilize training and accelerate convergence in deep networks.
Binary Classifiers - Builds binary classifiers to solve logic problems using weights and sigmoid activations.
Convolutional Layers - Provides convolutional layers that apply filters to extract spatial features from image data.
Convolutional Neural Network Architectures - Designs CNN structures specifically for image feature extraction and classification.
Cost Functions - Implements mathematical cost functions to measure prediction error for minimization via gradient descent.
Dropout Regularization - Implements dropout layers that randomly deactivate neurons during training to prevent overfitting.
Gradient-Based Parameter Updates - Implements the fundamental process of adjusting model weights using computed gradients.
Gradient Computation - Calculates function gradients manually to support training on multi-feature linear datasets.
Image Classification - Builds and trains convolutional neural networks to categorize images and recognize handwritten digits.
Linear and Logistic Regression - Implements linear and logistic regression models for binary and multi-class classification on structured data.
Linear Regression Models - Implements models that map multiple input feature vectors to a scalar output for continuous value prediction.
Logistic Regression Models - Builds binary classification models using the sigmoid function and weight optimization via gradient descent.
Linear Regression Implementations - Provides educational implementations of linear and binary regression models for predictive classification.
Spatial Model Architectures - Creates specialized models using convolutional layers to process and analyze spatial data.
LSTM Sequence Models - Uses LSTM cells to process time-series data and maintain state across sequential inputs.
Multilayer Perceptrons - Implements multilayer perceptrons with hidden layers for tasks like digit classification.
Convolutional Network Builders - Implements the construction of multi-layer convolutional neural networks using TensorFlow for image classification tasks.
Neural Network Regularization - Applies regularization techniques including dropout and batch normalization to prevent overfitting.
Non-Linear Classifiers - Builds multi-layer architectures using activation functions to solve complex non-linear classification problems.
Gradient Descent Algorithms - Implements iterative optimization algorithms that update parameters in the direction of the negative gradient.
Recurrent Neural Networks - Implements recurrent neural network architectures for processing sequential data and character sequences.
Spatial Processing Operations - Develops convolutional networks designed for spatial analysis and image recognition tasks.
Backpropagation Training - Demonstrates the backpropagation algorithm by manually calculating gradients and updating weights.
LSTM Architectures - Constructs recurrent architectures utilizing LSTM cells for character-level sequence prediction.
Class Probability Estimation - Implements the calculation of likelihoods for multi-class target categories using probability distributions.
XOR Problem Solvers - Implements a multi-layer perceptron with sigmoid activation specifically to solve the XOR problem.
Cost Function Visualizations - Plots the relationship between model parameters and resulting costs to visualize how weights affect error.
Deep Learning Workflow Optimization - Tunes learning rates and visualizes cost functions to optimize the training workflow of deep models.
Error Rate Reduction Techniques - Optimizes model performance by calculating gradients and using optimizers to lower prediction error rates.
Accuracy Calculators - Includes utilities for computing classification accuracy by comparing predicted indices against ground truth labels.
Character-Level Models - Implements models that predict the next character in a sequence to capture linguistic patterns.
Sequential and Graph Data Analysis - Provides capabilities for analyzing sequential data for applications like stock prediction and translation.
Sequential Data Models - Creates frameworks for predicting future values in sequences, including character generation and time-series.
Time Series Forecasting - Implements recurrent neural networks to forecast future values in temporal data, such as stock prices.
Training Progress Monitors - Provides tools for tracking real-time metrics and model performance during the training process.
Xavier Initializations - Implements Xavier weight initialization to stabilize gradient variance during training.
Sequential State Architectures - Builds architectures that process sequential data by maintaining a hidden state across time steps.
Learning Rate Schedulers - Provides mechanisms to adjust the learning rate during training to improve convergence stability.
Softmax Regression - Implements multi-class classification models using softmax regression theory and practical code.
Softmax Normalization - Applies softmax normalization to map raw model outputs to probability distributions for multi-class classification.
Training Metrics - Logs scalars and histograms during training to track loss and weight distributions over time.
Learning and Reference - TensorFlow basic tutorials.

d2l-ai/d2l-en

29,001View on GitHub

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

MorvanZhou/Tensorflow-Tutorial

4,334View on GitHub

This project is a collection of educational resources and reference implementations for neural network development using TensorFlow. It serves as a comprehensive learning course, machine learning curriculum, and practical implementation guide for building deep learning architectures. The codebase provides instructional materials and examples covering a wide range of model types, including convolutional neural networks for image classification, recurrent networks and long short-term memory cells for sequential data, and autoencoders for generative modeling. It also includes implementations for

trekhleb/homemade-machine-learning

24,608View on GitHub

This project provides a collection of machine learning algorithms implemented from scratch in Python. It serves as an educational resource using interactive notebooks that combine code with mathematical explanations to demonstrate the first principles of data science. The repository includes reference implementations for neural networks, such as multilayer perceptrons with backpropagation, and supervised learning models including linear and logistic regression. It also covers unsupervised learning through k-means clustering and Gaussian anomaly detection. The codebase covers a broad range of

dsgiitr/d2l-pytorch

4,353View on GitHub

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

hunkimDeepLearningZeroToAll

Features