Deep Learning Now!
Chapter 1: Introduction to AI
History of AI Timeline

1943  Shallow Neural Networks
 Creates the neuron to predict a function
 No learning involved

1958  The Perceptron
 Learn weights (perceptron)

19691982  Slow development of AI
 Back propagation algorithm developed
 1974 Applies back propagation to neural networks

19821995  Hopfield network
 Convolutional Neural Networks
 Recurrent Neural Networks
 1989  Handwriting recognition for postcodes

1997  Long Short Term Memory (LSTM)

2006  Multilayer neural networks

2011  ReLU activation function

2012  Dropout technology to prevent overfitting

2014  GANs get introduced

2015  TensorFlow 1.0 released

2019  OpenAI
 TensorFlow 2.0
Characteristics of Deep Learning
 Data Volume
 The requirements are getting bigger as more complex data are needed
 Computing Power
 Requires more computational power
 Network Scale
 How many layers of neurons are needed
Applications

Computer Vision
 Image classification
 Object detection
 Semantic segmentation
 Image captioning
 Image generation

NLP

Reinforcement Learning
 Game Playing
 Robotics
 Autonomous Driving
Deep Learning Frameworks
 Theano
 TensorFlow
 Scikitlearn
 No GPU acceleration
 Caffe
 Integrated with PyTorch
 Torch
 Based on Lua
 MXNet
 Pytorch
 Keras
 TensorFlow
Keras is the high level API design specs.
TensorFlow has an implementation of Keras in it called tf.keras
TensorFlow
Don't use TensorFlow 1.0. Use TensorFlow 2.0. TensorFlow 1.0 is not compatible with TensorFlow 2.0. Also, it is more verbose.