Tensorflow Deep Learning and Artificial Intelligence | FE075B3B187DC4DBB4D8409428F3D03E7F970BC6

Tensorflow Deep Learning and Artificial Intelligence

Name	Tensorflow 2.0 Deep Learning and Artificial Intelligence	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	7.4 GB
Total Files	403
Last Seen	2025-05-21 23:41
Hash	FE075B3B187DC4DBB4D8409428F3D03E7F970BC6

/.../18. Setting up your Environment (FAQ by Student Request)/
2. Anaconda Environment Setup.mp4	189.7 MB
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt	32.8 KB
3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4	175.4 MB
2. Anaconda Environment Setup.srt	20.4 KB
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt	15.0 KB
1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	157.9 MB
/
TutsNode.com.txt	0.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt	0.6 KB
/.../20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt	32.4 KB
4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt	23.6 KB
3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt	16.5 KB
1. How to Succeed in this Course (Long Version).srt	15.0 KB
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4	113.4 MB
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	110.7 MB
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4	83.6 MB
1. How to Succeed in this Course (Long Version).mp4	36.9 MB
/.../5. Convolutional Neural Networks/
5. CNN Architecture.srt	28.6 KB
4. Convolution on Color Images.srt	21.0 KB
1. What is Convolution (part 1).srt	20.6 KB
6. CNN Code Preparation.srt	20.1 KB
11. Improving CIFAR-10 Results.srt	13.5 KB
9. Data Augmentation.srt	11.5 KB
3. What is Convolution (part 3).srt	8.2 KB
7. CNN for Fashion MNIST.srt	8.2 KB
2. What is Convolution (part 2).srt	7.4 KB
10. Batch Normalization.srt	6.7 KB
8. CNN for CIFAR-10.srt	5.5 KB
5. CNN Architecture.mp4	84.5 MB
1. What is Convolution (part 1).mp4	83.6 MB
6. CNN Code Preparation.mp4	80.6 MB
11. Improving CIFAR-10 Results.mp4	76.5 MB
4. Convolution on Color Images.mp4	72.8 MB
7. CNN for Fashion MNIST.mp4	44.9 MB
9. Data Augmentation.mp4	36.6 MB
8. CNN for CIFAR-10.mp4	31.1 MB
3. What is Convolution (part 3).mp4	29.0 MB
2. What is Convolution (part 2).mp4	23.3 MB
10. Batch Normalization.mp4	22.1 MB
/.../3. Machine Learning and Neurons/
10. Why Keras.srt	5.9 KB
2. Code Preparation (Classification Theory).srt	20.7 KB
1. What is Machine Learning.srt	18.9 KB
7. How does a model learn.srt	14.3 KB
6. The Neuron.srt	12.8 KB
5. Regression Notebook.srt	12.4 KB
9. Saving and Loading a Model.srt	5.0 KB
3. Classification Notebook.srt	9.6 KB
4. Code Preparation (Regression Theory).srt	9.3 KB
8. Making Predictions.srt	8.2 KB
11. Suggestion Box.srt	4.9 KB
1. What is Machine Learning.mp4	68.7 MB
2. Code Preparation (Classification Theory).mp4	62.7 MB
5. Regression Notebook.mp4	60.3 MB
3. Classification Notebook.mp4	57.2 MB
7. How does a model learn.mp4	50.3 MB
6. The Neuron.mp4	44.6 MB
8. Making Predictions.mp4	35.5 MB
9. Saving and Loading a Model.mp4	31.2 MB
4. Code Preparation (Regression Theory).mp4	28.6 MB
11. Suggestion Box.mp4	28.4 MB
10. Why Keras.mp4	27.8 MB
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/1. Welcome/
3.1 Colab Notebooks.html	0.2 KB
3.2 Github Link.html	0.1 KB
2. Outline.srt	17.5 KB
3. Where to get the code.srt	15.7 KB
1. Introduction.srt	5.8 KB
2. Outline.mp4	77.3 MB
3. Where to get the code.mp4	66.0 MB
1. Introduction.mp4	36.5 MB
/.../11. Deep Reinforcement Learning (Theory)/
2. Elements of a Reinforcement Learning Problem.srt	26.8 KB
11. Q-Learning.srt	18.3 KB
12. Deep Q-Learning DQN (pt 1).srt	16.8 KB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt	15.2 KB
13. Deep Q-Learning DQN (pt 2).srt	13.5 KB
4. Markov Decision Processes (MDPs).srt	13.0 KB
6. Value Functions and the Bellman Equation.srt	12.8 KB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt	12.7 KB
3. States, Actions, Rewards, Policies.srt	11.6 KB
7. What does it mean to “learn”.srt	9.1 KB
1. Deep Reinforcement Learning Section Introduction.srt	8.8 KB
14. How to Learn Reinforcement Learning.srt	7.8 KB
10. Epsilon-Greedy.srt	7.7 KB
5. The Return.srt	6.4 KB
2. Elements of a Reinforcement Learning Problem.mp4	103.4 MB
11. Q-Learning.mp4	64.8 MB
12. Deep Q-Learning DQN (pt 1).mp4	59.0 MB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4	55.5 MB
13. Deep Q-Learning DQN (pt 2).mp4	52.0 MB
4. Markov Decision Processes (MDPs).mp4	51.7 MB
6. Value Functions and the Bellman Equation.mp4	45.7 MB
3. States, Actions, Rewards, Policies.mp4	45.4 MB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4	44.8 MB
10. Epsilon-Greedy.mp4	42.1 MB
1. Deep Reinforcement Learning Section Introduction.mp4	39.9 MB
14. How to Learn Reinforcement Learning.mp4	39.5 MB
7. What does it mean to “learn”.mp4	33.3 MB
5. The Return.mp4	22.2 MB
/.../6. Recurrent Neural Networks, Time Series, and Sequence Data/
5. Recurrent Neural Networks.srt	26.2 KB
1. Sequence Data.srt	24.6 KB
12. Demo of the Long Distance Problem.srt	23.6 KB
9. GRU and LSTM (pt 1).srt	23.3 KB
15. Stock Return Predictions using LSTMs (pt 1).srt	16.1 KB
17. Stock Return Predictions using LSTMs (pt 3).srt	14.8 KB
10. GRU and LSTM (pt 2).srt	14.6 KB
3. Autoregressive Linear Model for Time Series Prediction.srt	14.6 KB
2. Forecasting.srt	13.7 KB
7. RNN for Time Series Prediction.srt	11.5 KB
12. Demo of the Long Distance Problem.mp4	130.1 MB
8. Paying Attention to Shapes.srt	10.1 KB
11. A More Challenging Sequence.srt	9.8 KB
18. Other Ways to Forecast.srt	7.4 KB
6. RNN Code Preparation.srt	7.3 KB
16. Stock Return Predictions using LSTMs (pt 2).srt	6.7 KB
13. RNN for Image Classification (Theory).srt	6.1 KB
4. Proof that the Linear Model Works.srt	4.7 KB
14. RNN for Image Classification (Code).srt	4.3 KB
1. Sequence Data.mp4	94.5 MB
5. Recurrent Neural Networks.mp4	87.0 MB
9. GRU and LSTM (pt 1).mp4	83.7 MB
7. RNN for Time Series Prediction.mp4	77.7 MB
3. Autoregressive Linear Model for Time Series Prediction.mp4	75.2 MB
17. Stock Return Predictions using LSTMs (pt 3).mp4	70.6 MB
15. Stock Return Predictions using LSTMs (pt 1).mp4	70.4 MB
11. A More Challenging Sequence.mp4	67.8 MB
8. Paying Attention to Shapes.mp4	55.0 MB
10. GRU and LSTM (pt 2).mp4	52.8 MB
2. Forecasting.mp4	49.0 MB
16. Stock Return Predictions using LSTMs (pt 2).mp4	34.6 MB
13. RNN for Image Classification (Theory).mp4	30.5 MB
18. Other Ways to Forecast.mp4	29.7 MB
14. RNN for Image Classification (Code).mp4	24.4 MB
6. RNN Code Preparation.mp4	19.3 MB
4. Proof that the Linear Model Works.mp4	17.0 MB
/.../4. Feedforward Artificial Neural Networks/
5. Activation Functions.srt	23.2 KB
2. Beginners Rejoice The Math in This Course is Optional.srt	17.4 KB
8. Code Preparation (ANN).srt	16.7 KB
7. How to Represent Images.srt	16.0 KB
10. ANN for Regression.srt	13.1 KB
3. Forward Propagation.srt	12.5 KB
4. The Geometrical Picture.srt	11.8 KB
6. Multiclass Classification.srt	11.2 KB
9. ANN for Image Classification.srt	10.2 KB
1. Artificial Neural Networks Section Introduction.srt	8.1 KB
5. Activation Functions.mp4	84.4 MB
7. How to Represent Images.mp4	73.9 MB
10. ANN for Regression.mp4	72.6 MB
2. Beginners Rejoice The Math in This Course is Optional.mp4	71.8 MB
4. The Geometrical Picture.mp4	59.2 MB
8. Code Preparation (ANN).mp4	53.4 MB
9. ANN for Image Classification.mp4	50.0 MB
3. Forward Propagation.mp4	49.0 MB
6. Multiclass Classification.mp4	43.4 MB
1. Artificial Neural Networks Section Introduction.mp4	31.3 MB
/.../19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/
2. How to Code Yourself (part 1).srt	22.7 KB
1. Beginner's Coding Tips.srt	19.5 KB
4. Proof that using Jupyter Notebook is the same as not using it.srt	14.6 KB
3. How to Code Yourself (part 2).srt	13.3 KB
5. Is Theano Dead.srt	12.9 KB
1. Beginner's Coding Tips.mp4	79.4 MB
2. How to Code Yourself (part 1).mp4	75.3 MB
4. Proof that using Jupyter Notebook is the same as not using it.mp4	72.8 MB
3. How to Code Yourself (part 2).mp4	51.5 MB
5. Is Theano Dead.mp4	42.7 MB
/.../10. GANs (Generative Adversarial Networks)/
1. GAN Theory.srt	21.2 KB
2. GAN Code.srt	15.2 KB
1. GAN Theory.mp4	91.4 MB
2. GAN Code.mp4	82.1 MB
/.../13. Advanced Tensorflow Usage/
2. Tensorflow Serving pt 2.srt	20.9 KB
4. Why is Google the King of Distributed Computing.srt	11.5 KB
3. Tensorflow Lite (TFLite).srt	11.3 KB
5. Training with Distributed Strategies.srt	8.7 KB
1. What is a Web Service (Tensorflow Serving pt 1).srt	7.9 KB
6. Using the TPU.srt	7.1 KB
2. Tensorflow Serving pt 2.mp4	110.1 MB
6. Using the TPU.mp4	47.4 MB
4. Why is Google the King of Distributed Computing.mp4	47.1 MB
5. Training with Distributed Strategies.mp4	45.7 MB
3. Tensorflow Lite (TFLite).mp4	44.7 MB
1. What is a Web Service (Tensorflow Serving pt 1).mp4	29.1 MB
/8. Recommender Systems/
1. Recommender Systems with Deep Learning Theory.srt	17.8 KB
2. Recommender Systems with Deep Learning Code.srt	12.0 KB
1. Recommender Systems with Deep Learning Theory.mp4	72.0 MB
2. Recommender Systems with Deep Learning Code.mp4	61.7 MB
/.../7. Natural Language Processing (NLP)/
2. Code Preparation (NLP).srt	17.2 KB
1. Embeddings.srt	16.6 KB
5. CNNs for Text.srt	10.3 KB
4. Text Classification with LSTMs.srt	10.0 KB
6. Text Classification with CNNs.srt	6.8 KB
3. Text Preprocessing.srt	6.3 KB
2. Code Preparation (NLP).mp4	59.8 MB
1. Embeddings.mp4	55.1 MB
4. Text Classification with LSTMs.mp4	53.1 MB
5. CNNs for Text.mp4	42.4 MB
6. Text Classification with CNNs.mp4	41.5 MB
3. Text Preprocessing.mp4	30.2 MB
/.../16. In-Depth Gradient Descent/
5. Adam (pt 1).srt	17.1 KB
4. Variable and Adaptive Learning Rates.srt	15.5 KB
6. Adam (pt 2).srt	14.8 KB
1. Gradient Descent.srt	10.0 KB
3. Momentum.srt	8.0 KB
2. Stochastic Gradient Descent.srt	5.5 KB
5. Adam (pt 1).mp4	57.8 MB
6. Adam (pt 2).mp4	55.3 MB
1. Gradient Descent.mp4	36.6 MB
4. Variable and Adaptive Learning Rates.mp4	36.5 MB
3. Momentum.mp4	35.9 MB
2. Stochastic Gradient Descent.mp4	24.1 MB
/.../12. Stock Trading Project with Deep Reinforcement Learning/
2. Data and Environment.srt	16.1 KB
6. Code pt 2.srt	12.0 KB
10. Help! Why is the code slower on my machine.srt	12.0 KB
4. Program Design and Layout.srt	8.8 KB
8. Code pt 4.srt	8.6 KB
7. Code pt 3.srt	7.9 KB
5. Code pt 1.srt	7.4 KB
3. Replay Buffer.srt	7.1 KB
1. Reinforcement Learning Stock Trader Introduction.srt	7.0 KB
9. Reinforcement Learning Stock Trader Discussion.srt	4.5 KB
6. Code pt 2.mp4	71.3 MB
8. Code pt 4.mp4	55.1 MB
7. Code pt 3.mp4	54.6 MB
2. Data and Environment.mp4	53.4 MB
10. Help! Why is the code slower on my machine.mp4	44.5 MB
5. Code pt 1.mp4	41.5 MB
1. Reinforcement Learning Stock Trader Introduction.mp4	27.3 MB
4. Program Design and Layout.mp4	27.2 MB
3. Replay Buffer.mp4	25.2 MB
9. Reinforcement Learning Stock Trader Discussion.mp4	17.4 MB
/.../21. Appendix FAQ Finale/
1. What is the Appendix.srt	3.8 KB
2. BONUS Lecture.srt	8.1 KB
2. BONUS Lecture.mp4	39.6 MB
1. What is the Appendix.mp4	17.2 MB
/2. Google Colab/
1. Intro to Google Colab, how to use a GPU or TPU for free.srt	14.5 KB
3. Uploading your own data to Google Colab.srt	12.3 KB
4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt	11.8 KB
2. Tensorflow 2.0 in Google Colab.srt	9.7 KB
5. How to Succeed in this Course.srt	8.5 KB
3. Uploading your own data to Google Colab.mp4	77.2 MB
1. Intro to Google Colab, how to use a GPU or TPU for free.mp4	56.5 MB
5. How to Succeed in this Course.mp4	45.9 MB
2. Tensorflow 2.0 in Google Colab.mp4	42.6 MB
4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4	40.8 MB
/.../9. Transfer Learning for Computer Vision/
5. Transfer Learning Code (pt 1).srt	14.1 KB
1. Transfer Learning Theory.srt	10.9 KB
6. Transfer Learning Code (pt 2).srt	10.7 KB
3. Large Datasets and Data Generators.srt	9.0 KB
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt	7.5 KB
4. 2 Approaches to Transfer Learning.srt	6.1 KB
5. Transfer Learning Code (pt 1).mp4	69.8 MB
1. Transfer Learning Theory.mp4	57.8 MB
6. Transfer Learning Code (pt 2).mp4	48.3 MB
3. Large Datasets and Data Generators.mp4	38.3 MB
2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4	33.1 MB
4. 2 Approaches to Transfer Learning.mp4	21.6 MB
/.../14. Low-Level Tensorflow/
3. Variables and Gradient Tape.srt	13.9 KB
4. Build Your Own Custom Model.srt	13.6 KB
1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt	12.5 KB
2. Constants and Basic Computation.srt	9.9 KB
4. Build Your Own Custom Model.mp4	61.4 MB
3. Variables and Gradient Tape.mp4	58.8 MB
2. Constants and Basic Computation.mp4	42.3 MB
1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4	40.6 MB
/.../15. In-Depth Loss Functions/
1. Mean Squared Error.srt	11.5 KB
3. Categorical Cross Entropy.srt	9.9 KB
2. Binary Cross Entropy.srt	7.4 KB
1. Mean Squared Error.mp4	35.4 MB
3. Categorical Cross Entropy.mp4	33.2 MB
2. Binary Cross Entropy.mp4	24.8 MB
/17. Extras/
1. How to Choose Hyperparameters.srt	8.9 KB
3. Links to TF2.0 Notebooks.html	8.3 KB
2. Where Are The Exercises.srt	5.5 KB
1. How to Choose Hyperparameters.mp4	39.8 MB
2. Where Are The Exercises.mp4	27.2 MB
Total files 403