Day #187 - Deep Learning Basics: Introduction and Overview - MIT

Well Explained, Great Introduction and covered the fundamentals and state of art of Deep Learning

Key Lessons

• DL to extract patterns in data with little human effort (Automated Approach)

• Evolution of Deep Learning with respect to years of new innovations

• DL Tools 

• Apply methodology requires organize data, label, select aspect of data to reval answers for our questions
• CPU, GPU, TPU - Hardware for efficient execution 
• Higher abstractions help people to solve it in lesser time 

• DL - Higher and Higher level of abstraction of understanding patterns

• History of Science is the Simpler representation of Ideas
• DL remove human from the picture, Automate the feature learning
• Problems are not formulated as Data Driven Learning
• RNN to predict intent of players in scene
• Visual understanding is hard

• Regression is Continuous, Classification is Categorical

• Sequence of inputs, Sequence of outputs
• Forward Pass

• Many levels of layers create the necessary layers
• Many of the slides are summary and self-explanatory, Well explained

• Activation functions, Loss functions

• Backpropagation to adjust the weights, error flow backward to network

• After Forward pass compute error
• Backward Pass - Compute gradients
• Learning rate how fast the network learns    

Generalize without memorizing

Normalize input data before feeding to network

CNN - Enables image classification

Find Candidates, bounding boxes

Region proposals - Loop over region proposals

Single shot - remove for loop (less accuracy for objects far or smaller)

Trandeoff performance vs accuracy

Semantic segmentation (pixel level boundaries of object)

Transfer Learning - Commonly Applied - Pretrained model -  Chop off the fully connected layer - New Dataset and retrain the network

Word2vec form representations of data, words far in euclidean sense

Challenge is learning long term context

Bidirectional RNN

Generalized Model Architecture for text, audio, video, chat etc

  

	#Import Library
	import tensorflow as tf
	from tensorflow import keras
	#getdata
	#Get Dataset
	(train_images,train_labels),(test_images,test_labels) = keras.datasets.mnist.load_data()
	#setup model
	#Architecture
	model = keras.Sequential([keras.layers.Flatten(input_shape=(28,28)),
	keras.layers.Dense(128,activation=tf.nn.relu),
	keras.layers.Dense(10,activation=tf.nn.softmax)])
	model.compile(optimizer=tf.train.AdamOptimizer(),loss='sparse_categorical_crossentropy',metrics=['accuracy'])
	#train model
	model.fit(train_images,train_labels,epochs=5)
	#evaluate
	test_loss,test_acc = model.evaluate(test_images,test_labels)
	print('test accuracy:',test_acc)
	#make prediction
	predictions = model.predict(test_images)
	print(predictions)

view raw MIT_Example1.py hosted with ❤ by GitHub

Happy Mastering DL!!!