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[FreeCoursesOnline.Me] PacktPub - Master Deep Learning With TensorFlow 2.0 In Python [2019] [Video]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	8ef76cbab81900d4d663641cb4f159b74fbcb062
Size:	2.3 GB in 84 files
Release Date(s):	Torrent Release: 24-09-2019 by user
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DESCRIPTION

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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.

VISITOR COMMENTS (0

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FILE LIST

Filename		Size
	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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