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Udacity Deep Learning v4.0.0

Meet Your Instructors-
Projects You will Build
Why Anaconda
Traffic Navigation with Deep Reinforcement Learning
Data Has Dimensions
Element-wise Matrix Operations
Matrix Multiplication Part 1
Matrix Multiplication Part 2
Matrix Transposes
Exemplo de classifica‡ao
Linear Boundaries-X
09 Higher Dimensions
DL 06 Perceptron Definition Fix V2
DL 08 AND And OR Perceptrons-Y
DL 09 XOR Perceptron-
07 Perceptron Algorithm Trick
DL 10 S Perceptron Algorithm
Perceptron Algorithm-
Perceptron Agorithm Pseudocode
Non-Linear Regions
Error Functions
Error Functions
Discrete vs Continuous-rdP
DL 18 Q Softmax V2
DL 18 S Softmax-n8S
Quiz - Softmax
One-Hot Encoding
Maximum Likelihood 1-1yJx
Maximum Likelihood 2
Quiz - Cross 1-
Quiz Cross Entropy
Cross Entropy 1
CrossEntropy V1
Formula For Cross 1
DL 27 Multi-Class Cross Entropy 2 Fix
DL 29 Logistic Regression-Minimizing The Error Function
Error Function
Gradient Descent-rhVIF
Gradient Descent Algorithm
Gradient Descent Vs Perceptron Algorithm
Continuous Perceptrons-07-JJ
Non-Linear Data
Non-Linear Models
29 Neural Network Architecture 2
Combinando modelos
Multiclass Classification
DL 41 Feedforward FIX V2
DL 42 Neural Network Error Function (1)
Backpropagation V2
Calculating The Gradient 1
Chain Rule
DL 46 Calculating The Gradient 2 V2 (2)
Gradient Descent
Gradient Descent-Math
Multilayer perceptrons
Training Optimization
Underfitting And Overfitting-xj4PlXMsN
Model Complexity Graph
DL 53 Q Regularization
Local Minima-gF_sW_nY
Random Restart
Vanishing Gradient
Other Activation Functions-kA
Batch vs Stochastic Gradient Descent
Learning Rate
Error Functions Around the World
Introduction to the Project
Introducing Andrew Trask
Andrew Trask - Intro
Framing the Problem
Mini Project 1 Solution
Transforming Text into Numbers
Mini Project 2 Solution
Building a Neural Network
Mini Project 3 Solution
Understanding Neural Noise
Understanding Inefficiencies in our Network-4MuS
Mini Project 5 Solution
Further Noise Reduction
Mini Project 6 Solution
Analysis What's Going on in the Weights
Andrew Trask - Outro
Keras Lab
Apresentando Alexis
Aplica‡oes de CNNs
How Computers Interpret Images
MLPs For Image Classification
Categorical Cross-Entropy
Model Validation in Keras
When do MLPs (not) work well
Local Connectivity-z9wiDg0w
Convolutional Layers
Camadas convolucionais-RnM1D-XI-
Stride and Padding
Pooling Layers
CNNs For Image Classification
CNNs in Keras Practical Example
Image Augmentation in Keras
Groundbreaking CNN Architectures-ddrB
Visualizando CNNs
Transfer Learning
Transfer Learning in Keras
Weight Initialization 1
Weight Initialization 2
Weight Initialization 3
Weight Initialization 4
A-Simple-Autoencoders 21718
Simple Autoencoder Solution
Convolutional Autoencoders
Convolutional Autoencoder Solutions
Transfer Learning-
Building VGGNet
Pretrained VGGNet
Data Preparation-WfsDMq
Data Preparation
Building The Classifier
Building The Classifier
Training The Classifier
Training And Testing
02 Skin Cancer V4
Survival Rate
Medical Classification
The Data
06 Image Challenge V3
07 Quiz Data Challenges V1
Solution Data Challenges
Training The Neural Network-HwiI-UXUx
10 Quiz Random Vs Preinitiliazed Weights V3
Solution Random Vs Preinitialized Thoughts
Validating The Training
13 Quiz Sensitivity And Specificty V3
Solution Sensitivty And Specificity
15 Quiz Diagnosing Cancer V3
16 Solution Diagnosing Cancer V3
ROC Curve
17 Quiz ROC Curve 1 PT2 V1
Solution ROC Curve
ROC Curve
What Is The Neural Network Looking At-qN
Confusion Matrix-Question 1
Confusion Matrix-3rpN
Mini Project Introduction-Rgf3YVFWl
00 Luis Introducing Ortal Newtitle121217-oXv7GiC
01 RNN Intro V6 Final
02 RNN History V4 Final
03 RNN Applications V3 Final
04 RNN FFNN Reminder A V7 Final
05 RNN FFNN Reminder B V6 Final
06 FeedForward A V7 Final-4rCfnWbx8
07 FeedForward B V3
08 Backpropagation Theory V6 Final
13 Overfitting Intro V4 Final
10 Backpropagation Example A V3 Final
Regra da cadeia
12 Backpropagation Example B V6 Final
14 RNN A V4 Final
16 RNN B V4 Final
17 RNN Unfolded V3 Final
18 RNN Example V5 Final
19 RNN BPTT A V6 Final-eE2L3
20 RNN BPTT B V5 Final
21 RNN BPTT C V7 Final
RNN Summary
23 From RNNs To LSTMs V4 Final
LSTM Basics
LSTM Architecture
Learn Gate
Forget Gate
Remember Gate
LSTM 7 Use Gate
Putting It All Together-IF8FlKW
Other Architectures
Character-Wise RNN
Implementing a Character-wise RNN
Batching Data Solution
LSTM Cell-ajC
LSTM Cell Solution
RNN Output
Network Loss-itu
Output And Loss Solutions
Build The Network
Build The Network And Results
Learning Rate
Minibatch Size
Number Of Iterations
Number Of Hidden Units Layers
RNN Hyperparameters
Implementing Word2Vec
Subsampling Solution
Making Batches-jx7qwdw
Batches Solution-DdfR0RjSC
Building The Network
Negative Sampling
Building The Network Solution-pkBAhQ2Ki
Training Results
Sentiment Prediction
Data Preprocessing-h4
Creating Testing Sets
Building The RNN 1
Training The Network
Sentiment RNN 2
GANs Intro
Cool Things To Do With GANs-bo
Other Generative Models, How GANs Work
Games, Equilibrium, GANs Solution Render
GANs Architecture
Getting Started with GANs
Generator Network
Discriminator Network
Generator and Discriminator Solutions
Building the Network
Building the Network Solution
Training Losses
Training Optimizers
Training Losses and Optimizers Solutions
A Trained GAN-TR
DCGAN And The Generator
Generator Solution
Discriminator Solution
Building And Training The Network
Hyperparameters Solution
Last Project - Congrats
Semi-Supervised Learning
Introducing Semi-Supervised Learning
Data Prep
Building The Generator And Discriminator
Model Loss Exercise
Model Optimization Exercise
Training The Network -P
Discriminator Solution
Model Loss Solution
Model Optimizer Solution
Trained Semi-Supervised GAN-9yWYZDX8
The Setting
OpenAI Gym
The Setting, Revisited
Continuing Tasks-E1I
The Reward Hypothesis
Goals and Rewards, Part 1
Goals and Rewards, Part 2
Cumulative Reward
Discounted Return
MDPs, Part 1
MDPs, Part 2
MDPs, Part 3
M0 L3 C01 Intro- V3 No Slack-OH-5IlSH
Gridworld Example
State-Value Functions
Bellman Equations
Action-Value Functions
Optimal Policies
Another Gridworld Example-n9SbomnLb
An Iterative Method-AX
M1 L1 C05 V3 No Slack-OH
Policy Improvement
Policy Iteration
Truncated Policy Iteration-a
Value Iteration
MC Prediction State Values
MC Prediction Action Values
Generalized Policy Iteration
MC Control Incremental Mean-E2RITH
MC Control Policy Evaluation
MC Control Policy Improvement-2RKH
MC Control Constant-alpha
TD Prediction TD(0)
TD Prediction Action Values-1c029
TD Control Sarsa(0)
TD Control Sarsamax
TD Control Expected Sarsa
Deep Reinforcement Learning
Continuous Spaces
Tile Coding
Coarse Coding
Function Approximation
Linear Function Approximation-OJ5wrB7o
Kernel Functions
Non-Linear Function Approximation
Intro to Deep Q-Learning
Neural Nets as Value Functions
Monte Carlo Learning
Temporal Difference Learning
Deep Q Network-GgtR_d1OB
Experience Replay-wX_-SZG
Fixed Q Targets
Deep Q-Learning Algorithm
DQN Improvements
Wrap Up
M2L3 01 V1
M2L3 02 V2
M2L3 03 V2-TePX
M2L3 04 V1
M2L3 05 V1
M2L3 06 V1
M2L3 07 V2
M2L3 08 V1
RL M2L4 01 Actor Critic Methods RENDER V1 V1
RL M2L4 02 A Better Score Function V2-_HBJ3l10
RL M2L4 03 Two Function Approximators V1
RL M2L4 04 The Actor And The Critic V1
RL M2L4 05 Advantage Function RENDER V1 V2
RL M2L4 06 Actor Critic With Advantage RENDER V1 V1
Confusion Matrix-Question 1
Accuracy 2
When Accuracy Wont Work-r0-O
04 Quiz False Negatives And Positives SC V1
Answer False Negatives And Positives
06 Precision SC V1
07 Recall SC V1
ROC Curve
Welcome To Linear Regression
DLND REG 01 Quiz Housing Prices V2
Solution Housing Prices-uhdTulw9
Fitting A Line
Moving A Line
Absolute Trick
Square Trick-AGZEq
Gradient Descent
Mean Absolute Error
Mean Squared Error
Minimizing Error Functions
Absolute Vs Squared Error
DLND REG 12 Absolute Vs Squared Error 2 V1 (1)
DLND REG 13 Absolute Vs Squared Error 3 V1 (1)
Higher Dimensions-
Closed Form Solution
Polynomial Regression-DBhWG
Neural Network Regression
Miniflow Introduction
Pixels are Features!

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