Kleio T.
After 365:
Bioinformatics and Neuroinformatics student
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Online Course
Deep Learning with TensorFlow 2
with
Iskren Vankov
and
Iliya Valchanov
Master deep learning in Python with TensorFlow 2: Apply neural networks to solve real-world data science challenges
17614 students already have enrolled
Skill level:
Advanced
Duration:
5 hours
-
Lessons (12 hours)
-
Practice exams (16 minutes)
- Lessons (12 hours)
- Practice exams (16 minutes)
CPE credits:
11.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master the math behind deep learning algorithms.
- Build and customize machine learning algorithms from scratch.
- Understand key deep learning concepts to optimize neural networks.
- Prevent overfitting with early stopping and improve generalizability.
- Solve complex real-world problems using TensorFlow 2.
Topics & tools
pythontheorymachine learningprogrammingmathematicsdeep learningtensorflowneural networksmachine and deep learning
Your instructor
Oxford Graduate | Quantum Computing Researcher | AI & Machine Learning Expert
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Computer Science
Media and recognition
- Published in Quantum Science and Technology
- Microsoft Research Award: Best Informatics Dissertation
- BSc CompSci & Physics Prize---top mark in program
- Graduate of Oxford, Caltech & Edinburgh, First Class Honors
CEO of Team-GPT | AI Educator | Co-founder of 365 Data Science & 3veta
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, International Economics, Management, and Finance
Course OVERVIEW
Description
CPE Credits: 11.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team. Not to mention that the field of machine learning is the driving force of artificial intelligence. This course will teach you how to leverage deep learning and neural networks for the purposes of data science. The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework.
Prerequisites
- Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab)
- Intermediate Python and machine learning knowledge is required.
- Familiarity with NumPy and neural network fundamentals is recommended.
Advanced preparation
Curriculum
93 lessons
122 exercises
4 exams
- 2. Neural networks Intro42 MinThe basic logic behind training an algorithm involves four ingredients: data, model, objective function, and an optimization algorithm. In this part of the course, we describe each of them and build a solid foundation that allows you to understand the idea behind using neural networks. After completing this chapter, you will know what the various types of machine learning are, how to train a machine learning model, and understand terms like objective function, L2-norm loss, cross-entropy loss, one gradient descent, and n-parameter gradient descent.Introduction to neural networks FreeExercise FreeTraining the model theory FreeExercise FreeTypes of machine learning FreeExercise FreeThe linear model FreeExercise FreeThe linear model. Multiple inputs. FreeExercise FreeThe linear model. Multiple inputs and multiple outputs FreeExercise FreeGraphical representation FreeExercise FreeThe objective function FreeExercise FreeL2-norm loss FreeExercise FreeCross-entropy loss FreeExercise FreeOne-parameter gradient descent FreeExercise FreeN-parameter gradient descent FreeExercise Free
- 3. Setting up the environment24 MinHere, we will show you how to install the Jupyter Notebook (the environment we will use to code in Python) and how to import the relevant libraries. Because this course is based in Python, we will be working with several popular libraries: NumPy, SciPy, scikit-learn and TensorFlow.
- 4. Minimal example20 MinIt is time to build your first machine learning algorithm. We will show you how to import the relevant libraries, how to generate random input data for the model to train on, how to create the targets the model will aim at, and how to plot the training data. The mechanics of this model exemplify how all regressions you’ve run in different packages (scikit-learn) or software (Excel) work. This is an iterative method aiming to find the best-fitting line.
- 5. Introduction to TensorFlow 224 MinHaving created the simple net, we 'translate' it to TensorFlow. This is our way of taking a simple, well-understood problem to introduce the syntax and logic of TensorFlow.TensorFlow OutlineTensorFlow 2 IntroExerciseA note on coding in TensorFlowTypes of file formats in Tensorflow and data handlingExerciseModel layout - inputs, outputs, targets, weights, bias, optimizer, and lossExerciseInterpreting the result and extracting the weights and biasExerciseCustomizing your modelIntroduction to TensorFlow - ExercisesPractice exam
- 6. Deep nets overview25 MinFrom this section on, we will explore deep neural networks. Most real-life dependencies cannot be modelled with a simple linear combination (as we have done so far). And because we want to be better forecasters, we need better models. Most of the time, this means working with a model that is more sophisticated than a liner model. In this section, we will talk about concepts like deep nets, non-linearities, activation functions, softmax activation, and backpropagation. Sounds a bit complex, but we have made it easy for you!
- 7. Backpropagation (optional)1 MinТo get a truly deep understanding of deep neural networks, one will have to look at the mathematics of it. As backpropagation is at the core of the optimization process, we wanted to introduce you to it. This is not a necessary part of the course, as in TensorFlow, sklearn, or any other machine learning package (as opposed to simply NumPy), will have backpropagation methods incorporated.
- 8. Overfitting20 MinSome of the most common pitfalls you can have when creating predictive models, and especially in deep learning, is to either underfit or overfit your data. This means to either take less advantage of the machine learning algorithm than you could have due to insufficient training (underfitting), or alternatively create a model that fits the training data too much (overtrain the model) which makes it unsuitable for a different sample (overfitting).
- 9. Initialization9 MinInitialization is the process in which we set the initial values of weights, and it's an important aspect of building a machine learning model. In this section, you will learn how to initialize the weights of your model and how to apply Xavier initialization.
- 10. Optimizers21 MinThe gradient descent iterates the whole training set before updating the weights. Every iteration updates the weights in a relatively small way. Here, you will learn common pitfalls related to this method and how to boost them, using stochastic gradient descent, momentum, learning rate schedules, and adaptive learning rates.
- 11. Preprocessing15 MinA large part of the effort data scientists make when creating a new model is related to preprocessing. This process refers to any manipulation we apply to the dataset before running it and training the model. Learning how to preprocess data is fundamental for anyone who wants to be able to create machine learning models, as no meaningful framework can simply take raw data and provide an answer. In this part of the course, we will show you how to prepare your data for analysis and modeling.
- 12. Deeper example40 MinOnce we have learned all the relevant theory, we are ready to jump into deep waters. We explore the 'Hello world' of deep learning - the MNIST dataset, where we classify 60,000 images into 10 classes (the 10 digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10).MNIST datasetHow to tackle the MNIST datasetMNIST - Importing libraries and dataPreprocess the data - create a validation dataset and scale the dataPreprocess the data - scale the test dataPreprocess the data - shuffle and batchPreprocess the data - shuffle and batch ExerciseOutline the modelSelect the loss and the optimizerLearningMNIST - ExercisesTesting the modelPractice exam
- 13. Business case43 MinData science without an application is nothing but research. Since we at 365 believe that the skills you acquire should be relevant for your work, we finish the course with a business case, where we implement all the deep learning knowledge you've acquired.Exploring the dataset and identifying predictorsOutlining the business case solutionBalancing a datasetPreprocessing the dataPreprocessing ExerciseLoad the preprocessed dataLoad the preprocessed data ExerciseLearning and interpreting the resultSetting an early stopping mechanismSetting an early stopping mechanism ExerciseTesting the business modelHomework
- 14. Conclusion19 MinThis section is designed to help you continue your specialization and data science journey. In this section, we discuss what is further out there in the machine learning world, how Google’s DeepMind uses machine learning, what are RNNs, and what non-NN approaches are there.
- 15. Course exam20 Min
Free preview
1.1 Why machine learning
7 min
2.1 Introduction to neural networks
4 min
2.3 Training the model theory
3 min
2.5 Types of machine learning
4 min
2.7 The linear model
3 min
2.9 The linear model. Multiple inputs.
2 min
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