Update README.md

Author: Andrea Palazzi

README.md

@@ -7,7 +7,7 @@
 ## Deep Learning
 
 ### Gradient Descent
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/gradient_descent/gradient_descent.pdf"><img src="./thumbs/gradient_descent.gif" alt="thumb_gradient_descent" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/gradient_descent/gradient_descent.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/gradient_descent.gif" alt="thumb_gradient_descent" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/slides/deep_learning/gradient_descent).
 <br>
@@ -16,21 +16,21 @@ Practice (1) [slides](https://github.com/ndrplz/machine_learning_lectures/blob/m
 Practice (2) [slides](https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/tensorflow_linear_regression/tex/tensorflow_regression.pdf) and [code](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/tensorflow_linear_regression/code) (TensorFlow).
 
 ### Neural Networks and Deep Neural Networks
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/deep_neural_networks/deep_neural_networks.pdf"><img src="./thumbs/deep_neural_networks.gif" alt="thumb_neural_networks" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/deep_neural_networks/deep_neural_networks.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/deep_neural_networks.gif" alt="thumb_neural_networks" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/deep_neural_networks/).
 <br>
 Practice [slides](https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/tensorflow_neural_network/tex/tensorflow_neural_nets.pdf) and [code](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/tensorflow_neural_network/code) (TensorFlow).
 
 ### Convolutional Neural Networks
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/convolutional_neural_networks/convolutional_neural_networks.pdf"><img src="./thumbs/convolutional_neural_networks.gif" alt="thumb_convnets" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/convolutional_neural_networks/convolutional_neural_networks.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/convolutional_neural_networks.gif" alt="thumb_convnets" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/slides/deep_learning/convolutional_neural_networks).
 <br>
 Practice [slides](https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/tensorflow_convolutional_nets/tex/tensorflow_convnets.pdf) and [code](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/tensorflow_convolutional_nets/code) (TensorFlow).
 
 ### Recurrent Neural Networks
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/recurrent_neural_networks/recurrent_neural_networks.pdf"><img src="./thumbs/recurrent_neural_networks.gif" alt="thumb_recurrent" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/deep_learning/recurrent_neural_networks/recurrent_neural_networks.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/recurrent_neural_networks.gif" alt="thumb_recurrent" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/slides/deep_learning/recurrent_neural_networks).
 <br>
@@ -41,14 +41,14 @@ Practice [slides](https://github.com/ndrplz/machine_learning_lectures/blob/maste
 ## Reinforcement Learning
 
 ### Introduction and Model Free Learning
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/01_introduction_and_model_free_learning/01_introduction_and_model_free_learning.pdf"><img src="./thumbs/01_introduction_and_model_free_learning.gif" alt="thumb_model_free" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/01_introduction_and_model_free_learning/01_introduction_and_model_free_learning.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/01_introduction_and_model_free_learning.gif" alt="thumb_model_free" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/01_introduction_and_model_free_learning/).
 <br>
 Practice [slides](https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/q_learning/tex/q_learning.pdf) and [code](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/q_learning/code) (TensorFlow).
 
 ### Function Approximation
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/02_function_approximation/02_function_approximation.pdf"><img src="./thumbs/02_function_approximation.gif" alt="thumb_fun_approx" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/02_function_approximation/02_function_approximation.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/02_function_approximation.gif" alt="thumb_fun_approx" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/blob/master/slides/reinforcement_learning/02_function_approximation/).
 
@@ -57,47 +57,47 @@ LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/blob/ma
 ## Machine Learning
 
 ### Boosting
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/boosting/tex/boosting.pdf"><img src="./thumbs/boosting.gif" alt="thumb_boosting" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/boosting/tex/boosting.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/boosting.gif" alt="thumb_boosting" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/boosting/tex).
 <br>
 Practice code: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/boosting/code).
 
 ### Clustering
 
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/clustering/tex/clustering.pdf"><img src="./thumbs/clustering.gif" alt="thumb_clustering" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/clustering/tex/clustering.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/clustering.gif" alt="thumb_clustering" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/clustering/tex).
 <br>
 Practice code: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/clustering/code).
 
 ### Dimensionality Reduction
 
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/dimensionality_reduction/tex/dimensionality_reduction.pdf"><img src="./thumbs/dimensionality_reduction.gif" alt="thumb_dim_reduction" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/dimensionality_reduction/tex/dimensionality_reduction.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/dimensionality_reduction.gif" alt="thumb_dim_reduction" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/dimensionality_reduction/tex).
 <br>
 Practice code: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/dimensionality_reduction/code).
 
 ### Logistic Regression
 
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/logistic_regression/tex/logistic_regression.pdf"><img src="./thumbs/logistic_regression.gif" alt="thumb_logistic_regression" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/logistic_regression/tex/logistic_regression.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/logistic_regression.gif" alt="thumb_logistic_regression" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/logistic_regression/tex).
 <br>
 Practice code: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/logistic_regression/code).
 
 ### Naive Bayes
 
-<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/naive_bayes/tex/naive_bayes.pdf"><img src="./thumbs/naive_bayes.gif" alt="thumb_bayes" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/blob/master/lab/naive_bayes/tex/naive_bayes.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/naive_bayes.gif" alt="thumb_bayes" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/naive_bayes/tex).
 <br>
 Practice code: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/naive_bayes/code).
 
 ### Support Vector Machine (SVM)
 
-<a href="https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/support_vector_machines/tex/support_vector_machines.pdf"><img src="./thumbs/support_vector_machines.gif" alt="thumb_svm" height="256px"></a>
+<a href="https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/support_vector_machines/tex/support_vector_machines.pdf"><img src="https://github.com/ndrplz/machine_learning_lectures/blob/master/thumbs/support_vector_machines.gif" alt="thumb_svm" height="256px"></a>
 <br>
 LaTeX source: [here](https://github.com/ndrplz/machine_learning_lectures/tree/master/lab/support_vector_machines/tex).
 <br>