Download Packt | Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO] torrent

Packt | Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO]

Torrent:	Packt \| Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO]
Description:	For More Udemy Free Courses >>> https://freetutorials.us/ For more Lynda and other Courses >>> https://www.freecoursesonline.me/ Forum for discussion >>> https://1hack.us/ 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.
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Show Files »	[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video] 0. Websites you may like 0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url (0.4 KB) 1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url (0.3 KB) 2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url (0.3 KB) 3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url (0.2 KB) 4. (FTUApps.com) Download Cracked Developers Applications For Free.url (0.2 KB) How you can help Team-FTU.txt (0.2 KB) 01.Welcome! Course introduction 0101.Meet your instructors and why you should study machine learning.mp4 (84.7 MB) 0102.What does the course cover.mp4 (39.1 MB) 02.Introduction to neural networks 0201.Introduction to neural networks.mp4 (45.7 MB) 0202.Training the model.mp4 (26.8 MB) 0203.Types of machine learning.mp4 (40.8 MB) 0204.The linear model.mp4 (26.0 MB) 0205.The linear model. Multiple inputs.mp4 (23.7 MB) 0206.The linear model. Multiple inputs and multiple outputs.mp4 (42.2 MB) 0207.Graphical representation.mp4 (22.0 MB) 0208.The objective function.mp4 (17.7 MB) 0209.L2-norm loss.mp4 (21.4 MB) 0210.Cross-entropy loss.mp4 (33.4 MB) 0211.One parameter gradient descent.mp4 (56.4 MB) 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) 0302.Why Python and why Jupyter.mp4 (34.7 MB) 0303.Installing Anaconda.mp4 (31.3 MB) 0304.The Jupyter dashboard - part 1.mp4 (9.2 MB) 0305.The Jupyter dashboard - part 2.mp4 (20.4 MB) 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) 0402.Minimal example - part 2.mp4 (23.7 MB) 0403.Minimal example - part 3.mp4 (20.4 MB) 0404.Minimal example - part 4.mp4 (30.4 MB) 05.TensorFlow - An introduction 0501.TensorFlow outline.mp4 (42.0 MB) 0502.TensorFlow 2 intro.mp4 (37.8 MB) 0503.A Note on Coding in TensorFlow.mp4 (8.1 MB) 0504.Types of file formats in TensorFlow and data handling.mp4 (13.3 MB) 0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 (32.9 MB) 0506.Interpreting the result and extracting the weights and bias.mp4 (31.4 MB) 0507.Customizing your model.mp4 (21.6 MB) 06.Going deeper Introduction to deep neural networks 0601.Layers.mp4 (20.5 MB) 0602.What is a deep net.mp4 (32.6 MB) 0603.Understanding deep nets in depth.mp4 (58.2 MB) 0604.Why do we need non-linearities.mp4 (38.0 MB) 0605.Activation functions.mp4 (38.0 MB) 0606.Softmax activation.mp4 (25.0 MB) 0607.Backpropagation.mp4 (52.7 MB) 0608.Backpropagation - visual representation.mp4 (24.4 MB) 07.Overfitting 0701.Underfitting and overfitting.mp4 (34.1 MB) 0702.Underfitting and overfitting - classification.mp4 (32.5 MB) 0703.Training and validation.mp4 (37.5 MB) 0704.Training, validation, and test.mp4 (31.3 MB) 0705.N-fold cross validation.mp4 (25.6 MB) 0706.Early stopping.mp4 (28.3 MB) 08.Initialization 0801.Initialization - Introduction.mp4 (26.2 MB) 0802.Types of simple initializations.mp4 (12.3 MB) 0803.Xavier initialization.mp4 (19.1 MB) 09.Gradient descent and learning rates 0901.Stochastic gradient descent.mp4 (34.5 MB) 0902.Gradient descent pitfalls.mp4 (14.3 MB) 0903.Momentum.mp4 (19.0 MB) 0904.Learning rate schedules.mp4 (37.1 MB) 0905.Learning rate schedules. A picture.mp4 (10.9 MB) 0906.Adaptive learning rate schedules.mp4 (29.8 MB) 0907.Adaptive moment estimation.mp4 (29.1 MB) 10.Preprocessing 1001.Preprocessing introduction.mp4 (25.6 MB) 1002.Basic preprocessing.mp4 (11.1 MB) 1003.Standardization.mp4 (40.4 MB) 1004.Dealing with categorical data.mp4 (18.2 MB) 1005.One-hot and binary encoding.mp4 (32.3 MB) 11.The MNIST example 1101.The dataset.mp4 (20.7 MB) 1102.How to tackle the MNIST.mp4 (33.3 MB) 1103.Importing the relevant packages and load the data.mp4 (15.8 MB) 1104.Preprocess the data - create a validation dataset and scale the data.mp4 (27.1 MB) 1105.Preprocess the data - shuffle and batch the data.mp4 (36.6 MB) 1106.Outline the model.mp4 (27.4 MB) 1107.Select the loss and the optimizer.mp4 (12.7 MB) 1108.Learning.mp4 (20.4 MB) 1109.Testing the model.mp4 (15.3 MB) 12.Business case 1201.Exploring the dataset and identifying predictors.mp4 (30.2 MB) 1202.Outlining the business case solution.mp4 (9.5 MB) 1203.Balancing the dataset.mp4 (13.7 MB) 1204.Preprocessing the data.mp4 (44.5 MB) 1205.Load the preprocessed data.mp4 (18.2 MB) 1206.Learning and interpreting the result.mp4 (26.4 MB) 1207.Setting an early stopping mechanism.mp4 (21.5 MB) 1208.Testing the model.mp4 (9.6 MB) 13.Conclusion 1301.See how much you have learned.mp4 (38.9 MB) 1302.What's further out there in the machine and deep learning world.mp4 (17.5 MB) 1303.An overview of CNNs.mp4 (18.6 MB) 1304.An overview of RNNs.mp4 (27.4 MB) 1305.An overview of non-NN approaches.mp4 (40.2 MB) Exercise Files exercise_files.zip (1.4 MB) files
Added:	2019-09-16 11:04:41
Language:	English
Peers:	Seeders : 26 , Leechers : 9
Release name:	Packt \| Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO]
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