[ZeroToMastery, Daniel Bourke] TensorFlow for Deep Learning Bootcamp: Zero to Mastery [5/2025, ENG]

1. Understand how to integrate Machine Learning into tools and applications
   
2. Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
   
3. Applying Deep Learning for Time Series Forecasting
   
4. Be recognized as a top candidate for recruiters seeking TensorFlow developers
   
5. Build TensorFlow models using Computer Vision, Convolutional Neural Networks, and Natural Language Processing
   
6. Increase your skills in Machine Learning and Deep Learning
   
7. Learn to build all types of Machine Learning Models using the latest TensorFlow 2
   
8. Using real-world images in different shapes and sizes to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
   
9. Gain the skills you need to become a TensorFlow Developer

0. TensorFlow Fundamentals

1. Introduction to tensors (creating tensors)
   
2. Getting information from tensors (tensor attributes)
   
3. Manipulating tensors (tensor operations)
   
4. Tensors and NumPy
   
5. Using @tf.function (a way to speed up your regular Python functions)
   
6. Using GPUs with TensorFlow

1. Neural Network Regression with TensorFlow

1. Build TensorFlow sequential models with multiple layers
   
2. Prepare data for use with a machine learning model
   
3. Learn the different components which make up a deep learning model (loss function, architecture, optimization function)
   
4. Learn how to diagnose a regression problem (predicting a number) and build a neural network for it

2. Neural Network Classification with TensorFlow

1. Learn how to diagnose a classification problem (predicting whether something is one thing or another)
   
2. Build, compile & train machine learning classification models using TensorFlow
   
3. Build and train models for binary and multi-class classification
   
4. Plot modelling performance metrics against each other
   
5. Match input (training data shape) and output shapes (prediction data target)

3. Computer Vision and Convolutional Neural Networks with TensorFlow

1. Build convolutional neural networks with Conv2D and pooling layers
   
2. Learn how to diagnose different kinds of computer vision problems
   
3. Learn to how to build computer vision neural networks
   
4. Learn how to use real-world images with your computer vision models

4. Transfer Learning with TensorFlow Part 1: Feature Extraction

1. Learn how to use pre-trained models to extract features from your own data
   
2. Learn how to use TensorFlow Hub for pre-trained models
   
3. Learn how to use TensorBoard to compare the performance of several different models

5. Transfer Learning with TensorFlow Part 2: Fine-tuning

1. Learn how to setup and run several machine learning experiments
   
2. Learn how to use data augmentation to increase the diversity of your training data
   
3. Learn how to fine-tune a pre-trained model to your own custom problem
   
4. Learn how to use Callbacks to add functionality to your model during training

6. Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)

1. Learn how to scale up an existing model
   
2. Learn to how evaluate your machine learning models by finding the most wrong predictions
   
3. Beat the original Food101 paper using only 10% of the data

7. Milestone Project 1: Food Vision

1. Combine everything you've learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper.

8. NLP Fundamentals in TensorFlow
You'll learn to:

1. Preprocess natural language text to be used with a neural network
   
2. Create word embeddings (numerical representations of text) with TensorFlow
   
3. Build neural networks capable of binary and multi-class classification using:
   1. RNNs (recurrent neural networks)
      
   2. LSTMs (long short-term memory cells)
      
   3. GRUs (gated recurrent units)
      
   4. CNNs
4. Learn how to evaluate your NLP models

9. Milestone Project 2: SkimLit

1. Replicate the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)

10. Time Series fundamentals in TensorFlow

1. Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)
   
2. Prepare data for time series neural networks (features and labels)
   
3. Understanding and using different time series evaluation methods
   1. MAE — mean absolute error
4. Build time series forecasting models with TensorFlow
   1. RNNs (recurrent neural networks)
      
   2. CNNs (convolutional neural networks)

11. Milestone Project 3: (Surprise)

1. If you've read this far, you are probably interested in the course. This last project will be good... we promise you, so see you inside the course
   What's the bottom line?

General
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