This repository contains my notes and code for the "TensorFlow Developer Certificate" course. The course covers the fundamentals of machine learning with TensorFlow, including neural networks, computer vision, natural language processing, and time series analysis.
- Introduction to TensorFlow
- Neural Network Regression with TensorFlow
- Neural Network Classification with TensorFlow
- Computer Vision and Convolutional Neural Networks with TensorFlow
- Transfer Learning with TensorFlow Part 1: Feature Extraction
- Transfer Learning with TensorFlow Part 2: Fine-tuning
- Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)
- Milestone Project 1: Food Vision
- NLP Fundamentals in TensorFlow
- Milestone Project 2: SkimLit
- Time Series fundamentals in TensorFlow
- Milestone Project 3: (Surprise)
This section covers the basics of TensorFlow, including creating and manipulating tensors, using GPUs, and optimizing code with tf.function.
This section covers how to build and train regression models using TensorFlow, including preparing data, defining loss functions and optimization functions, and diagnosing problems.
This section covers how to build and train classification models using TensorFlow, including working with binary and multi-class data, plotting performance metrics, and matching input and output shapes.
This section covers how to build convolutional neural networks with TensorFlow for computer vision problems, including diagnosing problems, using real-world images, and working with Conv2D and pooling layers.
This section covers how to use pre-trained models and TensorFlow Hub for feature extraction, and how to use TensorBoard to compare model performance.
This section covers how to set up and run machine learning experiments, use data augmentation, fine-tune pre-trained models, and use callbacks during training.
This section covers how to scale up existing models, evaluate models, and beat the original Food101 paper using only 10% of the data.
This project combines everything learned in the previous sections to build a computer vision model capable of classifying 101 different kinds of food.
This section covers how to preprocess natural language text, create word embeddings, and build neural networks capable of binary and multi-class classification using RNNs, LSTMs, GRUs, and CNNs.
This project replicates the model used in the PubMed 200k paper to classify different sequences in medical abstracts
Diagnosing time series problems, preparing data for time series neural networks, understanding and using different evaluation methods, and building time series forecasting models with RNNs and CNNs.