Deep learning for machine learning aspirants

01 - What is Deep Learning.srt
02 - Course Overview.srt
03 - Solving Problems - Big and Small.srt
04 - Lets Get Started.srt
05 - Supervised Classification.srt
06 - Classification For Detection Quiz.srt
07 - Classification For Detection Solution.srt
08 - Classification For Ranking Quiz.srt
09 - Classification For Ranking Solution.srt
10 - Lets make a deal.srt
11 - Training Your Logistic Classifier.srt
12 - Softmax Q.srt
13 - Softmax S.srt
14 - Softmax Quiz 2.srt
15 - Softmax Solution 2.srt
16 - Softmax Quiz 3.srt
17 - Softmax Solution 3.srt
18 - One-Hot Encoding.srt
19 - One-Hot Encoding Quiz.srt
20 - One-Hot Encoding Solution.srt
21 - Cross Entropy.srt
22 - Minimizing Cross Entropy.srt
23 - Transition into Practical Aspects of Learning.srt
24 - Numerical Stability.srt
25 - Numerical Stability.srt
26 - Normalized Inputs and Initial Weights.srt
27 - Lather Rinse Repeat.srt
28 - Measuring Performance.srt
29 - The Kaggle Challenge - Higgs Boson.srt
30 - Transition Overfitting - Dataset Size.srt
31 - Validation and Test Set Size.srt
32 - Validation Set Size.srt
33 - Validation Set Size.srt
34 - Validation Test Set Size Continued.srt
35 - Optimizing a Logistic Classifier.srt
36 - Stochastic Gradient Descent.srt
37 - Momentum and Learning Rate Decay.srt
38 - Parameter Hyperspace.srt

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