Deep Learning with TensorFlow - iNovAITec

• Introduction to tensors (creating tensors)
• Getting information from tensors (tensor attributes)
• Manipulating tensors (tensor operations)
• Tensors and NumPy
  
• Using GPUs with TensorFlow
  

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

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

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

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

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

• Learn how to scale up an existing model
  
• Learn to how evaluate your machine learning models by finding the most wrong predictions
  
• Beat the original Food-Image-101 paper using only 10% of the data
  

• 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 Food-Image-101  paper.

• Preprocess natural language text to be used with a neural network
• Create word embeddings (numerical representations of text) with TensorFlow
  
• Build neural networks capable of binary and multi-class classification using:
• RNNs (recurrent neural networks)
• LSTMs (long short-term memory cells)
• GRUs (gated recurrent units)
  
• CNNs (Convolutional Neural Networks)
  

• Learn how to evaluate your NLP models
  

• Audio-Visual Automatic Speech Recognition has become the most  promising research area when the audio signal gets corrupted by noise.

Time Series fundamentals in TensorFlow
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)
Prepare data for time series neural networks (features and labels)
Understanding and using different time series evaluation methods
MAE — mean absolute error
Build time series forecasting models with TensorFlow
RNNs (recurrent neural networks)
CNNs (convolutional neural networks)

• Anyone who wants to pass the TensorFlow Developer exam so they can join  Google's Certificate Network and display their certificate and badges on  their resume, GitHub, and social media platforms including LinkedIn,  making it easy to share their level of TensorFlow expertise with the  world
  
• Students, developers, and data scientists who want to demonstrate  practical machine learning skills through the building and training of  models using TensorFlow
  
• Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
• Anyone looking to master building ML models with the latest version of TensorFlow