Udemy - Tensorflow 2.0: Deep Learning and Artificial Intelligence

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  • Type Tutorials
  • Language English
  • Total size 7.0 GB
  • Uploaded By Fcw007
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  • Last checked 2 days ago
  • Date uploaded 5 years ago
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Infohash : 833CA9039EC9CEFB8075B869555571A7A7BB46FF



Tensorflow 2.0: Deep Learning and Artificial Intelligence

Tensorflow is the worldโ€™s most popular library for deep learning, and itโ€™s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

Created byLazy Programmer Inc., Lazy Programmer Team
Last updated 2/2020
English
English [Auto-generated]

For More Courses Visit: https://freecourseworld.com

Files:

[FreeCourseWorld.Com] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence 1. Welcome
  • 1. Introduction.mp4 (39.2 MB)
  • 1. Introduction.srt (5.7 KB)
  • 2. Outline.mp4 (73.7 MB)
  • 2. Outline.srt (17.1 KB)
  • 3. Where to get the code.mp4 (30.5 MB)
  • 3. Where to get the code.srt (7.6 KB)
10. GANs (Generative Adversarial Networks)
  • 1. GAN Theory.mp4 (86.5 MB)
  • 1. GAN Theory.srt (20.7 KB)
  • 2. GAN Code.mp4 (78.2 MB)
  • 2. GAN Code.srt (14.9 KB)
11. Deep Reinforcement Learning (Theory)
  • 1. Deep Reinforcement Learning Section Introduction.mp4 (37.8 MB)
  • 1. Deep Reinforcement Learning Section Introduction.srt (8.6 KB)
  • 10. Epsilon-Greedy.mp4 (37.6 MB)
  • 10. Epsilon-Greedy.srt (7.4 KB)
  • 11. Q-Learning.mp4 (61.3 MB)
  • 11. Q-Learning.srt (17.9 KB)
  • 12. Deep Q-Learning DQN (pt 1).mp4 (55.7 MB)
  • 12. Deep Q-Learning DQN (pt 1).srt (16.4 KB)
  • 13. Deep Q-Learning DQN (pt 2).mp4 (49.2 MB)
  • 13. Deep Q-Learning DQN (pt 2).srt (13.2 KB)
  • 14. How to Learn Reinforcement Learning.mp4 (37.5 MB)
  • 14. How to Learn Reinforcement Learning.srt (7.6 KB)
  • 2. Elements of a Reinforcement Learning Problem.mp4 (97.8 MB)
  • 2. Elements of a Reinforcement Learning Problem.srt (26.2 KB)
  • 3. States, Actions, Rewards, Policies.mp4 (43.0 MB)
  • 3. States, Actions, Rewards, Policies.srt (11.3 KB)
  • 4. Markov Decision Processes (MDPs).mp4 (49.0 MB)
  • 4. Markov Decision Processes (MDPs).srt (12.7 KB)
  • 5. The Return.mp4 (20.9 MB)
  • 5. The Return.srt (6.3 KB)
  • 6. Value Functions and the Bellman Equation.mp4 (43.3 MB)
  • 6. Value Functions and the Bellman Equation.srt (12.5 KB)
  • 7. What does it mean to โ€œlearnโ€.mp4 (30.3 MB)
  • 7. What does it mean to โ€œlearnโ€.srt (8.9 KB)
  • 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 (39.0 MB)
  • 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt (12.7 KB)
  • 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 (52.5 MB)
  • 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt (14.9 KB)
12. Stock Trading Project with Deep Reinforcement Learning
  • 1. Reinforcement Learning Stock Trader Introduction.mp4 (29.7 MB)
  • 1. Reinforcement Learning Stock Trader Introduction.srt (6.8 KB)
  • 2. Data and Environment.mp4 (56.0 MB)
  • 2. Data and Environment.srt (15.7 KB)
  • 3. Replay Buffer.mp4 (24.1 MB)
  • 3. Replay Buffer.srt (6.9 KB)
  • 4. Program Design and Layout.mp4 (29.8 MB)
  • 4. Program Design and Layout.srt (8.6 KB)
  • 5. Code pt 1.mp4 (46.8 MB)
  • 5. Code pt 1.srt (7.2 KB)
  • 6. Code pt 2.mp4 (83.4 MB)
  • 6. Code pt 2.srt (11.8 KB)
  • 7. Code pt 3.mp4 (62.3 MB)
  • 7. Code pt 3.srt (7.8 KB)
  • 8. Code pt 4.mp4 (59.2 MB)
  • 8. Code pt 4.srt (8.2 KB)
  • 9. Reinforcement Learning Stock Trader Discussion.mp4 (18.2 MB)
  • 9. Reinforcement Learning Stock Trader Discussion.srt (4.4 KB)
13. Advanced Tensorflow Usage
  • 1. What is a Web Service (Tensorflow Serving pt 1).mp4 (31.6 MB)
  • 1. What is a Web Service (Tensorflow Serving pt 1).srt (7.7 KB)
  • 2. Tensorflow Serving pt 2.mp4 (124.5 MB)
  • 2. Tensorflow Serving pt 2.srt (20.4 KB)
  • 3. Tensorflow Lite (TFLite).mp4 (42.4 MB)
  • 3. Tensorflow Lite (TFLite).srt (11.0 KB)
  • 4. Why is Google the King of Distributed Computing.mp4 (50.8 MB)
  • 4. Why is Google the King of Distributed Computing.srt (11.3 KB)
  • 5. Training with Distributed Strategies.mp4 (50.1 MB)
  • 5. Training with Distributed Strategies.srt (8.5 KB)
  • 6. Using the TPU.html (1.8 KB)
14. Low-Level Tensorflow
  • 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 (42.5 MB)
  • 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt (12.2 KB)
  • 2. Constants and Basic Computation.mp4 (50.2 MB)
  • 2. Constants and Basic Computation.srt (9.6 KB)
  • 3. Variables and Gradient Tape.mp4 (70.6 MB)
  • 3. Variables and Gradient Tape.srt (13.6 KB)
  • 4. Build Your Own Custom Model.mp4 (70.2 MB)
  • 4. Build Your Own Custom Model.srt (13.3 KB)
15. In-Depth Loss Functions
  • 1. Mean Squared Error.mp4 (37.3 MB)
  • 1. Mean Squared Error.srt (11.2 KB)
  • 2. Binary Cross Entropy.mp4 (21.5 MB)
  • 2. Binary Cross Entropy.srt (7.3 KB)
  • 3. Categorical Cross Entropy.mp4 (35.4 MB)
  • 3. Categorical Cross Entropy.srt (9.6 KB)
16. In-Depth Gradient Descent
  • 1. Gradient Descent.mp4 (34.9 MB)
  • 1. Gradient Descent.srt (9.8 KB)
  • 2. Stochastic Gradient Descent.mp4 (25.0 MB)
  • 2. Stochastic Gradient Descent.srt (5.4 KB)
  • 3. Momentum.mp4 (39.4 MB)
  • 3. Momentum.srt (7.8 KB)
  • 4. Variable and Adaptive Learning Rates.mp4 (38.5 MB)
  • 4. Variable and Adaptive Learning Rates.srt (15.2 KB)
  • 5. Adam.mp4 (42.6 MB)
  • 5. Adam.srt (13.5 KB)
17. Extras
  • 1. Links to TF2.0 Notebooks.html (7.8 KB)
18. Setting up your Environment
  • 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (166.7 MB)
  • 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt (14.7 KB)
  • 2. Windows-Focused Environment Setup 2018.mp4 (194.0 MB)
  • 2. Windows-Focused Environment Setup 2018.srt (20.0 KB)
  • 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 (167.3 MB)
  • 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt (32.0 KB)
19. Appendix FAQ
  • 1. What is the Appendix.mp4 (18.0 MB)
  • 1. What is the Appendix.srt (3.7 KB)
  • 10. BONUS Where to get discount coupons and FREE deep learning material.mp4 (37.8 MB)
  • 10. BONUS Where to get discount coupons and FREE deep learning material.srt (7.9 KB)
  • 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (117.1 MB)
  • 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt (31.6 KB)
  • 3. How to Code Yourself (part 1).mp4 (82.1 MB)
  • 3. How to Code Yourself (part 1).srt (22.1 KB)
  • 4. How to Code Yourself (part 2).mp4 (56.4 MB)
  • 4. How to Code Yourself (part 2).srt (13.0 KB)
  • 5. Proof that using Jupyter Notebook is the same as not using it.mp4 (77.9 MB)
  • 5. Proof that using Jupyter Notebook is the same as not using it.srt (14.2 KB)
  • 6. How to Succeed in this Course (Long Version).mp4 (38.9 MB)
  • 6. How to Succeed in this Course (Long Version).srt (14.6 KB)
  • 7. Is Theano Dead.mp4 (44.4 MB)
  • 7. Is Theano Dead.srt (12.6 KB)
  • 8. What order should I take your courses in (part 1).mp4 (88.1 MB)
  • 8. What order should I take your courses in (part 1).srt (16.1 KB)
  • 9. What order should I take your courses in (part 2).mp4 (122.6 MB)
  • 9. What order should I take your courses in (part 2).srt (23.0 KB)
2. Google Colab
  • 1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 (65.2 MB)
  • 1. Intro to Google Colab, how to use a GPU or TPU for free.srt (14.1 KB)
  • 2. Tensorflow 2.0 in Google Colab.mp4 (51.1 MB)
  • 2. Tensorflow 2.0 in Google Colab.srt (9.5 KB)
  • 3. Uploading your own data to Google Colab.mp4 (89.1 MB)
  • 3. Uploading your own data to Google Colab.srt (12.0 KB)
  • 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 (43.8 MB)
  • 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt (11.5 KB)
3. Machine Learning and Neurons
  • 1. What is Machine Learning.mp4 (73.2 MB)
  • 1. What is Machine Learning.srt (18.4 KB)
  • 2. Code Preparation (Classification Theory).mp4 (68.5 MB)
  • 2. Code Preparation (Classification Theory).srt (20.3 KB)
  • 3. Classification Notebook.mp4 (66.3 MB)
  • 3. Classification Notebook.srt (9.4 KB)
  • 4. Code Preparation (Regression Theory).mp4 (31.3 MB)
  • 4. Code Preparation (Regression Theory).srt (9.1 KB)
  • 5. Regression Notebook.mp4 (71.7 MB)
  • 5. Regression Notebook.srt (12.1 KB)
  • 6. The Neuron.mp4 (49.4 MB)
  • 6. The Neuron.srt (12.5 KB)
  • 7. How does a model learn.mp4 (55.0 MB)
  • 7. How does a model learn.srt (14.0 KB)
  • 8. Making Predictions.mp4 (42.0 MB)
  • 8. Making Predictions.srt (8.0 KB)
  • 9. Saving and Loading a Model.mp4 (35.3 MB)
  • 9. Saving and Loading a Model.srt (4.9 KB)
4. Feedforward Artificial Neural Networks
  • 1. Artificial Neural Networks Section Introduction.mp4 (32.5 MB)
  • 1. Artificial Neural Networks Section Introduction.srt (7.9 KB)
  • 2. Forward Propagation.mp4 (49.3 MB)
  • 2. Forward Propagation.srt (12.2 KB)
  • 3. The Geometrical Picture.mp4 (56.5 MB)
  • 3. The Geometrical Picture.srt (11.5 KB)
  • 4. Activation Functions.mp4 (92.2 MB)
  • 4. Activation Functions.srt (22.6 KB)
  • 5. Multiclass Classification.mp4 (46.9 MB)
  • 5. Multiclass Classification.srt (11.0 KB)
  • 6. How to Represent Images.mp4 (80.9 MB)
  • 6. How to Represent Images.srt (15.6 KB)
  • 7. Code Preparation (ANN).mp4 (56.2 MB)
  • 7. Code Preparation (ANN).srt (16.3 KB)
  • 8. ANN for Image Classification.mp4 (58.4 MB)
  • 8. ANN for Image Classification.srt (9.9 KB)
  • 9. ANN for Regression.mp4 (84.0 MB)
  • 9. ANN for Regression.srt (12.8 KB)
5. Convolutional Neural Networks
  • 1. What is Convolution (part 1).mp4 (83.6 MB)
  • 1. What is Convolution (part 1).srt (20.1 KB)
  • 10. Batch Normalization.mp4 (23.5 MB)
  • 10. Batch Normalization.srt (6.5 KB)
  • 11. Improving CIFAR-10 Results.mp4 (86.4 MB)
  • 11. Improving CIFAR-10 Results.srt (13.2 KB)
  • 2. What is Convolution (part 2).mp4 (25.2 MB)
  • 2. What is Convolution (part 2).srt (7.2 KB)
  • 3. What is Convolution (part 3).mp4 (27.6 MB)
  • 3. What is Convolution (part 3).srt (8.0 KB)
  • 4. Convolution on Color Images.mp4 (77.0 MB)
  • 4. Convolution on Color Images.srt (20.6 KB)
  • 5. CNN Architecture.mp4 (90.9 MB)
  • 5. CNN Architecture.srt (27.9 KB)
  • 6. CNN Code Preparation.mp4 (86.3 MB)
  • 6. CNN Code Preparation.srt (19.6 KB)
  • 7. CNN for Fashion MNIST.mp4 (51.6 MB)
  • 7. CNN for Fashion MNIST.srt (8.0 KB)
  • 8. CNN for CIFAR-10.mp4 (34.8 MB)
  • 8. CNN for CIFAR-10.srt (5.4 KB)
  • 9. Data Augmentation.mp4 (39.2 MB)
  • 9. Data Augmentation.srt (11.2 KB)
6. Recurrent Neural Networks, Time Series, and Sequence Data
  • 1. Sequence Data.mp4 (103.2 MB)
  • 1. Sequence Data.srt (24.0 KB)
  • 10. GRU and LSTM (pt 2).mp4 (53.6 MB)
  • 10. GRU and LSTM (pt 2).srt (14.4 KB)
  • 11. A More Challenging Sequence.mp4 (77.7 MB)
  • 11. A More Challenging Sequence.srt (9.6 KB)
  • 12. Demo of the Long Distance Problem.mp4 (143.1 MB)
  • 12. Demo of the Long Distance Problem.srt (23.1 KB)
  • 13. RNN for Image Classification (Theory).mp4 (31.5 MB)
  • 13. RNN for Image Classification (Theory).srt (6.0 KB)
  • 14. RNN for Image Classification (Code).mp4 (27.4 MB)
  • 14. RNN for Image Classification (Code).srt (4.2 KB)
  • 15. Stock Return Predictions using LSTMs (pt 1).mp4 (80.0 MB)
  • 15. Stock Return Predictions using LSTMs (pt 1).srt (15.7 KB)
  • 16. Stock Return Predictions using LSTMs (pt 2).mp4 (38.2 MB)
  • 16. Stock Return Predictions using LSTMs (pt 2).srt (6.5 KB)
  • 17. Stock Return Predictions using LSTMs (pt 3).mp4 (76.7 MB)
  • 17. Stock Return Predictions using LSTMs (pt 3).srt (14.4 KB)
  • 2. Forecasting.mp4 (47.2 MB)
  • 2. Forecasting.srt (12.7 KB)
  • 3. Autoregressive Linear Model for Time Series Prediction.mp4 (87.7 MB)
  • 3. Autoregressive Linear Model for Time Series Prediction.srt (14.2 KB)
  • 4. Proof that the Linear Model Works.mp4 (18.3 MB)
  • 4. Proof that the Linear Model Works.srt (4.6 KB)
  • 5. Recurrent Neural Networks.mp4 (92.0 MB)
  • 5. Recurrent Neural Networks.srt (25.6 KB)
  • 6. RNN Code Preparation.mp4 (20.4 MB)
  • 6. RNN Code Preparation.srt (7.1 KB)
  • 7. RNN for Time Series Prediction.mp4 (87.2 MB)
  • 7. RNN for Time Series Prediction.srt (11.2 KB)
  • 8. Paying Attention to Shapes.mp4 (64.3 MB)
  • 8. Paying Attention to Shapes.srt (9.9 KB)
  • 9. GRU and LSTM (pt 1).mp4 (76.1 MB)
  • 9. GRU and LSTM (pt 1).srt (21.1 KB)
7. Natural Language Processing (NLP)
  • 1. Embeddings.mp4 (58.0 MB)
  • 1. Embeddings.srt (16.2 KB)
  • 2. Code Preparation (NLP).mp4 (62.9 MB)
  • 2. Code Preparation (NLP).srt (16.8 KB)
  • 3. Text Preprocessing.mp4 (36.1 MB)
  • 3. Text Preprocessing.srt (6.2 KB)
  • 4. Text Classification with LSTMs.mp4 (60.6 MB)
  • 4. Text Classification with LSTMs.srt (9.8 KB)
  • 5. CNNs for Text.mp4 (40.9 MB)
  • 5. CNNs for Text.srt (9.6 KB)
  • 6. Text Classification with CNNs.mp4 (46.4 MB)
  • 6. Text Classification with CNNs.srt (6.6 KB)
8. Recommender Systems
  • 1. Recommender Systems with Deep Learning Theory.mp4 (68.7 MB)
  • 1. Recommender Systems with Deep Learning Theory.srt (17.4 KB)
  • 2. Recommender Systems with Deep Learning Code.mp4 (58.8 MB)
  • 2. Recommender Systems with Deep Learning Code.srt (11.7 KB)
9. Transfer Learning for Computer Vision
  • 1. Transfer Learning Theory.mp4 (55.2 MB)
  • 1. Transfer Learning Theory.srt (10.7 KB)
  • 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 (31.5 MB)
  • 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt (7.3 KB)
  • 3. Large Datasets and Data Generators.mp4 (36.6 MB)
  • 3. Large Datasets and Data Generators.srt (8.8 KB)
  • 4. 2 Approaches to Transfer Learning.mp4 (20.6 MB)
  • 4. 2 Approaches to Transfer Learning.srt (6.0 KB)
  • 5. Transfer Learning Code (pt 1).mp4 (66.6 MB)
  • 5. Transfer Learning Code (pt 1).srt (13.8 KB)
  • 6. Transfer Learning Code (pt 2).mp4 (46.1 MB)
  • 6. Transfer Learning Code (pt 2).srt (10.4 KB)
  • [FreeCourseWorld.Com].url (0.1 KB)

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