TensorFlow Developer Professional Certificate

DeepLearningAI · Intermediate ·📐 ML Fundamentals ·3mo ago
Skills: ML Pipelines85%

Key Takeaways

Introduces the TensorFlow Developer Professional Certificate, covering TensorFlow basics and deep learning applications

Full Transcript

Welcome to TensorFlow from basics to mastery. Some of you may have taken deep learning or machine learning from me and learn about the amazing things you can now do with deep learning machine learning. One of the best tools you can use to implement these algorithms is TensorFlow. Learning algorithms can be quite complicated and today programming frameworks like TensorFlow, PyTorch, Caffe, and many others can save you a lot of time. These tools can be complicated and what this set of courses will do is teach you how to use TensorFlow effectively. In order to teach much of these courses, I'm absolutely thrilled to introduce Laurence Moroney. >> Thank you, Andrew. He is a developer advocate at Google and has been working on Google AI and TensorFlow. Laurence has also written over 30 programming books including four sci-fi novels. Yeah, exactly. I've I've been busy. I I really enjoy writing, but the one thing I enjoy even more is like learning and teaching AI. So, and actually I've learned from the specializations that you mentioned and I learned from your courses, so it's a real honor to be here with you. Oh, thank you. I I I did not know that you were taking my courses well. Thank you. Oh, definitely. So, it's a big fan and that's really what got me into AI was It's actually a long story. I had started doing AI many, many years ago back when it was things like Prolog and Lisp and all that, but now when we've gotten more into machine learning and deep learning with neural networks, I needed a place to learn it and actually learned it from your courses. So, it's it's it's been exciting to be actually coming full circle and now teaching it myself, too. Thank you. I should not know. So, thank you for sharing that. I caught you by surprise. >> [laughter] >> So, it's like where the industry's at right now is one of the things that like really excites me because it's like it's just it's it's really it's exploding, right? There's a deep learning and and machine learning skills are becoming ever more important and opening up whole new scenarios. One of the strange things and exciting things about machine learning and AI is that it's no longer just a technical thing limited to the software industry. So, that everyone in or at least every industry needs to figure this out. Yeah, yeah. And it's exciting from a developer's perspective because it's there's a new paradigm. And that kind of And the new paradigm to me is opening up scenarios that weren't previously possible. Things that were too difficult for me to write programs for. And so and whatever it's like whenever a new paradigm comes and these new tools come and you can open up new scenarios, then that opens up great new opportunities. Yeah, and I think one of the tragic things today is even though the whole world sees the promise and the hope of these machine learning and AI capabilities changing so many things, the world just doesn't have enough AI developers today. Exactly. I mean, there I've seen surveys of like, you know, 25, 26 million software developers and like maybe 300,000 AI practitioners. So, part of my personal passion is to try and turn like those 24.7 non-AI practitioners and significant portion of them into people who can understand AI and who can build the the new and exciting things that we can't think of. Yeah. So, I think if you finish this set of courses and learn how to code in TensorFlow, hopefully that will help you do some of this exciting work and maybe become an AI developer. So, in the next video, you'll hear Laurence talk about the differences between traditional programming paradigms versus the machine learning and deep learning programming paradigm. And you'll also hear about how to fit an X to Y data relationship, how to fit a straight line to data. So, please go on to the next video. Thank you.

Original Description

Get started now: https://learn.deeplearning.ai/specializations/tensorflow-developer-professional-certificate The TensorFlow Developer Professional Certificate Specialization is aimed at developers who want to learn about TensorFlow to build AI applications: learn the basics on how to use TensorFlow to build, train, and optimize deep neural networks and dive deep into Computer Vision, Natural Language Processing, and Time Series Analysis. TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Who should join? This is a hands-on specialization for developers who want to learn TensorFlow to build AI applications. A high-school level of mathematics and prior experience with Python will help learners get the most out of this class. Prior machine learning or deep learning knowledge is helpful but not required. Enroll now and propel your career forward with cutting-edge skills and hands-on experience!
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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
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2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
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9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
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10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
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11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
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12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
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14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
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15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
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16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
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17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
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18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
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19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
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20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
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21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
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22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
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23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
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24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
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25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
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26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
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27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
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28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
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29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
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30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
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31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
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32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
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33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
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34 TensorFlow (C2W3L11)
TensorFlow (C2W3L11)
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35 Activation Functions (C1W3L06)
Activation Functions (C1W3L06)
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36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
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37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
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38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
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39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
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40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
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41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
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42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
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43 Train/Dev/Test Sets (C2W1L01)
Train/Dev/Test Sets (C2W1L01)
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44 Deep L-Layer Neural Network (C1W4L01)
Deep L-Layer Neural Network (C1W4L01)
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45 Random Initialization (C1W3L11)
Random Initialization (C1W3L11)
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46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
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47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
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48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
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49 Parameters vs Hyperparameters (C1W4L07)
Parameters vs Hyperparameters (C1W4L07)
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50 Vectorizing Across Multiple Examples (C1W3L04)
Vectorizing Across Multiple Examples (C1W3L04)
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51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
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52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
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53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
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54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
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55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
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56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
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57 Weight Initialization in a Deep Network (C2W1L11)
Weight Initialization in a Deep Network (C2W1L11)
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58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
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59 Regularization (C2W1L04)
Regularization (C2W1L04)
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60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
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