002 train() and eval() modes - Free IT Course Platform

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01 – Introduction

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Lesson 001 How to learn from this course

Lesson 002 Using Udemy like a pro

02 – Download all course materials

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Lesson 001 Downloading and using the code

Lesson 002 My policy on code-sharing

03 – Concepts in deep learning

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Lesson 001 What is an artificial neural network

Lesson 002 How models learn

Lesson 003 The role of DL in science and knowledge

Lesson 004 Running experiments to understand DL

Lesson 005 Are artificial neurons like biological neurons

04 – About the Python tutorial

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Lesson 001 Should you watch the Python tutorial

05 – Math, numpy, PyTorch

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Lesson 002 Introduction to this section

Lesson 003 Spectral theories in mathematics

Lesson 004 Terms and datatypes in math and computers

Lesson 005 Converting reality to numbers

Lesson 006 Vector and matrix transpose

Lesson 007 OMG it’s the dot product!

Lesson 008 Matrix multiplication

Lesson 009 Softmax

Lesson 010 Logarithms

Lesson 011 Entropy and cross-entropy

Lesson 012 Minmax and argminargmax

Lesson 013 Mean and variance

Lesson 014 Random sampling and sampling variability

Lesson 015 Reproducible randomness via seeding

Lesson 016 The t-test

Lesson 017 Derivatives intuition and polynomials

Lesson 018 Derivatives find minima

Lesson 019 Derivatives product and chain rules

06 – Gradient descent

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Lesson 001 Overview of gradient descent

Lesson 002 What about local minima

Lesson 003 Gradient descent in 1D

Lesson 004 CodeChallenge unfortunate starting value

Lesson 005 Gradient descent in 2D

Lesson 006 CodeChallenge 2D gradient ascent

Lesson 007 Parametric experiments on g.d

Lesson 008 CodeChallenge fixed vs. dynamic learning rate

Lesson 009 Vanishing and exploding gradients

Lesson 010 Tangent Notebook revision history

07 – ANNs (Artificial Neural Networks)

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Lesson 001 The perceptron and ANN architecture

Lesson 002 A geometric view of ANNs

Lesson 003 ANN math part 1 (forward prop)

Lesson 004 ANN math part 2 (errors, loss, cost)

Lesson 005 ANN math part 3 (backprop)

Lesson 006 ANN for regression

Lesson 007 CodeChallenge manipulate regression slopes

Lesson 008 ANN for classifying qwerties

Lesson 009 Learning rates comparison

Lesson 010 Multilayer ANN

Lesson 011 Linear solutions to linear problems

Lesson 012 Why multilayer linear models don’t exist

Lesson 013 Multi-output ANN (iris dataset)

Lesson 014 CodeChallenge more qwerties!

Lesson 015 Comparing the number of hidden units

Lesson 016 Depth vs. breadth number of parameters

Lesson 017 Defining models using sequential vs. class

Lesson 018 Model depth vs. breadth

Lesson 019 CodeChallenge convert sequential to class

Lesson 021 Reflection Are DL models understandable yet

08 – Overfitting and cross-validation

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Lesson 001 What is overfitting and is it as bad as they say

Lesson 002 Cross-validation

Lesson 003 Generalization

Lesson 004 Cross-validation — manual separation

Lesson 005 Cross-validation — scikitlearn

Lesson 006 Cross-validation — DataLoader

Lesson 007 Splitting data into train, devset, test

Lesson 008 Cross-validation on regression

09 – Regularization

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Lesson 001 Regularization Concept and methods

002 train() and eval() modes

Lesson 003 Dropout regularization

Lesson 004 Dropout regularization in practice

Lesson 005 Dropout example 2

Lesson 006 Weight regularization (L1L2) math

Lesson 007 L2 regularization in practice

Lesson 008 L1 regularization in practice

Lesson 009 Training in mini-batches

Lesson 010 Batch training in action

Lesson 011 The importance of equal batch sizes

Lesson 012 CodeChallenge Effects of mini-batch size

10 – Metaparameters (activations, optimizers)

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Lesson 001 What are metaparameters

Lesson 002 The wine quality dataset

Lesson 003 CodeChallenge Minibatch size in the wine dataset

Lesson 004 Data normalization

Lesson 005 The importance of data normalization

Lesson 006 Batch normalization

Lesson 007 Batch normalization in practice

Lesson 008 CodeChallenge Batch-normalize the qwerties

Lesson 009 Activation functions

Lesson 010 Activation functions in PyTorch

Lesson 011 Activation functions comparison

Lesson 012 CodeChallenge Compare relu variants

Lesson 013 CodeChallenge Predict sugar

Lesson 014 Loss functions

Lesson 015 Loss functions in PyTorch

Lesson 016 More practice with multioutput ANNs

Lesson 017 Optimizers (minibatch, momentum)

Lesson 018 SGD with momentum

Lesson 019 Optimizers (RMSprop, Adam)

Lesson 020 Optimizers comparison

Lesson 021 CodeChallenge Optimizers and… something

Lesson 022 CodeChallenge Adam with L2 regularization

Lesson 023 Learning rate decay

Lesson 024 How to pick the right metaparameters

11 – FFNs (Feed-Forward Networks)

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Lesson 001 What are fully-connected and feedforward networks

Lesson 002 The MNIST dataset

Lesson 003 FFN to classify digits

Lesson 004 CodeChallenge Binarized MNIST images

Lesson 005 CodeChallenge Data normalization

Lesson 006 Distributions of weights pre- and post-learning

Lesson 007 CodeChallenge MNIST and breadth vs. depth

Lesson 008 CodeChallenge Optimizers and MNIST

Lesson 009 Scrambled MNIST

Lesson 010 Shifted MNIST

Lesson 011 CodeChallenge The mystery of the missing 7

Lesson 012 Universal approximation theorem

12 – More on data

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Lesson 001 Anatomy of a torch dataset and dataloader

Lesson 002 Data size and network size

Lesson 003 CodeChallenge unbalanced data

Lesson 004 What to do about unbalanced designs

Lesson 005 Data oversampling in MNIST

Lesson 006 Data noise augmentation (with devset+test)

Lesson 007 Data feature augmentation

Lesson 008 Getting data into colab

Lesson 009 Save and load trained models

Lesson 010 Save the best-performing model

Lesson 011 Where to find online datasets

13 – Measuring model performance

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Lesson 001 Two perspectives of the world

Lesson 002 Accuracy, precision, recall, F1

Lesson 003 APRF in code

Lesson 004 APRF example 1 wine quality

Lesson 005 APRF example 2 MNIST

Lesson 006 CodeChallenge MNIST with unequal groups

Lesson 007 Computation time

Lesson 008 Better performance in test than train

14 – FFN milestone projects

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Lesson 001 Project 1 A gratuitously complex adding machine

Lesson 002 Project 1 My solution

Lesson 003 Project 2 Predicting heart disease

Lesson 004 Project 2 My solution

Lesson 005 Project 3 FFN for missing data interpolation

Lesson 006 Project 3 My solution

15 – Weight inits and investigations

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Lesson 001 Explanation of weight matrix sizes

Lesson 002 A surprising demo of weight initializations

Lesson 003 Theory Why and how to initialize weights

Lesson 004 CodeChallenge Weight variance inits

Lesson 005 Xavier and Kaiming initializations

Lesson 006 CodeChallenge Xavier vs. Kaiming

Lesson 007 CodeChallenge Identically random weights

Lesson 008 Freezing weights during learning

Lesson 009 Learning-related changes in weights

Lesson 010 Use default inits or apply your own

16 – Autoencoders

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Lesson 001 What are autoencoders and what do they do

Lesson 002 Denoising MNIST

Lesson 003 CodeChallenge How many units

Lesson 004 AEs for occlusion

Lesson 005 The latent code of MNIST

Lesson 006 Autoencoder with tied weights

17 – Running models on a GPU

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Lesson 001 What is a GPU and why use it

Lesson 002 Implementation

Lesson 003 CodeChallenge Run an experiment on the GPU

18 – Convolution and transformations

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Lesson 001 Convolution concepts

Lesson 002 Feature maps and convolution kernels

Lesson 003 Convolution in code

Lesson 004 Convolution parameters (stride, padding)

Lesson 005 The Conv2 class in PyTorch

Lesson 006 CodeChallenge Choose the parameters

Lesson 007 Transpose convolution

Lesson 008 Maxmean pooling

Lesson 009 Pooling in PyTorch

Lesson 010 To pool or to stride

Lesson 011 Image transforms

Lesson 012 Creating and using custom DataLoaders

19 – Understand and design CNNs

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Lesson 001 The canonical CNN architecture

Lesson 002 CNN to classify MNIST digits

Lesson 003 CNN on shifted MNIST

Lesson 004 Classify Gaussian blurs

Lesson 005 Examine feature map activations

Lesson 006 CodeChallenge Softcode internal parameters

Lesson 007 CodeChallenge How wide the FC

Lesson 008 Do autoencoders clean Gaussians

Lesson 009 CodeChallenge AEs and occluded Gaussians

Lesson 010 CodeChallenge Custom loss functions

Lesson 011 Discover the Gaussian parameters

Lesson 012 The EMNIST dataset (letter recognition)

Lesson 013 Dropout in CNNs

Lesson 014 CodeChallenge How low can you go

Lesson 015 CodeChallenge Varying number of channels

Lesson 016 So many possibilities! How to create a CNN

20 – CNN milestone projects

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Lesson 001 Project 1 Import and classify CIFAR10

Lesson 002 Project 1 My solution

Lesson 003 Project 2 CIFAR-autoencoder

Lesson 004 Project 3 FMNIST

Lesson 005 Project 4 Psychometric functions in CNNs

21 – Transfer learning

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Lesson 001 Transfer learning What, why, and when

Lesson 002 Transfer learning MNIST – FMNIST

Lesson 003 CodeChallenge letters to numbers

Lesson 004 Famous CNN architectures

Lesson 005 Transfer learning with ResNet-18

Lesson 006 CodeChallenge VGG-16

Lesson 007 Pretraining with autoencoders

Lesson 008 CIFAR10 with autoencoder-pretrained model

22 – Style transfer

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Lesson 001 What is style transfer and how does it work

Lesson 002 The Gram matrix (feature activation covariance)

Lesson 003 The style transfer algorithm

Lesson 004 Transferring the screaming bathtub

Lesson 005 CodeChallenge Style transfer with AlexNet

23 – Generative adversarial networks

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Lesson 001 GAN What, why, and how

Lesson 002 Linear GAN with MNIST

Lesson 003 CodeChallenge Linear GAN with FMNIST

Lesson 004 CNN GAN with Gaussians

Lesson 005 CodeChallenge Gaussians with fewer layers

Lesson 006 CNN GAN with FMNIST

Lesson 007 CodeChallenge CNN GAN with CIFAR

24 – RNNs (Recurrent Neural Networks) (and GRULSTM)

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Lesson 001 Leveraging sequences in deep learning

Lesson 002 How RNNs work

Lesson 003 The RNN class in PyTorch

Lesson 004 Predicting alternating sequences

Lesson 005 CodeChallenge sine wave extrapolation

Lesson 006 More on RNNs Hidden states, embeddings

Lesson 007 GRU and LSTM

Lesson 008 The LSTM and GRU classes

Lesson 009 Lorem ipsum

25 – Ethics of deep learning

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Lesson 001 Will AI save us or destroy us

Lesson 002 Example case studies

Lesson 003 Some other possible ethical scenarios

Lesson 004 Will deep learning take our jobs

Lesson 005 Accountability and making ethical AI

26 – Where to go from here

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Lesson 001 How to learn topic _X_ in deep learning

Lesson 002 How to read academic DL papers

27 – Python intro Data types

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Lesson 001 How to learn from the Python tutorial

Lesson 002 Variables

Lesson 003 Math and printing

Lesson 004 Lists (1 of 2)

Lesson 005 Lists (2 of 2)

Lesson 006 Tuples

Lesson 007 Booleans

Lesson 008 Dictionaries

28 – Python intro Indexing, slicing

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Lesson 001 Indexing

Lesson 002 Slicing

29 – Python intro Functions

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Lesson 001 Inputs and outputs

Lesson 002 Python libraries (numpy)

Lesson 003 Python libraries (pandas)

Lesson 004 Getting help on functions

Lesson 005 Creating functions

Lesson 006 Global and local variable scopes

Lesson 007 Copies and referents of variables

Lesson 008 Classes and object-oriented programming

30 – Python intro Flow control

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Lesson 001 If-else statements

Lesson 002 If-else statements, part 2

Lesson 003 For loops

Lesson 004 Enumerate and zip

Lesson 005 Continue

Lesson 006 Initializing variables

Lesson 007 Single-line loops (list comprehension)

Lesson 008 while loops

Lesson 009 Broadcasting in numpy

Lesson 010 Function error checking and handling

31 – Python intro Text and plots

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Lesson 001 Printing and string interpolation

Lesson 002 Plotting dots and lines

Lesson 003 Subplot geometry

Lesson 004 Making the graphs look nicer

Lesson 005 Seaborn

Lesson 006 Images

Lesson 007 Export plots in low and high resolution

32 – Bonus section

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A deep understanding of deep learning (with Python intro)

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