TensorFlow Tutorial

TensorFlow is an open-source machine-learning framework developed by Google. It provides flexible tools to create neural networks for tasks such as classification, computer vision and natural language processing.

• It is highly scalable for both research and production.
• It supports CPUs, GPUs, and TPUs for faster computation.

Introduction

This section provides a brief overview of TensorFlow and its importance in machine learning and deep learning.

• Introduction TensorFlow
• Architecture of TensorFlow
• Installations in macOS
• Installations in Windows
• Installations in Linux
• TensorFlow vs PyTorch
• TensorFlow vs Keras

Basics

This section covers the fundamental concepts required to start building and working with tensors and models.

• Tensor
• Tensor Indexing
• Tensor Reshaping
• Tensor Broadcasting
• Ragged Tensors
• Sparse Tensors
• String Tensors
• Variables
• Automatic Differentiation
• Graphs and Functions
• Bitwise Operations
• Numerical Operations
• Sequential Model
• Functional API
• Running o CPU
• Running o GPU

Model Building and Training

This section explains how to create, train, evaluate and manage deep learning models.

• TensorFlow Keras API
• Neural Network Layers
• Single Layer Perceptron
• Multi-Layer Perceptron Learning
• Building a Neural Network
• Activation Functions
• Loss Functions
• Optimizers
• Backpropagation
• Gradient Descent
• model.evaluate()
• model.fit()
• Evaluation Metrics
• Saving and Loading Models
• Callbacks
• Visualizing Training Progress

Natural Language Processing (NLP)

This section covers how TensorFlow is used to process and model text data for language-based tasks.

• Text Preprocessing
• TF-IDF Representations
• Bag-of-Words Representations
• Recurrent Layers
• Recurrent Neural Networks (RNN)
• Attention Layers
• Transformer Model
• Text Classification Using RNNs
• Text Generation using LSTM
• Text Generation using GRU
• Time Series Forecasting
• Detecting Spam Emails
• Sentence Autocomplete

Computer Vision

This section explains how TensorFlow is used to build models for processing and analyzing images and visual data.

• Convolution Layers
• Convolutional Neural Network (CNN)
• Image Classifications
• Object Detection
• FaceMask Detection
• Image Generation using GANs

Applications of TensorFlow

• Highly scalable for large ML systems
• Production-ready deployment tools
• Strong ecosystem for research and industry