#### Section 1 : Introduction

 Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 2 About Certification Pdf Lecture 3 About Proctor Testing Pdf Lecture 4 All Course Resources + Notebooks Text Lecture 5 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

#### Section 2 : Deep Learning and TensorFlow Fundamentals

 Lecture 6 What is deep learning 4:23 Lecture 7 Why use deep learning 9:39 Lecture 8 What are neural networks 10:26 Lecture 9 What is deep learning already being used for 8:37 Lecture 10 What is and why use TensorFlow 7:56 Lecture 11 What is a Tensor 3:38 Lecture 12 What we're going to cover throughout the course 4:29 Lecture 13 How to approach this course 5:34 Lecture 14 Need A Refresher Text Lecture 15 Creating your first tensors with TensorFlow and tf 18:45 Lecture 16 Creating tensors with TensorFlow and tf 7:7 Lecture 17 Creating random tensors with TensorFlow 9:40 Lecture 18 Shuffling the order of tensors 9:40 Lecture 19 Creating tensors from NumPy arrays 11:55 Lecture 20 Getting information from your tensors (tensor attributes) 11:57 Lecture 21 Indexing and expanding tensors 12:33 Lecture 22 Manipulating tensors with basic operations 5:34 Lecture 23 Matrix multiplication with tensors part 1 11:53 Lecture 24 Matrix multiplication with tensors part 2 13:29 Lecture 25 Matrix multiplication with tensors part 3 10:3 Lecture 26 Changing the datatype of tensors 6:56 Lecture 27 Tensor aggregation (finding the min, max, mean & more) 9:49 Lecture 28 Tensor troubleshooting example (updating tensor datatypes) 6:14 Lecture 29 Finding the positional minimum and maximum of a tensor (argmin and argmax) 9:31 Lecture 30 Squeezing a tensor (removing all 1-dimension axes) 3:0 Lecture 31 One-hot encoding tensors 5:46 Lecture 32 Trying out more tensor math operations 4:48 Lecture 33 Exploring TensorFlow and NumPy's compatibility 5:43 Lecture 34 Making sure our tensor operations run really fast on GPUs 10:19 Lecture 35 TensorFlow Fundamentals challenge, exercises & extra-curriculum Text Lecture 36 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 37 About Certification Pdf

#### Section 3 : Neural network regression with TensorFlow

 Lecture 38 Introduction to Neural Network Regression with TensorFlow 7:33 Lecture 39 Inputs and outputs of a neural network regression model 8:59 Lecture 40 Anatomy and architecture of a neural network regression model 7:55 Lecture 41 Creating sample regression data (so we can model it) 12:47 Lecture 42 Note Code update for upcoming lecture(s) for TensorFlow 2 Text Lecture 43 The major steps in modelling with TensorFlow 20:15 Lecture 44 Steps in improving a model with TensorFlow part 1 6:3 Lecture 45 Steps in improving a model with TensorFlow part 2 9:26 Lecture 46 Steps in improving a model with TensorFlow part 3 12:33 Lecture 47 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise) 7:24 Lecture 48 Evaluating a TensorFlow model part 2 (the three datasets) 11:2 Lecture 49 Evaluating a TensorFlow model part 3 (getting a model summary) 17:18 Lecture 50 Evaluating a TensorFlow model part 4 (visualising a model's layers) 7:15 Lecture 51 Evaluating a TensorFlow model part 5 (visualising a model's predictions) 9:16 Lecture 52 Evaluating a TensorFlow model part 6 (common regression evaluation metrics) 8:6 Lecture 53 Evaluating a TensorFlow regression model part 7 (mean absolute error) 5:53 Lecture 54 Evaluating a TensorFlow regression model part 7 (mean square error) 3:19 Lecture 55 Setting up TensorFlow modelling experiments part 1 (start with a simple model) 13:50 Lecture 56 Setting up TensorFlow modelling experiments part 2 (increasing complexity) 11:30 Lecture 57 Comparing and tracking your TensorFlow modelling experiments 10:20 Lecture 58 How to save a TensorFlow model 8:20 Lecture 59 How to load and use a saved TensorFlow model 10:16 Lecture 60 (Optional) How to save and download files from Google Colab 6:19 Lecture 61 Putting together what we've learned part 1 (preparing a dataset) 13:31 Lecture 62 Putting together what we've learned part 2 (building a regression model) 13:21 Lecture 63 Putting together what we've learned part 3 (improving our regression model) 15:47 Lecture 64 Preprocessing data with feature scaling part 1 (what is feature scaling) 9:34 Lecture 65 Preprocessing data with feature scaling part 2 (normalising our data) 10:57 Lecture 66 Preprocessing data with feature scaling part 3 (fitting a model on scaled data) 7:41 Lecture 67 TensorFlow Regression challenge, exercises & extra-curriculum Text Lecture 68 Learning Guideline Text

#### Section 4 : Neural network classification in TensorFlow

 Lecture 69 Introduction to neural network classification in TensorFlow 8:25 Lecture 70 Example classification problems (and their inputs and outputs) 6:38 Lecture 71 Input and output tensors of classification problems 6:22 Lecture 72 Typical architecture of neural network classification models with TensorFlow 9:36 Lecture 73 Creating and viewing classification data to model 11:34 Lecture 74 Checking the input and output shapes of our classification data 4:38 Lecture 75 Building a not very good classification model with TensorFlow 12:11 Lecture 76 Trying to improve our not very good classification model 9:13 Lecture 77 Creating a function to view our model's not so good predictions 15:8 Lecture 78 Note Updates for TensorFlow 2 Text Lecture 79 Make our poor classification model work for a regression dataset 12:19 Lecture 80 Non-linearity part 1 Straight lines and non-straight lines 9:39 Lecture 81 Non-linearity part 2 Building our first neural network with non-linearity 5:47 Lecture 82 Non-linearity part 3 Upgrading our non-linear model with more layers 10:19 Lecture 83 Non-linearity part 4 Modelling our non-linear data once and for all 8:38 Lecture 84 Non-linearity part 5 Replicating non-linear activation functions from scratch Lecture 85 Getting great results in less time by tweaking the learning rate 14:47 Lecture 86 Using the TensorFlow History object to plot a model's loss curves 6:12 Lecture 87 Using callbacks to find a model's ideal learning rate 17:32 Lecture 88 Training and evaluating a model with an ideal learning rate 9:21 Lecture 89 Introducing more classification evaluation methods 6:5 Lecture 90 Finding the accuracy of our classification model 4:18 Lecture 91 Creating our first confusion matrix (to see where our model is getting confused) 8:28 Lecture 92 Making our confusion matrix prettier 14:1 Lecture 93 Putting things together with multi-class classification part 1 Getting the data 10:37 Lecture 94 Multi-class classification part 2 Becoming one with the data 7:8 Lecture 95 Multi-class classification part 3 Building a multi-class classification model 15:38 Lecture 96 Multi-class classification part 4 Improving performance with normalisation 12:43 Lecture 97 Multi-class classification part 5 Comparing normalised and non-normalised data 4:14 Lecture 98 Multi-class classification part 6 Finding the ideal learning rate 10:39 Lecture 99 Multi-class classification part 7 Evaluating our model 13:16 Lecture 100 Multi-class classification part 8 Creating a confusion matrix 4:26 Lecture 101 Multi-class classification part 9 Visualising random model predictions Lecture 102 What patterns is our model learning 15:33 Lecture 103 TensorFlow classification challenge, exercises & extra-curriculum Text

#### Section 6 : Transfer Learning in TensorFlow Part 1 Feature extraction

 Lecture 141 What is and why use transfer learning 10:12 Lecture 142 Downloading and preparing data for our first transfer learning model 14:40 Lecture 143 Introducing Callbacks in TensorFlow and making a callback to track our models 10:1 Lecture 144 Exploring the TensorFlow Hub website for pretrained models 9:51 Lecture 145 Building and compiling a TensorFlow Hub feature extraction model Lecture 146 Blowing our previous models out of the water with transfer learning 9:13 Lecture 147 Plotting the loss curves of our ResNet feature extraction model 7:36 Lecture 148 Building and training a pre-trained EfficientNet model on our data 9:42 Lecture 149 Different Types of Transfer Learning 11:40 Lecture 150 Comparing Our Model's Results 15:17 Lecture 151 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum Text Lecture 152 Exercise Imposter Syndrome 2:56

#### Section 9 : Milestone Project 1 Food Vision Big™

 Lecture 203 Introduction to Milestone Project 1 Food Vision Big™ 5:44 Lecture 204 Making sure we have access to the right GPU for mixed precision training 10:18 Lecture 205 Getting helper functions ready 3:6 Lecture 206 Introduction to TensorFlow Datasets (TFDS) 12:3 Lecture 207 Exploring and becoming one with the data (Food101 from TensorFlow Datasets) 15:56 Lecture 208 Creating a preprocessing function to prepare our data for modelling 15:50 Lecture 209 Batching and preparing our datasets (to make them run fast) 13:48 Lecture 210 Exploring what happens when we batch and prefetch our data 6:49 Lecture 211 Creating modelling callbacks for our feature extraction model 7:14 Lecture 212 Note Mixed Precision producing errors for TensorFlow 2 Text Lecture 213 Turning on mixed precision training with TensorFlow 10:5 Lecture 214 Creating a feature extraction model capable of using mixed precision training 12:42 Lecture 215 Checking to see if our model is using mixed precision training layer by layer 7:57 Lecture 216 Training and evaluating a feature extraction model (Food Vision Big™) 10:19 Lecture 217 Introducing your Milestone Project 1 challenge build a model to beat DeepFood 7:48 Lecture 218 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum Text

#### Section 10 : NLP Fundamentals in TensorFlow

 Lecture 219 Welcome to natural language processing with TensorFlow! Text Lecture 220 Introduction to Natural Language Processing 12:52 Lecture 221 Example NLP inputs and outputs 7:22 Lecture 222 The typical architecture of a Recurrent Neural Network (RNN) 9:3 Lecture 223 Preparing a notebook for our first NLP with TensorFlow project 8:53 Lecture 224 Becoming one with the data and visualising a text dataset 16:41 Lecture 225 Splitting data into training and validation sets 6:27 Lecture 226 Converting text data to numbers using tokenisation and embeddings (overview) 9:23 Lecture 227 Setting up a TensorFlow TextVectorization layer to convert text to numbers 17:10 Lecture 228 Mapping the TextVectorization layer to text data and turning it into numbers 11:3 Lecture 229 Creating an Embedding layer to turn tokenised text into embedding vectors 12:27 Lecture 230 Discussing the various modelling experiments we're going to run 8:58 Lecture 231 Model 0 Building a baseline model to try and improve upon 9:25 Lecture 232 Creating a function to track and evaluate our model's results 12:14 Lecture 233 Model 1 Building, fitting and evaluating our first deep model on text data 20:52 Lecture 234 Visualising our model's learned word embeddings with TensorFlow's projector tool 20:44 Lecture 235 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more 9:34 Lecture 236 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM) 18:17 Lecture 237 Model 3 Building, fitting and evaluating a GRU-cell powered RNN 16:56 Lecture 238 Model 4 Building, fitting and evaluating a bidirectional RNN model 19:35 Lecture 239 Discussing the intuition behind Conv1D neural networks for text and sequences 19:32 Lecture 240 Model 5 Building, fitting and evaluating a 1D CNN for text 9:58 Lecture 241 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP) 13:45 Lecture 242 Model 6 Building, training and evaluating a transfer learning model for NLP 10:46 Lecture 243 Preparing subsets of data for model 7 (same as model 6 but 10% of data) 10:52 Lecture 244 Model 7 Building, training and evaluating a transfer learning model on 10% data 10:4 Lecture 245 Fixing our data leakage issue with model 7 and retraining it 13:43 Lecture 246 Comparing all our modelling experiments evaluation metrics 13:14 Lecture 247 Uploading our model's training logs to TensorBoard and comparing them 11:15 Lecture 248 Saving and loading in a trained NLP model with TensorFlow 10:25 Lecture 249 Downloading a pretrained model and preparing data to investigate predictions 13:25 Lecture 250 Visualising our model's most wrong predictions 8:29 Lecture 251 Making and visualising predictions on the test dataset 8:27 Lecture 252 Understanding the concept of the speedscore tradeoff 15:2 Lecture 253 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum Text

#### Section 11 : Milestone Project 2 SkimLit

 Lecture 254 Introduction to Milestone Project 2 SkimLit 14:20 Lecture 255 What we're going to cover in Milestone Project 2 (NLP for medical abstracts) 7:22 Lecture 256 SkimLit inputs and outputs 11:2 Lecture 257 Setting up our notebook for Milestone Project 2 (getting the data) 14:58 Lecture 258 Visualising examples from the dataset (becoming one with the data) 13:18 Lecture 259 Writing a preprocessing function to structure our data for modelling Lecture 260 Performing visual data analysis on our preprocessed text 7:55 Lecture 261 Turning our target labels into numbers (ML models require numbers) 13:15 Lecture 262 Model 0 Creating, fitting and evaluating a baseline model for SkimLit 9:26 Lecture 263 Preparing our data for deep sequence models 9:56 Lecture 264 Creating a text vectoriser to map our tokens (text) to numbers 14:7 Lecture 265 Creating a custom token embedding layer with TensorFlow 9:14 Lecture 266 Creating fast loading dataset with the TensorFlow tf 9:50 Lecture 267 Model 1 Building, fitting and evaluating a Conv1D with token embeddings 17:21 Lecture 268 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2 10:53 Lecture 269 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings 11:31 Lecture 270 Creating a character-level tokeniser with TensorFlow's TextVectorization layer 23:24 Lecture 271 Creating a character-level embedding layer with tf 7:44 Lecture 272 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings 13:45 Lecture 273 Discussing how we're going to build Model 4 (character + token embeddings) 6:5 Lecture 274 Model 4 Building a multi-input model (hybrid token + character embeddings) 15:36 Lecture 275 Model 4 Plotting and visually exploring different data inputs 7:32 Lecture 276 Crafting multi-input fast loading tf 8:41 Lecture 277 Model 4 Building, fitting and evaluating a hybrid embedding model 13:18 Lecture 278 Model 5 Adding positional embeddings via feature engineering (overview) 7:18 Lecture 279 Encoding the line number feature to used with Model 5 12:26 Lecture 280 Encoding the total lines feature to be used with Model 5 7:56 Lecture 281 Model 5 Building the foundations of a tribrid embedding model 9:19 Lecture 282 Model 5 Completing the build of a tribrid embedding model for sequences 14:9 Lecture 283 Visually inspecting the architecture of our tribrid embedding model 10:25 Lecture 284 Creating multi-level data input pipelines for Model 5 with the tf 9:0 Lecture 285 Bringing SkimLit to life!!! (fitting and evaluating Model 5) 10:35 Lecture 286 Comparing the performance of all of our modelling experiments 9:36 Lecture 287 Saving, loading & testing our best performing model 7:49 Lecture 288 Congratulations and your challenge before heading to the next module 12:34 Lecture 289 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum Text

#### Section 12 : Time Series fundamentals in TensorFlow + Milestone Project 3 BitPredict

 Lecture 290 Welcome to time series fundamentals with TensorFlow + Milestone Project 3! Text Lecture 291 Example forecasting problems in daily life 4:52 Lecture 292 What can be forecast 7:58 Lecture 293 What we're going to cover (broadly) 2:35 Lecture 294 Time series forecasting inputs and outputs 8:56 Lecture 295 Downloading and inspecting our Bitcoin historical dataset 14:58 Lecture 296 Visualizing our Bitcoin historical data with pandas 4:53 Lecture 297 Reading in our Bitcoin data with Python's CSV module 10:58 Lecture 298 Creating train and test splits for time series (the wrong way) 8:37 Lecture 299 Creating train and test splits for time series (the right way) 7:12 Lecture 300 Creating a plotting function to visualize our time series data 7:57 Lecture 301 Model 0 Making and visualizing a naive forecast model 12:16 Lecture 302 Implementing MASE with TensorFlow 9:38 Lecture 303 Discussing the use of windows and horizons in time series data 7:50 Lecture 304 Creating a function to make predictions with our trained models 14:3 Lecture 305 Model 3 Visualizing the results 8:44 Lecture 306 Comparing our modelling experiments so far and discussing autocorrelation 9:44 Lecture 307 Preparing data for building a Conv1D model 13:21 Lecture 308 Preparing our multivariate time series for a model 13:37 Lecture 309 Model 7 Setting up hyperparameters for the N-BEATS algorithm 8:51 Lecture 310 Model 7 Getting ready for residual connections 12:56 Lecture 311 Model 8 Ensemble model overview 4:44 Lecture 312 Model 8 Building, compiling and fitting an ensemble of models 20:4 Lecture 313 Getting the upper and lower bounds of our prediction intervals 7:57 Lecture 314 Plotting the prediction intervals of our ensemble model prediction 13:3 Lecture 315 Model 9 Creating a function to make forecasts into the future Lecture 316 Model 9 Plotting our model's future forecasts 13:9 Lecture 317 Model 10 Introducing the turkey problem and making data for it 14:15 Lecture 318 TensorFlow Time Series Fundamentals Challenge and Extra Resources Text

#### Section 13 : Passing the TensorFlow Developer Certificate Exam

 Lecture 319 Get ready to be TensorFlow Developer Certified! Text Lecture 320 What is the TensorFlow Developer Certification 5:29 Lecture 321 Why the TensorFlow Developer Certification 6:57 Lecture 322 How to prepare (your brain) for the TensorFlow Developer Certification 8:14 Lecture 323 How to prepare (your computer) for the TensorFlow Developer Certification 12:43 Lecture 324 What to do after the TensorFlow Developer Certification exam 2:14

#### Section 14 : Where To Go From Here

 Lecture 325 Become An Alumni Text Lecture 326 About Proctor Testing Pdf Lecture 327 About Certification Pdf

#### Section 15 : Appendix Machine Learning Primer

 Lecture 328 Quick Note Upcoming Videos Text Lecture 329 What is Machine Learning 6:52 Lecture 330 AIMachine LearningData Science 4:51 Lecture 331 Exercise Machine Learning Playground 6:16 Lecture 332 How Did We Get Here 6:3 Lecture 333 Exercise YouTube Recommendation Engine 4:25 Lecture 334 Types of Machine Learning 4:41 Lecture 335 Are You Getting It Yet Text Lecture 336 What Is Machine Learning Round 2 4:45 Lecture 337 Section Review 1:48

#### Section 16 : Appendix Machine Learning and Data Science Framework

 Lecture 338 Quick Note Upcoming Videos Text Lecture 339 Section Overview 3:9 Lecture 340 Introducing Our Framework 2:38 Lecture 341 6 Step Machine Learning Framework 4:59 Lecture 342 Types of Machine Learning Problems 10:32 Lecture 343 Types of Data 4:51 Lecture 344 Types of Evaluation 3:31 Lecture 345 Features In Data 5:22 Lecture 346 Modelling - Splitting Data 5:58 Lecture 347 Modelling - Picking the Model 4:35 Lecture 348 Modelling - Tuning 3:17 Lecture 349 Modelling - Comparison 9:32 Lecture 350 Overfitting and Underfitting Definitions Text Lecture 351 Experimentation 3:35 Lecture 352 Tools We Will Use 4:0 Lecture 353 Optional Elements of AI Text

#### Section 17 : Appendix Pandas for Data Analysis

 Lecture 354 Quick Note Upcoming Videos Text Lecture 355 Section Overview 2:27 Lecture 356 Downloading Workbooks and Assignments Text Lecture 357 Pandas Introduction 4:29 Lecture 358 Series, Data Frames and CSVs 13:21 Lecture 359 Data from URLs Text Lecture 360 Describing Data with Pandas 9:49 Lecture 361 Selecting and Viewing Data with Pandas 11:8 Lecture 362 Selecting and Viewing Data with Pandas Part 2 13:7 Lecture 363 Manipulating Data 13:57 Lecture 364 Manipulating Data 2 9:57 Lecture 365 Manipulating Data 3 10:12 Lecture 366 Assignment Pandas Practice Text Lecture 367 How To Download The Course Assignments 7:43

#### Section 18 : Appendix NumPy

 Lecture 368 Quick Note Upcoming Videos Text Lecture 369 Section Overview 2:41 Lecture 370 NumPy Introduction 5:18 Lecture 371 Quick Note Correction In Next Video Text Lecture 372 NumPy DataTypes and Attributes 14:6 Lecture 373 Creating NumPy Arrays 9:22 Lecture 374 NumPy Random Seed 7:17 Lecture 375 Viewing Arrays and Matrices 9:35 Lecture 376 Manipulating Arrays 11:32 Lecture 377 Manipulating Arrays 2 9:44 Lecture 378 Standard Deviation and Variance 7:10 Lecture 379 mp4 7:27 Lecture 380 Dot Product vs Element Wise 11:45 Lecture 381 Exercise Nut Butter Store Sales 13:4 Lecture 382 Comparison Operators 3:34 Lecture 383 Sorting Arrays 6:20 Lecture 384 Turn Images Into NumPy Arrays 7:37 Lecture 385 Assignment NumPy Practice Text Lecture 386 Optional Extra NumPy resources Text