Deep Learning with Python, 2nd Edition, Video Edition

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Deep Learning with Python, Second Edition, Video Edition [TutsNode.com] - Deep Learning with Python, Second Edition, Video Edition
  • 03-Chapter 1 Understanding how deep learning works, in three figures.mp4 (102.9 MB)
  • 49-Chapter 9 Advanced deep learning for computer vision.mp4 (99.8 MB)
  • 33-Chapter 6 Collect a dataset.mp4 (85.1 MB)
  • 96-Chapter 14 Lifelong learning and modular subroutine reuse.mp4 (84.1 MB)
  • 02-Chapter 1 Learning rules and representations from data.mp4 (83.4 MB)
  • 04-Chapter 1 Before deep learning - A brief history of machine learning.mp4 (82.7 MB)
  • 74-Chapter 12 How do you generate sequence data.mp4 (81.9 MB)
  • 86-Chapter 14 Conclusions.mp4 (81.2 MB)
  • 77-Chapter 12 Neural style transfer.mp4 (80.9 MB)
  • 73-Chapter 12 Generative deep learning.mp4 (80.6 MB)
  • 27-Chapter 5 The nature of generalization in deep learning.mp4 (80.5 MB)
  • 66-Chapter 11 Two approaches for representing groups of words - Sets and sequences.mp4 (79.8 MB)
  • 71-Chapter 11 Beyond text classification - Sequence-to-sequence learning.mp4 (79.6 MB)
  • 36-Chapter 6 Deploy the model.mp4 (78.2 MB)
  • 05-Chapter 1 Back to neural networks.mp4 (77.9 MB)
  • 01-Chapter 1 What is deep learning.mp4 (76.8 MB)
  • 79-Chapter 12 Implementing a VAE with Keras.mp4 (75.9 MB)
  • 82-Chapter 13 Hyperparameter optimization.mp4 (75.1 MB)
  • 44-Chapter 8 The convolution operation.mp4 (74.4 MB)
  • 55-Chapter 9 Visualizing heatmaps of class activation.mp4 (74.3 MB)
  • 70-Chapter 11 The Transformer encoder.mp4 (72.5 MB)
  • 28-Chapter 5 Evaluating machine learning models.mp4 (72.5 MB)
  • 69-Chapter 11 The Transformer architecture.mp4 (71.7 MB)
  • 67-Chapter 11 Processing words as a sequence - The sequence model approach, Part 1.mp4 (70.5 MB)
  • 38-Chapter 7 Working with Keras - A deep dive.mp4 (69.9 MB)
  • 92-Chapter 14 Setting the course toward greater generality in AI.mp4 (69.5 MB)
  • 14-Chapter 2 Derivative of a tensor operation - The gradient.mp4 (69.5 MB)
  • 30-Chapter 5 Improving generalization.mp4 (69.4 MB)
  • 19-Chapter 3 First steps with TensorFlow.mp4 (69.2 MB)
  • 21-Chapter 3 The “compile” step - Configuring the learning process.mp4 (68.0 MB)
  • 52-Chapter 9 Depthwise separable convolutions.mp4 (67.9 MB)
  • 17-Chapter 3 Introduction to Keras and TensorFlow.mp4 (67.6 MB)
  • 45-Chapter 8 Training a convnet from scratch on a small dataset.mp4 (67.1 MB)
  • 32-Chapter 6 The universal workflow of machine learning.mp4 (66.9 MB)
  • 93-Chapter 14 Implementing intelligence - The missing ingredients.mp4 (66.0 MB)
  • 47-Chapter 8 Leveraging a pretrained model.mp4 (65.1 MB)
  • 62-Chapter 10 Using bidirectional RNNs.mp4 (64.9 MB)
  • 48-Chapter 8 Feature extraction with a pretrained model.mp4 (64.3 MB)
  • 23-Chapter 4 Building your model.mp4 (64.2 MB)
  • 87-Chapter 14 Key enabling technologies.mp4 (63.6 MB)
  • 81-Chapter 13 Best practices for the real world.mp4 (63.0 MB)
  • 80-Chapter 12 A bag of tricks.mp4 (62.5 MB)
  • 07-Chapter 1 Algorithms.mp4 (62.4 MB)
  • 25-Chapter 4 Predicting house prices - A regression example.mp4 (61.8 MB)
  • 46-Chapter 8 Data preprocessing.mp4 (61.7 MB)
  • 31-Chapter 5 Regularizing your model.mp4 (60.8 MB)
  • 89-Chapter 14 The limitations of deep learning.mp4 (60.3 MB)
  • 40-Chapter 7 Using built-in training and evaluation loops.mp4 (60.2 MB)
  • 88-Chapter 14 Key network architectures.mp4 (59.9 MB)
  • 61-Chapter 10 Advanced use of recurrent neural networks.mp4 (59.8 MB)
  • 95-Chapter 14 Blending together deep learning and program synthesis.mp4 (58.9 MB)
  • 50-Chapter 9 Modern convnet architecture patterns.mp4 (58.7 MB)
  • 53-Chapter 9 Interpreting what convnets learn.mp4 (58.5 MB)
  • 75-Chapter 12 A text-generation callback with variable-temperature sampling.mp4 (58.5 MB)
  • 63-Chapter 11 Deep learning for text.mp4 (57.9 MB)
  • 76-Chapter 12 DeepDream.mp4 (57.4 MB)
  • 51-Chapter 9 Residual connections.mp4 (57.3 MB)
  • 26-Chapter 5 Fundamentals of machine learning.mp4 (57.0 MB)
  • 24-Chapter 4 Classifying newswires - A multiclass classification example.mp4 (56.8 MB)
  • 06-Chapter 1 Why deep learning Why now.mp4 (56.5 MB)
  • 72-Chapter 11 Sequence-to-sequence learning with Transformer.mp4 (56.1 MB)
  • 20-Chapter 3 Anatomy of a neural network - Understanding core Keras APIs.mp4 (56.1 MB)
  • 78-Chapter 12 Generating images with variational autoencoders.mp4 (55.8 MB)
  • 15-Chapter 2 Chaining derivatives - The Backpropagation algorithm.mp4 (55.0 MB)
  • 56-Chapter 10 Deep learning for timeseries.mp4 (53.9 MB)
  • 16-Chapter 2 Looking back at our first example.mp4 (53.7 MB)
  • 83-Chapter 13 Scaling-up model training.mp4 (53.6 MB)
  • 91-Chapter 14 The purpose of intelligence.mp4 (53.5 MB)
  • 22-Chapter 4 Getting started with neural networks - Classification and regression.mp4 (53.4 MB)
  • 68-Chapter 11 Processing words as a sequence - The sequence model approach, Part 2.mp4 (52.5 MB)
  • 08-Chapter 2 The mathematical building blocks of neural networks.mp4 (52.0 MB)
  • 65-Chapter 11 Vocabulary indexing.mp4 (50.4 MB)
  • 13-Chapter 2 The engine of neural networks - Gradient-based optimization.mp4 (50.0 MB)
  • 10-Chapter 2 Real-world examples of data tensors.mp4 (48.6 MB)
  • 94-Chapter 14 The missing half of the picture.mp4 (47.9 MB)
  • 09-Chapter 2 Data representations for neural networks.mp4 (47.6 MB)
  • 57-Chapter 10 Preparing the data.mp4 (47.0 MB)
  • 11-Chapter 2 The gears of neural networks - Tensor operations.mp4 (46.5 MB)
  • 64-Chapter 11 Preparing text data.mp4 (46.5 MB)
  • 41-Chapter 7 Writing your own training and evaluation loops.mp4 (45.8 MB)
  • 90-Chapter 14 Local generalization vs. extreme generalization.mp4 (45.3 MB)
  • 58-Chapter 10 Let’s try a basic machine learning model.mp4 (45.0 MB)
  • 34-Chapter 6 Develop a model.mp4 (44.4 MB)
  • 18-Chapter 3 Setting up a deep learning workspace.mp4 (43.5 MB)
  • 60-Chapter 10 A recurrent layer in Keras.mp4 (41.7 MB)
  • 85-Chapter 13 TPU training.mp4 (41.6 MB)
  • 35-Chapter 6 Beat a baseline.mp4 (41.2 MB)
  • 43-Chapter 8 Introduction to deep learning for computer vision.mp4 (40.7 MB)
  • 29-Chapter 5 Improving model fit.mp4 (40.5 MB)
  • 59-Chapter 10 Understanding recurrent neural networks.mp4 (40.5 MB)
  • 54-Chapter 9 Visualizing convnet filters.mp4 (40.4 MB)
  • 84-Chapter 13 Multi-GPU training.mp4 (37.5 MB)
  • 12-Chapter 2 Tensor reshaping.mp4 (36.7 MB)
  • 42

Description


Description

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.
In Deep Learning with Python, Second Edition you will learn:

Deep learning from first principles
Image classification and image segmentation
Timeseries forecasting
Text classification and machine translation
Text generation, neural style transfer, and image generation
Printed in full color throughout

Published 10/2021



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Deep Learning with Python, 2nd Edition, Video Edition

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Deep Learning with Python, 2nd Edition, Video Edition

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