TensorFlow Developer Certificate in 2023: Zero to Mastery

Name: TensorFlow Developer Certificate in 2023: Zero to Mastery
Price: 9 USD
Availability: InStock

62h 43m 54s

English

Paid

November 22, 2024

Course description

Learn TensorFlow. Pass the TensorFlow Developer Certificate Exam. Get Hired as a TensorFlow developer. This course will take you from a TensorFlow beginner to being part of Google's Certification Network.

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#1: Course Outline

All Course Lessons (377)

#	Lesson Title	Duration
1	Course Outline Demo	05:22
2	What is deep learning?	04:39
3	Why use deep learning?	09:39
4	What are neural networks?	10:27
5	What is deep learning already being used for?	08:37
6	What is and why use TensorFlow?	07:57
7	What is a Tensor?	03:38
8	What we're going to cover throughout the course	04:30
9	How to approach this course	05:34
10	Creating your first tensors with TensorFlow and tf.constant()	18:46
11	Creating tensors with TensorFlow and tf.Variable()	07:08
12	Creating random tensors with TensorFlow	09:41
13	Shuffling the order of tensors	09:41
14	Creating tensors from NumPy arrays	11:56
15	Getting information from your tensors (tensor attributes	11:58
16	Indexing and expanding tensors	12:34
17	Manipulating tensors with basic operations	05:35
18	Matrix multiplication with tensors part 1	11:54
19	Matrix multiplication with tensors part 2	13:30
20	Matrix multiplication with tensors part 3	10:04
21	Changing the datatype of tensors	06:56
22	Tensor aggregation (finding the min, max, mean & more)	09:50
23	Tensor troubleshooting example (updating tensor datatypes)	06:14
24	Finding the positional minimum and maximum of a tensor (argmin and argmax) (9:31)	09:32
25	Squeezing a tensor (removing all 1-dimension axes)	03:00
26	One-hot encoding tensors	05:47
27	Trying out more tensor math operations	04:48
28	Exploring TensorFlow and NumPy's compatibility	05:44
29	Making sure our tensor operations run really fast on GPUs	10:20
30	Introduction to Neural Network Regression with TensorFlow	07:34
31	Inputs and outputs of a neural network regression model	09:00
32	Anatomy and architecture of a neural network regression model	07:56
33	Creating sample regression data (so we can model it)	12:47
34	The major steps in modelling with TensorFlow	20:16
35	Steps in improving a model with TensorFlow part 1	06:03
36	Steps in improving a model with TensorFlow part 2	09:26
37	Steps in improving a model with TensorFlow part 3	12:34
38	Evaluating a TensorFlow model part 1 ("visualise, visualise, visualise")	07:25
39	Evaluating a TensorFlow model part 2 (the three datasets)	11:02
40	Evaluating a TensorFlow model part 3 (getting a model summary)	17:19
41	Evaluating a TensorFlow model part 4 (visualising a model's layers)	07:15
42	Evaluating a TensorFlow model part 5 (visualising a model's predictions)	09:17
43	Evaluating a TensorFlow model part 6 (common regression evaluation metrics)	08:06
44	Evaluating a TensorFlow regression model part 7 (mean absolute error)	05:53
45	Evaluating a TensorFlow regression model part 7 (mean square error)	03:19
46	Setting up TensorFlow modelling experiments part 1 (start with a simple model)	13:51
47	Setting up TensorFlow modelling experiments part 2 (increasing complexity)	11:30
48	Comparing and tracking your TensorFlow modelling experiments	10:21
49	How to save a TensorFlow model	08:20
50	How to load and use a saved TensorFlow model	10:16
51	(Optional) How to save and download files from Google Colab	06:19
52	Putting together what we've learned part 1 (preparing a dataset)	13:32
53	Putting together what we've learned part 2 (building a regression model)	13:21
54	Putting together what we've learned part 3 (improving our regression model)	15:48
55	Preprocessing data with feature scaling part 1 (what is feature scaling?)	09:35
56	Preprocessing data with feature scaling part 2 (normalising our data)	10:58
57	Preprocessing data with feature scaling part 3 (fitting a model on scaled data)	07:41
58	Introduction to neural network classification in TensorFlow	08:26
59	Example classification problems (and their inputs and outputs)	06:39
60	Input and output tensors of classification problems	06:22
61	Typical architecture of neural network classification models with TensorFlow	09:37
62	Creating and viewing classification data to model	11:35
63	Checking the input and output shapes of our classification data	04:39
64	Building a not very good classification model with TensorFlow	12:11
65	Trying to improve our not very good classification model	09:14
66	Creating a function to view our model's not so good predictions	15:09
67	Make our poor classification model work for a regression dataset	12:19
68	Non-linearity part 1: Straight lines and non-straight lines	09:39
69	Non-linearity part 2: Building our first neural network with non-linearity	05:48
70	Non-linearity part 3: Upgrading our non-linear model with more layers	10:19
71	Non-linearity part 4: Modelling our non-linear data once and for all	08:38
72	Non-linearity part 5: Replicating non-linear activation functions from scratch	14:27
73	Getting great results in less time by tweaking the learning rate	14:48
74	Using the TensorFlow History object to plot a model's loss curves	06:12
75	Using callbacks to find a model's ideal learning rate	17:33
76	Training and evaluating a model with an ideal learning rate	09:21
77	Introducing more classification evaluation methods	06:05
78	Finding the accuracy of our classification model	04:18
79	Creating our first confusion matrix (to see where our model is getting confused)	08:28
80	Making our confusion matrix prettier	14:01
81	Putting things together with multi-class classification part 1: Getting the data	10:38
82	Multi-class classification part 2: Becoming one with the data	07:08
83	Multi-class classification part 3: Building a multi-class classification model	15:39
84	Multi-class classification part 4: Improving performance with normalisation	12:44
85	Multi-class classification part 5: Comparing normalised and non-normalised data	04:14
86	Multi-class classification part 6: Finding the ideal learning rate	10:39
87	Multi-class classification part 7: Evaluating our model	13:17
88	Multi-class classification part 8: Creating a confusion matrix	04:27
89	Multi-class classification part 9: Visualising random model predictions	10:43
90	What "patterns" is our model learning?	15:34
91	Introduction to Computer Vision with TensorFlow	09:37
92	Introduction to Convolutional Neural Networks (CNNs) with TensorFlow	08:00
93	Downloading an image dataset for our first Food Vision model	08:28
94	Becoming One With Data	05:06
95	Becoming One With Data Part 2	12:27
96	Becoming One With Data Part 3	04:23
97	Building an end to end CNN Model	18:18
98	Using a GPU to run our CNN model 5x faster	09:18
99	Trying a non-CNN model on our image data	08:52
100	Improving our non-CNN model by adding more layers	09:53
101	Breaking our CNN model down part 1: Becoming one with the data	09:04
102	Breaking our CNN model down part 2: Preparing to load our data	11:47
103	Breaking our CNN model down part 3: Loading our data with ImageDataGenerator	09:55
104	Breaking our CNN model down part 4: Building a baseline CNN model	08:03
105	Breaking our CNN model down part 5: Looking inside a Conv2D layer	15:21
106	Breaking our CNN model down part 6: Compiling and fitting our baseline CNN	07:15
107	Breaking our CNN model down part 7: Evaluating our CNN's training curves	11:46
108	Breaking our CNN model down part 8: Reducing overfitting with Max Pooling	13:41
109	Breaking our CNN model down part 9: Reducing overfitting with data augmentation	06:53
110	Breaking our CNN model down part 10: Visualizing our augmented data	15:05
111	Breaking our CNN model down part 11: Training a CNN model on augmented data	08:50
112	Breaking our CNN model down part 12: Discovering the power of shuffling data	10:02
113	Breaking our CNN model down part 13: Exploring options to improve our model	05:22
114	Downloading a custom image to make predictions on	04:55
115	Writing a helper function to load and preprocessing custom images	10:01
116	Making a prediction on a custom image with our trained CNN	10:09
117	Multi-class CNN's part 1: Becoming one with the data	15:00
118	Multi-class CNN's part 2: Preparing our data (turning it into tensors)	06:39
119	Multi-class CNN's part 3: Building a multi-class CNN model	07:25
120	Multi-class CNN's part 4: Fitting a multi-class CNN model to the data	06:03
121	Multi-class CNN's part 5: Evaluating our multi-class CNN model (	04:52
122	Multi-class CNN's part 6: Trying to fix overfitting by removing layers	12:20
123	Multi-class CNN's part 7: Trying to fix overfitting with data augmentation	11:47
124	Multi-class CNN's part 8: Things you could do to improve your CNN model	04:24
125	Multi-class CNN's part 9: Making predictions with our model on custom images	09:23
126	Saving and loading our trained CNN model	06:22
127	What is and why use transfer learning?	10:13
128	Downloading and preparing data for our first transfer learning model	14:40
129	Introducing Callbacks in TensorFlow and making a callback to track our models	10:02
130	Exploring the TensorFlow Hub website for pretrained models	09:52
131	Building and compiling a TensorFlow Hub feature extraction model	14:01
132	Blowing our previous models out of the water with transfer learning	09:14
133	Plotting the loss curves of our ResNet feature extraction model	07:36
134	Building and training a pre-trained EfficientNet model on our data	09:43
135	Different Types of Transfer Learning	11:41
136	Comparing Our Model's Results	15:17
137	Introduction to Transfer Learning in TensorFlow Part 2: Fine-tuning	06:17
138	Importing a script full of helper functions (and saving lots of space)	07:36
139	Downloading and turning our images into a TensorFlow BatchDataset	15:39
140	Discussing the four (actually five) modelling experiments we're running	02:16
141	Comparing the TensorFlow Keras Sequential API versus the Functional API	02:35
142	Creating our first model with the TensorFlow Keras Functional API	11:39
143	Compiling and fitting our first Functional API model	10:54
144	Getting a feature vector from our trained model	13:40
145	Drilling into the concept of a feature vector (a learned representation)	03:44
146	Downloading and preparing the data for Model 1 (1 percent of training data)	09:52
147	Building a data augmentation layer to use inside our model	12:07
148	Visualising what happens when images pass through our data augmentation layer	10:56
149	Building Model 1 (with a data augmentation layer and 1% of training data)	15:56
150	Building Model 2 (with a data augmentation layer and 10% of training data)	16:38
151	Creating a ModelCheckpoint to save our model's weights during training	07:26
152	Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)	07:15
153	Loading and comparing saved weights to our existing trained Model 2	07:18
154	Preparing Model 3 (our first fine-tuned model)	20:27
155	Fitting and evaluating Model 3 (our first fine-tuned model)	07:46
156	Comparing our model's results before and after fine-tuning	10:27
157	Downloading and preparing data for our biggest experiment yet (Model 4)	06:25
158	Preparing our final modelling experiment (Model 4)	12:01
159	Fine-tuning Model 4 on 100% of the training data and evaluating its results	10:20
160	Comparing our modelling experiment results in TensorBoard	10:47
161	How to view and delete previous TensorBoard experiments	02:05
162	Introduction to Transfer Learning Part 3: Scaling Up	06:20
163	Getting helper functions ready and downloading data to model	13:35
164	Outlining the model we're going to build and building a ModelCheckpoint callback	05:39
165	Creating a data augmentation layer to use with our model	04:40
166	Creating a headless EfficientNetB0 model with data augmentation built in	08:59
167	Fitting and evaluating our biggest transfer learning model yet	07:57
168	Unfreezing some layers in our base model to prepare for fine-tuning	11:29
169	Fine-tuning our feature extraction model and evaluating its performance	08:24
170	Saving and loading our trained model	06:26
171	Downloading a pretrained model to make and evaluate predictions with	06:35
172	Making predictions with our trained model on 25,250 test samples	12:47
173	Unravelling our test dataset for comparing ground truth labels to predictions	06:06
174	Confirming our model's predictions are in the same order as the test labels	05:18
175	Creating a confusion matrix for our model's 101 different classes	12:08
176	Evaluating every individual class in our dataset	14:17
177	Plotting our model's F1-scores for each separate class	07:37
178	Creating a function to load and prepare images for making predictions	12:09
179	Making predictions on our test images and evaluating them	16:07
180	Discussing the benefits of finding your model's most wrong predictions	06:10
181	Writing code to uncover our model's most wrong predictions	11:17
182	Plotting and visualizing the samples our model got most wrong	10:37
183	Making predictions on and plotting our own custom images	09:50
184	Introduction to Milestone Project 1: Food Vision Big™	05:45
185	Making sure we have access to the right GPU for mixed precision training	10:18
186	Getting helper functions ready	03:07
187	Introduction to TensorFlow Datasets (TFDS)	12:04
188	Exploring and becoming one with the data (Food101 from TensorFlow Datasets)	15:57
189	Creating a preprocessing function to prepare our data for modelling	15:51
190	Batching and preparing our datasets (to make them run fast)	13:48
191	Exploring what happens when we batch and prefetch our data	06:50
192	Creating modelling callbacks for our feature extraction model	07:15
193	Turning on mixed precision training with TensorFlow	10:06
194	Creating a feature extraction model capable of using mixed precision training	12:43
195	Checking to see if our model is using mixed precision training layer by layer	07:57
196	Training and evaluating a feature extraction model (Food Vision Big™)	10:20
197	Introducing your Milestone Project 1 challenge: build a model to beat DeepFood	07:48
198	Introduction to Natural Language Processing (NLP) and Sequence Problems	12:52
199	Example NLP inputs and outputs	07:23
200	The typical architecture of a Recurrent Neural Network (RNN)	09:04
201	Preparing a notebook for our first NLP with TensorFlow project	08:53
202	Becoming one with the data and visualizing a text dataset	16:42
203	Splitting data into training and validation sets	06:27
204	Converting text data to numbers using tokenisation and embeddings (overview)	09:23
205	Setting up a TensorFlow TextVectorization layer to convert text to numbers	17:11
206	Mapping the TextVectorization layer to text data and turning it into numbers	11:03
207	Creating an Embedding layer to turn tokenised text into embedding vectors	12:28
208	Discussing the various modelling experiments we're going to run	08:58
209	Model 0: Building a baseline model to try and improve upon	09:26
210	Creating a function to track and evaluate our model's results	12:15
211	Model 1: Building, fitting and evaluating our first deep model on text data	20:52
212	Visualizing our model's learned word embeddings with TensorFlow's projector tool	20:44
213	High-level overview of Recurrent Neural Networks (RNNs) + where to learn more	09:35
214	Model 2: Building, fitting and evaluating our first TensorFlow RNN model (LSTM)	18:17
215	Model 3: Building, fitting and evaluating a GRU-cell powered RNN	16:57
216	Model 4: Building, fitting and evaluating a bidirectional RNN model	19:35
217	Discussing the intuition behind Conv1D neural networks for text and sequences	19:32
218	Model 5: Building, fitting and evaluating a 1D CNN for text	09:58
219	Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP)	13:46
220	Model 6: Building, training and evaluating a transfer learning model for NLP	10:46
221	Preparing subsets of data for model 7 (same as model 6 but 10% of data)	10:53
222	Model 7: Building, training and evaluating a transfer learning model on 10% data	10:05
223	Fixing our data leakage issue with model 7 and retraining it	13:43
224	Comparing all our modelling experiments evaluation metrics	13:15
225	Uploading our model's training logs to TensorBoard and comparing them	11:15
226	Saving and loading in a trained NLP model with TensorFlow	10:26
227	Downloading a pretrained model and preparing data to investigate predictions	13:25
228	Visualizing our model's most wrong predictions	08:29
229	Making and visualizing predictions on the test dataset	08:28
230	Understanding the concept of the speed/score tradeoff	15:02
231	Introduction to Milestone Project 2: SkimLit	14:21
232	What we're going to cover in Milestone Project 2 (NLP for medical abstracts)	07:23
233	SkimLit inputs and outputs	11:03
234	Setting up our notebook for Milestone Project 2 (getting the data)	14:59
235	Visualizing examples from the dataset (becoming one with the data)	13:19
236	Writing a preprocessing function to structure our data for modelling	19:51
237	Performing visual data analysis on our preprocessed text	07:56
238	Turning our target labels into numbers (ML models require numbers)	13:16
239	Model 0: Creating, fitting and evaluating a baseline model for SkimLit	09:26
240	Preparing our data for deep sequence models	09:56
241	Creating a text vectoriser to map our tokens (text) to numbers	14:08
242	Creating a custom token embedding layer with TensorFlow	09:15
243	Creating fast loading dataset with the TensorFlow tf.data API	09:50
244	Model 1: Building, fitting and evaluating a Conv1D with token embeddings	17:22
245	Preparing a pretrained embedding layer from TensorFlow Hub for Model 2	10:54
246	Model 2: Building, fitting and evaluating a Conv1D model with token embeddings	11:31
247	Creating a character-level tokeniser with TensorFlow's TextVectorization layer	23:25
248	Creating a character-level embedding layer with tf.keras.layers.Embedding	07:45
249	Model 3: Building, fitting and evaluating a Conv1D model on character embeddings	13:46
250	Discussing how we're going to build Model 4 (character + token embeddings)	06:05
251	Model 4: Building a multi-input model (hybrid token + character embeddings)	15:37
252	Model 4: Plotting and visually exploring different data inputs	07:33
253	Crafting multi-input fast loading tf.data datasets for Model 4	08:42
254	Model 4: Building, fitting and evaluating a hybrid embedding model	13:19
255	Model 5: Adding positional embeddings via feature engineering (overview)	07:19
256	Encoding the line number feature to used with Model 5	12:26
257	Encoding the total lines feature to be used with Model 5	07:57
258	Model 5: Building the foundations of a tribrid embedding model	09:20
259	Model 5: Completing the build of a tribrid embedding model for sequences	14:09
260	Visually inspecting the architecture of our tribrid embedding model	10:26
261	Creating multi-level data input pipelines for Model 5 with the tf.data API	09:01
262	Bringing SkimLit to life!!! (fitting and evaluating Model 5)	10:36
263	Comparing the performance of all of our modelling experiments	09:37
264	Saving, loading & testing our best performing model	07:49
265	Congratulations and your challenge before heading to the next module	12:34
266	Introduction to Milestone Project 3 (BitPredict) & where you can get help	03:54
267	What is a time series problem and example forecasting problems at Uber	07:47
268	Example forecasting problems in daily life	04:53
269	What can be forecast?	07:58
270	What we're going to cover (broadly)	02:36
271	Time series forecasting inputs and outputs	08:56
272	Downloading and inspecting our Bitcoin historical dataset	14:59
273	Different kinds of time series patterns & different amounts of feature variables	07:40
274	Visualizing our Bitcoin historical data with pandas	04:53
275	Reading in our Bitcoin data with Python's CSV module	10:59
276	Creating train and test splits for time series (the wrong way)	08:38
277	Creating train and test splits for time series (the right way)	07:13
278	Creating a plotting function to visualize our time series data	07:58
279	Discussing the various modelling experiments were going to be running	09:12
280	Model 0: Making and visualizing a naive forecast model	12:17
281	Discussing some of the most common time series evaluation metrics	11:12
282	Implementing MASE with TensorFlow	09:39
283	Creating a function to evaluate our model's forecasts with various metrics	10:12
284	Discussing other non-TensorFlow kinds of time series forecasting models	05:07
285	Formatting data Part 2: Creating a function to label our windowed time series	13:02
286	Discussing the use of windows and horizons in time series data	07:51
287	Writing a preprocessing function to turn time series data into windows & labels	23:36
288	Turning our windowed time series data into training and test sets	10:02
289	Creating a modelling checkpoint callback to save our best performing model	07:26
290	Model 1: Building, compiling and fitting a deep learning model on Bitcoin data	16:59
291	Creating a function to make predictions with our trained models	14:04
292	Model 2: Building, fitting and evaluating a deep model with a larger window size-27	17:44
293	Model 3: Building, fitting and evaluating a model with a larger horizon size	13:16
294	Adjusting the evaluation function to work for predictions with larger horizons	08:35
295	Model 3: Visualizing the results	08:45
296	Comparing our modelling experiments so far and discussing autocorrelation	09:45
297	Preparing data for building a Conv1D model	13:22
298	Model 4: Building, fitting and evaluating a Conv1D model on our Bitcoin data	14:52
299	Model 5: Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data	16:06
300	Investigating how to turn our univariate time series into multivariate	13:53
301	Creating and plotting a multivariate time series with BTC price and block reward	12:13
302	Preparing our multivariate time series for a model	13:38
303	Model 6: Building, fitting and evaluating a multivariate time series model	09:26
304	Model 7: Discussing what we're going to be doing with the N-BEATS algorithm	09:40
305	Model 7: Replicating the N-BEATS basic block with TensorFlow layer subclassing	18:39
306	Model 7: Testing our N-BEATS block implementation with dummy data inputs	15:03
307	Model 7: Setting up hyperparameters for the N-BEATS algorithm	08:51
308	Model 7: Getting ready for residual connections	12:56
309	Model 7: Outlining the steps we're going to take to build the N-BEATS model	10:06
310	Model 7: Putting together the pieces of the puzzle of the N-BEATS model	22:23
311	Model 7: Plotting the N-BEATS algorithm we've created and admiring its beauty	06:47
312	Model 8: Ensemble model overview	04:44
313	Model 8: Building, compiling and fitting an ensemble of models	20:05
314	Model 8: Making and evaluating predictions with our ensemble model	16:10
315	Discussing the importance of prediction intervals in forecasting	12:57
316	Getting the upper and lower bounds of our prediction intervals	07:58
317	Plotting the prediction intervals of our ensemble model predictions	13:03
318	(Optional) Discussing the types of uncertainty in machine learning	13:42
319	Model 9: Preparing data to create a model capable of predicting into the future	08:25
320	Model 9: Building, compiling and fitting a future predictions model	05:02
321	Model 9: Discussing what's required for our model to make future predictions	08:31
322	Model 9: Creating a function to make forecasts into the future	12:09
323	Model 9: Plotting our model's future forecasts	13:10
324	Model 10: Introducing the turkey problem and making data for it	14:16
325	Model 10: Building a model to predict on turkey data (why forecasting is BS)	13:39
326	Comparing the results of all of our models and discussing where to go next	13:00
327	What is the TensorFlow Developer Certification?	05:29
328	Why the TensorFlow Developer Certification?	06:58
329	How to prepare (your brain) for the TensorFlow Developer Certification	08:15
330	How to prepare (your computer) for the TensorFlow Developer Certification	12:44
331	What to do after the TensorFlow Developer Certification exam	02:14
332	What is Machine Learning?	04:52
333	AI/Machine Learning/Data Science	06:53
334	Exercise: Machine Learning Playground	06:17
335	How Did We Get Here?	06:04
336	Exercise: YouTube Recommendation Engine	04:25
337	Types of Machine Learning	04:42
338	What Is Machine Learning? Round 2	04:45
339	Section Review	01:49
340	Section Overview	02:39
341	Introducing Our Framework	03:09
342	6 Step Machine Learning Framework	05:00
343	Types of Machine Learning Problems	10:33
344	Types of Data	04:51
345	Types of Evaluation	03:32
346	Features In Data	05:59
347	Modelling - Splitting Data	05:23
348	Modelling - Picking the Model	04:36
349	Modelling - Tuning	03:18
350	Modelling - Comparison	03:36
351	Experimentation	09:33
352	Tools We Will Use	04:01
353	Section Overview	02:28
354	Pandas Introduction	04:30
355	Series, Data Frames and CSVs	13:22
356	Describing Data with Pandas	09:49
357	Selecting and Viewing Data with Pandas	11:09
358	Selecting and Viewing Data with Pandas Part 2	13:07
359	Manipulating Data	13:57
360	Manipulating Data 2	09:57
361	Manipulating Data 3	10:13
362	How To Download The Course Assignments	02:41
363	Section Overview	07:44
364	NumPy Introduction	05:18
365	NumPy DataTypes and Attributes	14:06
366	Creating NumPy Arrays	09:23
367	NumPy Random Seed	07:18
368	Viewing Arrays and Matrices	09:36
369	Manipulating Arrays	11:32
370	Manipulating Arrays 2	09:45
371	Standard Deviation and Variance	07:11
372	Reshape and Transpose	07:27
373	Dot Product vs Element Wise	11:46
374	Exercise: Nut Butter Store Sales	03:34
375	Comparison Operators	13:05
376	Sorting Arrays	06:20
377	Turn Images Into NumPy Arrays	07:38

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