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[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]

[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]

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By : 365 Careers

Build deep learning algorithms with TensorFlow 2.0, dive into neural networks, and apply your skills in a business case.

Video Details

ISBN 9781839218163
Course Length 4 hours 55 minutes

Learn

• Gain a strong understanding of TensorFlow – Google’s cutting-edge deep learning framework
• Understand backpropagation, Stochastic Gradient Descent, batching, momentum, and learning rate schedules
• Master 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

About

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what special quality do they have in common?

They are all masters of deep learning.

We often hear about AI, or self-driving cars, or 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 single algorithm that rules them all.

In this course, we’ll teach you to master Deep Learning. We start with the basics and take you step by step toward building your very first (or second, or third…) deep learning algorithm; we program everything in Python and explain each line of code. We do this early on to give you the confidence to progress to the more complex topics we cover.

All sophisticated concepts we teach are explained intuitively. You’ll get fully acquainted with TensorFlow and NumPy, two tools that are essential for creating and understanding Deep Learning algorithms. You’ll explore layers, their building blocks, and activations – sigmoid, tanh, ReLu, softmax, and more.

You’ll 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 learning. You’ll master state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too. you’ll learn how to build deep neural networks using real data, implemented by real companies in the real world—templates included! Also, you will create your very own deep learning algorithm.

Take the first step toward a satisfying data science career and becoming a Master of Deep Learning.

All the code files are placed at https://github.com/PacktPublishing/Master-Deep-Learning-with-TensorFlow-2.0-in-Python-2019

Features:

• Build deep learning algorithms from scratch in Python using NumPy and TensorFlow
• Set yourself apart from the competition with hands-on deep- and machine-learning experience
• Grasp the math behind deep learning algorithms.




File list
  • [FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]
  • 0. Websites you may like/How you can help Team-FTU.txt 237 B
  • 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4 84.7 MB
  • 01.Welcome! Course introduction/0102.What does the course cover.mp4 39.1 MB
  • 02.Introduction to neural networks/0201.Introduction to neural networks.mp4 45.7 MB
  • 02.Introduction to neural networks/0202.Training the model.mp4 26.8 MB
  • 02.Introduction to neural networks/0203.Types of machine learning.mp4 40.8 MB
  • 02.Introduction to neural networks/0204.The linear model.mp4 26 MB
  • 02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp4 23.7 MB
  • 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4 42.2 MB
  • 02.Introduction to neural networks/0207.Graphical representation.mp4 22 MB
  • 02.Introduction to neural networks/0208.The objective function.mp4 17.7 MB
  • 02.Introduction to neural networks/0209.L2-norm loss.mp4 21.4 MB
  • 02.Introduction to neural networks/0210.Cross-entropy loss.mp4 33.4 MB
  • 02.Introduction to neural networks/0211.One parameter gradient descent.mp4 56.4 MB
  • 02.Introduction to neural networks/0212.N-parameter gradient descent.mp4 57.6 MB
  • 03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp4 6.9 MB
  • 03.Setting up the working environment/0302.Why Python and why Jupyter.mp4 34.7 MB
  • 03.Setting up the working environment/0303.Installing Anaconda.mp4 31.3 MB
  • 03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp4 9.2 MB
  • 03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp4 20.4 MB
  • 03.Setting up the working environment/0306.Installing TensorFlow 2.mp4 51.2 MB
  • 04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp4 36.4 MB
  • 04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp4 23.7 MB
  • 04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp4 20.4 MB
  • 04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp4 30.4 MB
  • 05.TensorFlow - An introduction/0501.TensorFlow outline.mp4 42 MB
  • 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4 37.8 MB
  • 05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp4 8.1 MB
  • 05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp4 13.3 MB
  • 05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 32.9 MB
  • 05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp4 31.4 MB
  • 05.TensorFlow - An introduction/0507.Customizing your model.mp4 21.6 MB
  • 06.Going deeper Introduction to deep neural networks/0601.Layers.mp4 20.5 MB
  • 06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp4 32.6 MB
  • 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4 58.2 MB
  • 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4 38 MB
  • 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4 38 MB
  • 06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp4 25 MB
  • 06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp4 52.7 MB
  • 06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp4 24.4 MB
  • 07.Overfitting/0701.Underfitting and overfitting.mp4 34.1 MB
  • 07.Overfitting/0702.Underfitting and overfitting - classification.mp4 32.5 MB
  • 07.Overfitting/0703.Training and validation.mp4 37.5 MB
  • 07.Overfitting/0704.Training, validation, and test.mp4 31.3 MB
  • 07.Overfitting/0705.N-fold cross validation.mp4 25.6 MB
  • 07.Overfitting/0706.Early stopping.mp4 28.3 MB
  • 08.Initialization/0801.Initialization - Introduction.mp4 26.2 MB
  • 08.Initialization/0802.Types of simple initializations.mp4 12.3 MB
  • 08.Initialization/0803.Xavier initialization.mp4 19.1 MB
  • 09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp4 34.5 MB
  • 09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp4 14.3 MB
  • 09.Gradient descent and learning rates/0903.Momentum.mp4 19 MB
  • 09.Gradient descent and learning rates/0904.Learning rate schedules.mp4 37.1 MB
  • 09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp4 10.9 MB
  • 09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp4 29.8 MB
  • 09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp4 29.1 MB
  • 10.Preprocessing/1001.Preprocessing introduction.mp4 25.6 MB
  • 10.Preprocessing/1002.Basic preprocessing.mp4 11.1 MB
  • 10.Preprocessing/1003.Standardization.mp4 40.4 MB
  • 10.Preprocessing/1004.Dealing with categorical data.mp4 18.2 MB
  • 10.Preprocessing/1005.One-hot and binary encoding.mp4 32.3 MB
  • 11.The MNIST example/1101.The dataset.mp4 20.7 MB
  • 11.The MNIST example/1102.How to tackle the MNIST.mp4 33.3 MB
  • 11.The MNIST example/1103.Importing the relevant packages and load the data.mp4 15.8 MB
  • 11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp4 27.1 MB
  • 11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp4 36.6 MB
  • 11.The MNIST example/1106.Outline the model.mp4 27.4 MB
  • 11.The MNIST example/1107.Select the loss and the optimizer.mp4 12.7 MB
  • 11.The MNIST example/1108.Learning.mp4 20.4 MB
  • 11.The MNIST example/1109.Testing the model.mp4 15.3 MB
  • 12.Business case/1201.Exploring the dataset and identifying predictors.mp4 30.2 MB
  • 12.Business case/1202.Outlining the business case solution.mp4 9.5 MB
  • 12.Business case/1203.Balancing the dataset.mp4 13.7 MB
  • 12.Business case/1204.Preprocessing the data.mp4 44.5 MB
  • 12.Business case/1205.Load the preprocessed data.mp4 18.2 MB
  • 12.Business case/1206.Learning and interpreting the result.mp4 26.4 MB
  • 12.Business case/1207.Setting an early stopping mechanism.mp4 21.5 MB
  • 12.Business case/1208.Testing the model.mp4 9.6 MB
  • 13.Conclusion/1301.See how much you have learned.mp4 38.9 MB
  • 13.Conclusion/1302.What's further out there in the machine and deep learning world.mp4 17.5 MB
  • 13.Conclusion/1303.An overview of CNNs.mp4 18.6 MB
  • 13.Conclusion/1304.An overview of RNNs.mp4 27.4 MB
  • 13.Conclusion/1305.An overview of non-NN approaches.mp4 40.2 MB
  • Exercise Files/exercise_files.zip 1.4 MB

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