7.84 GB | 20min 20s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 -Welcome.mp4 (35.67 MB)
2 -Overview and Outline.mp4 (79.64 MB)
1 -Where to get the code, notebooks, and data.mp4 (26.91 MB)
2 -How to Succeed in This Course.mp4 (16.23 MB)
3 -Temporary 403 Errors.mp4 (21.98 MB)
1 -Intro to Google Colab, how to use a GPU or TPU for free.mp4 (60.44 MB)
2 -Uploading your own data to Google Colab.mp4 (90.51 MB)
3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 (56.99 MB)
1 -What is Machine Learning.mp4 (70.47 MB)
2 -Regression Basics.mp4 (73.01 MB)
3 -Regression Code Preparation.mp4 (45.5 MB)
4 -Regression Notebook.mp4 (71.87 MB)
5 -Moore's Law.mp4 (30.56 MB)
6 -Moore's Law Notebook.mp4 (78.92 MB)
7 -Linear Classification Basics.mp4 (65.85 MB)
8 -Classification Code Preparation.mp4 (26.55 MB)
9 -Classification Notebook.mp4 (78.34 MB)
10 -Saving and Loading a Model.mp4 (28.76 MB)
11 -A Short Neuroscience Primer.mp4 (44.63 MB)
12 -How does a model learn.mp4 (50.07 MB)
13 -Model With Logits.mp4 (27.22 MB)
14 -Train Sets vs Validation Sets vs Test Sets.mp4 (52.17 MB)
15 -Suggestion Box.mp4 (27.15 MB)
1 -Artificial Neural Networks Section Introduction.mp4 (33.42 MB)
2 -Forward Propagation.mp4 (47 MB)
3 -The Geometrical Picture.mp4 (56.44 MB)
4 -Activation Functions.mp4 (89.24 MB)
5 -Multiclass Classification.mp4 (48.65 MB)
6 -How to Represent Images.mp4 (75.44 MB)
7 -Color Mixing Clarification.mp4 (4.85 MB)
8 -Code Preparation (ANN).mp4 (66.07 MB)
9 -ANN for Image Classification.mp4 (106.37 MB)
10 -ANN for Regression.mp4 (80.08 MB)
11 -How to Choose Hyperparameters.mp4 (39.47 MB)
1 -What is Convolution (part 1).mp4 (79.71 MB)
2 -What is Convolution (part 2).mp4 (24.06 MB)
3 -What is Convolution (part 3).mp4 (29.81 MB)
4 -Convolution on Color Images.mp4 (75.65 MB)
5 -CNN Architecture.mp4 (89.46 MB)
6 -CNN Code Preparation (part 1).mp4 (79.91 MB)
7 -CNN Code Preparation (part 2).mp4 (36.67 MB)
8 -CNN Code Preparation (part 3).mp4 (33.65 MB)
9 -CNN for Fashion MNIST.mp4 (73.75 MB)
10 -CNN for CIFAR-10.mp4 (55.28 MB)
11 -Data Augmentation.mp4 (44.44 MB)
12 -Batch Normalization.mp4 (23.41 MB)
13 -Improving CIFAR-10 Results.mp4 (75.68 MB)
1 -Sequence Data.mp4 (114.18 MB)
2 -Forecasting.mp4 (48.39 MB)
3 -Autoregressive Linear Model for Time Series Prediction.mp4 (81.23 MB)
4 -Proof that the Linear Model Works.mp4 (17.83 MB)
5 -Recurrent Neural Networks.mp4 (92.6 MB)
6 -RNN Code Preparation.mp4 (55.26 MB)
7 -RNN for Time Series Prediction.mp4 (71.73 MB)
8 -Paying Attention to Shapes.mp4 (56.32 MB)
9 -GRU and LSTM (pt 1).mp4 (79.78 MB)
10 -GRU and LSTM (pt 2).mp4 (50.41 MB)
11 -A More Challenging Sequence.mp4 (87.18 MB)
12 -RNN for Image Classification (Theory).mp4 (32.24 MB)
13 -RNN for Image Classification (Code).mp4 (20.48 MB)
14 -Stock Return Predictions using LSTMs (pt 1).mp4 (77.75 MB)
15 -Stock Return Predictions using LSTMs (pt 2).mp4 (43.24 MB)
16 -Stock Return Predictions using LSTMs (pt 3).mp4 (71.1 MB)
17 -Other Ways to Forecast.mp4 (28.29 MB)
1 -Embeddings.mp4 (59.91 MB)
2 -Neural Networks with Embeddings.mp4 (15.6 MB)
3 -Text Preprocessing Concepts.mp4 (52.3 MB)
4 -Beginner Blues - PyTorch NLP Version.mp4 (64.1 MB)
5 -(Legacy) Text Preprocessing Code Preparation.mp4 (44.34 MB)
6 -(Legacy) Text Preprocessing Code Example.mp4 (47.78 MB)
7 -Text Classification with LSTMs (V2).mp4 (117.18 MB)
8 -CNNs for Text.mp4 (58.4 MB)
9 -Text Classification with CNNs (V2).mp4 (54.46 MB)
10 -(Legacy) VIP Making Predictions with a Trained NLP Model.mp4 (48.81 MB)
11 -VIP Making Predictions with a Trained NLP Model (V2).mp4 (25.46 MB)
1 -Recommender Systems with Deep Learning Theory.mp4 (64.74 MB)
2 -Recommender Systems with Deep Learning Code Preparation.mp4 (40.07 MB)
3 -Recommender Systems with Deep Learning Code (pt 1).mp4 (69.53 MB)
4 -Recommender Systems with Deep Learning Code (pt 2).mp4 (76.79 MB)
5 -VIP Making Predictions with a Trained Recommender Model.mp4 (32.7 MB)
1 -Transfer Learning Theory.mp4 (58.1 MB)
2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 (21.6 MB)
3 -Large Datasets.mp4 (41.23 MB)
4 -2 Approaches to Transfer Learning.mp4 (21.75 MB)
5 -Transfer Learning Code (pt 1).mp4 (77.78 MB)
6 -Transfer Learning Code (pt 2).mp4 (56.26 MB)
1 -GAN Theory.mp4 (92.01 MB)
2 -GAN Code Preparation.mp4 (28.1 MB)
3 -GAN Code.mp4 (61.47 MB)
1 -Deep Reinforcement Learning Section Introduction.mp4 (40.72 MB)
2 -Elements of a Reinforcement Learning Problem.mp4 (104.97 MB)
3 -States, Actions, Rewards, Policies.mp4 (44.06 MB)
4 -Markov Decision Processes (MDPs).mp4 (50.45 MB)
5 -The Return.mp4 (23.37 MB)
6 -Value Functions and the Bellman Equation.mp4 (47.69 MB)
7 -What does it mean to "learn".mp4 (31.64 MB)
8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 (45.84 MB)
9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 (55.51 MB)
10 -Epsilon-Greedy.mp4 (41.46 MB)
11 -Q-Learning.mp4 (66.73 MB)
12 -Deep Q-Learning DQN (pt 1).mp4 (60.13 MB)
13 -Deep Q-Learning DQN (pt 2).mp4 (52.1 MB)
14 -How to Learn Reinforcement Learning.mp4 (40.27 MB)
1 -Reinforcement Learning Stock Trader Introduction.mp4 (28.78 MB)
2 -Data and Environment.mp4 (55.69 MB)
3 -Replay Buffer.mp4 (24.89 MB)
4 -Program Design and Layout.mp4 (26.85 MB)
5 -Code pt 1.mp4 (66.34 MB)
6 -Code pt 2.mp4 (70 MB)
7 -Code pt 3.mp4 (58.58 MB)
8 -Code pt 4.mp4 (52.59 MB)
9 -Reinforcement Learning Stock Trader Discussion.mp4 (17.17 MB)
1 -Custom Loss and Estimating Prediction Uncertainty.mp4 (43.47 MB)
2 -Estimating Prediction Uncertainty Code.mp4 (42.66 MB)
1 -Facial Recognition Section Introduction.mp4 (24.31 MB)
2 -Siamese Networks.mp4 (50.41 MB)
3 -Code Outline.mp4 (23.86 MB)
4 -Loading in the data.mp4 (35 MB)
5 -Splitting the data into train and test.mp4 (26.26 MB)
6 -Converting the data into pairs.mp4 (30.37 MB)
7 -Generating Generators.mp4 (32.46 MB)
8 -Creating the model and loss.mp4 (29.35 MB)
9 -Accuracy and imbalanced classes.mp4 (51.15 MB)
10 -Facial Recognition Section Summary.mp4 (18.31 MB)
1 -Mean Squared Error.mp4 (33.78 MB)
2 -Binary Cross Entropy.mp4 (23.61 MB)
3 -Categorical Cross Entropy.mp4 (31.72 MB)
1 -Gradient Descent.mp4 (34.92 MB)
2 -Stochastic Gradient Descent.mp4 (22.97 MB)
3 -Momentum.mp4 (34.26 MB)
4 -Variable and Adaptive Learning Rates.mp4 (34.84 MB)
5 -Adam (pt 1).mp4 (55.11 MB)
6 -Adam (pt 2).mp4 (52.77 MB)
1 -Where Are The Exercises.mp4 (25.94 MB)
1 -Pre-Installation Check.mp4 (22.74 MB)
2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (150.72 MB)
3 -Anaconda Environment Setup.mp4 (180.83 MB)
4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 (167.24 MB)
1 -Beginner's Coding Tips.mp4 (75.68 MB)
2 -How to Code Yourself (part 1).mp4 (71.83 MB)
3 -How to Code Yourself (part 2).mp4 (49.13 MB)
4 -Proof that using Jupyter Notebook is the same as not using it.mp4 (69.44 MB)
5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 (43.56 MB)
6 -How to use Github & Extra Coding Tips (Optional).mp4 (63.91 MB)
1 -How to Succeed in this Course (Long Version).mp4 (35.21 MB)
2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (105.56 MB)
3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (79.77 MB)
4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (108.14 MB)
1 -What is the Appendix.mp4 (16.36 MB)
2 -BONUS.mp4 (40.43 MB)]
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