Deep Learning With Tensorflow 2.0 [2023]

Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case

What you'll learn

Gain a Strong Understanding of TensorFlow - Google’s Cutting-Edge Deep Learning Framework

Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow

Set Yourself Apart with Hands-on Deep and Machine Learning Experience

Grasp the Mathematics Behind Deep Learning Algorithms

Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules

Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization

Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

Requirements

Some basic Python programming skills

You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course.

All software and data used in the course are free.

Description

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?They are all masters of deep learning. We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?There are two routes you can take: The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there. The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge optimizations, get hands on with TensorFlow and even build your very own algorithm and put it through training!Wow, that’s going to look great on your resume!Speaking of resumes, you also get a certificate upon completion which employers can verify that you have successfully finished a prestigious 365 Careers course – and one of our best at that!Now, I can see you’re bragging a little, but I admit you have peaked my interest. What else does your course offer that will make my resume shine?Trust us, after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.Understand the backpropagation process, intuitively and mathematically.You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learningGet to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, tooLearn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included!Also, I don’t know if we’ve mentioned this, but you will have created your very own Deep Learning Algorithm after only 1 hour of the course.It’s this hands-on experience that will really make your resume stand outThis all sounds great, but I am a little overwhelmed, I’m afraid I may not have enough experience.We admit, you will need at least a little understanding of Python programming but nothing to worry about. We start with the basics and take you step by step toward building your very first (or second, or third etc.) Deep Learning algorithm – we program everything in Python and explain each line of code.We do this early on and it will give you the confidence to carry on to the more complex topics we cover.All the sophisticated concepts we teach are explained intuitively. Our beautifully animated videos and step by step approach ensures the course is a fun and engaging experience for all levels.We want everyone to get the most out of our course, and the best way to do that is to keep our students motivated. So, we worked hard to ensure that students with varying skills are challenged without being overwhelmed. Each lecture builds upon the last and practical exercises mean that you can practice what you’ve learned before moving on to the next step. And of course, we are available to answer any queries you have. In fact, we aim to answer any and all question within 1 business day. We don’t just chuck you in the pool then head to the bar and let you fend for yourself. Remember, we don’t just want you to enrol – we want you to complete the course and become a Master of Deep Learning.OK, awesome! I feel much better about my level of experience now, but we haven’t discussed yours! How do I know you can teach me to become a Master of Deep Learning?That’s an understandable worry, but it’s one we have no problem removing.We are 365 Careers and we’ve been creating online courses for ages. We have over 1,750,000 students and enjoy high ratings for all our Udemy courses. We are a team of experts who are all, at heart, teachers. We believe knowledge should be shared and not just through boring text books but in engaging and fun ways.We are well aware how difficult it is to build your knowledge and skills in the data science field, it’s so new and has grown so fast that the education sector has struggled to keep up and offer any substantial methods of teaching these topic areas. We wanted to change things – to rock the boat – so we developed our unique teaching style, one that countless students have enjoyed and thrived with.And between us, we think this course is one of our favourites, so if this is your first time with us, you’re in for a treat. If it’s not and you’ve taken one of our courses before, then, you’re still in for a treat!I’ve been hurt before though, how can I be sure you won’t let me down?Easy, with Udemy’s 30-day money back guarantee. We strive for the best and believe that our courses are the best out there. But you know what, everyone is different, and we understand that. So, we have no problem offering this guarantee, we want students who will complete and get the most out of this course. If you are one of the few who finds this course not what you wanted or expected then, get your money back. No questions, no risk, no problem.Great, that takes a load of my shoulders. What next?Click on the ‘Buy now’ button and take that first step toward a satisfying data science career and becoming a Master of Deep Learning.

Overview

Section 1: Welcome! Course introduction

Lecture 1 Meet your instructors and why you should study machine learning?

Lecture 2 What does the course cover?

Lecture 3 Download All Resources and Important FAQ

Section 2: Introduction to neural networks

Lecture 4 Introduction to neural networks

Lecture 5 Training the model

Lecture 6 Types of machine learning

Lecture 7 The linear model

Lecture 8 Need Help with Linear Algebra?

Lecture 9 The linear model. Multiple inputs

Lecture 10 The linear model. Multiple inputs and multiple outputs

Lecture 11 Graphical representation

Lecture 12 The objective function

Lecture 13 L2-norm loss

Lecture 14 Cross-entropy loss

Lecture 15 One parameter gradient descent

Lecture 16 N-parameter gradient descent

Section 3: Setting up the working environment

Lecture 17 Setting up the environment - An introduction - Do not skip, please!

Lecture 18 Why Python and why Jupyter?

Lecture 19 Installing Anaconda

Lecture 20 The Jupyter dashboard - part 1

Lecture 21 The Jupyter dashboard - part 2

Lecture 22 Jupyter Shortcuts

Lecture 23 Installing TensorFlow 2

Lecture 24 Installing packages - exercise

Lecture 25 Installing packages - solution

Section 4: Minimal example - your first machine learning algorithm

Lecture 26 Minimal example - part 1

Lecture 27 Minimal example - part 2

Lecture 28 Minimal example - part 3

Lecture 29 Minimal example - part 4

Lecture 30 Minimal example - Exercises

Section 5: TensorFlow - An introduction

Lecture 31 TensorFlow outline

Lecture 32 TensorFlow 2 intro

Lecture 33 A Note on Coding in TensorFlow

Lecture 34 Types of file formats in TensorFlow and data handling

Lecture 35 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss

Lecture 36 Interpreting the result and extracting the weights and bias

Lecture 37 Cutomizing your model

Lecture 38 Minimal example with TensorFlow - Exercises

Section 6: Going deeper: Introduction to deep neural networks

Lecture 39 Layers

Lecture 40 What is a deep net?

Lecture 41 Understanding deep nets in depth

Lecture 42 Why do we need non-linearities?

Lecture 43 Activation functions

Lecture 44 Softmax activation

Lecture 45 Backpropagation

Lecture 46 Backpropagation - visual representation

Section 7: Backpropagation. A peek into the Mathematics of Optimization

Lecture 47 Backpropagation. A peek into the Mathematics of Optimization

Section 8: Overfitting

Lecture 48 Underfitting and overfitting

Lecture 49 Underfitting and overfitting - classification

Lecture 50 Training and validation

Lecture 51 Training, validation, and test

Lecture 52 N-fold cross validation

Lecture 53 Early stopping

Section 9: Initialization

Lecture 54 Initialization - Introduction

Lecture 55 Types of simple initializations

Lecture 56 Xavier initialization

Section 10: Gradient descent and learning rates

Lecture 57 Stochastic gradient descent

Lecture 58 Gradient descent pitfalls

Lecture 59 Momentum

Lecture 60 Learning rate schedules

Lecture 61 Learning rate schedules. A picture

Lecture 62 Adaptive learning rate schedules

Lecture 63 Adaptive moment estimation

Section 11: Preprocessing

Lecture 64 Preprocessing introduction

Lecture 65 Basic preprocessing

Lecture 66 Standardization

Lecture 67 Dealing with categorical data

Lecture 68 One-hot and binary encoding

Section 12: The MNIST example

Lecture 69 The dataset

Lecture 70 How to tackle the MNIST

Lecture 71 Importing the relevant packages and load the data

Lecture 72 Preprocess the data - create a validation dataset and scale the data

Lecture 73 Preprocess the data - scale the test data

Lecture 74 Preprocess the data - shuffle and batch the data

Lecture 75 Preprocess the data - shuffle and batch the data

Lecture 76 Outline the model

Lecture 77 Select the loss and the optimizer

Lecture 78 Learning

Lecture 79 MNIST - exercises

Lecture 80 MNIST - solutions

Lecture 81 Testing the model

Section 13: Business case

Lecture 82 Exploring the dataset and identifying predictors

Lecture 83 Outlining the business case solution

Lecture 84 Balancing the dataset

Lecture 85 Preprocessing the data

Lecture 86 Preprocessing exercise

Lecture 87 Load the preprocessed data

Lecture 88 Load the preprocessed data - Exercise

Lecture 89 Learning and interpreting the result

Lecture 90 Setting an early stopping mechanism

Lecture 91 Setting an early stopping mechanism - Exercise

Lecture 92 Testing the model

Lecture 93 Final exercise

Section 14: Appendix: Linear Algebra Fundamentals

Lecture 94 What is a Matrix?

Lecture 95 Scalars and Vectors

Lecture 96 Linear Algebra and Geometry

Lecture 97 Scalars, Vectors and Matrices in Python

Lecture 98 Tensors

Lecture 99 Addition and Subtraction of Matrices

Lecture 100 Errors when Adding Matrices

Lecture 101 Transpose of a Matrix

Lecture 102 Dot Product of Vectors

Lecture 103 Dot Product of Matrices

Lecture 104 Why is Linear Algebra Useful?

Section 15: Conclusion

Lecture 105 See how much you have learned

Lecture 106 What’s further out there in the machine and deep learning world

Lecture 107 An overview of CNNs

Lecture 108 How DeepMind uses deep learning

Lecture 109 An overview of RNNs

Lecture 110 An overview of non-NN approaches

Section 16: Bonus lecture

Lecture 111 Bonus lecture: Next steps

Aspiring data scientists,People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence,,Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch