High Learning Rate Effects
5 explanations from your library.
10:58IIT Madras (NPTEL)
Learning Rate Decay
“So, let us say alpha is a very large number, let us say close to 1, then your parameter values would vary rapidly, okay, they would vary rapidly and by large amounts and would not settle down in a local minima, however lower learning rate would lead to”
20:00Prof. Prabir Kumar Biswas, IIT Kharagpur
From AdaGrad to RMSProp
“try to solve this problem which is given by Adagrad. So, the algorithm that we will talk about is what is RMS prop which tries to address this problem of Adagrad algorithm that is vanishing learning rate as the time increases as the number of”
Closest moment in this session: it teaches the idea without listing it as a key concept.
31:04IIT Madras (NPTEL)
Gradient Descent Variants and Momentum
“In that sense you do not have to really have to code them but you just have to understand how they work and try out different things for your particular implementation of a deep learning technique or a machine learning technique.”
Closest moment in this session: it teaches the idea without listing it as a key concept.
40:00Prof. Prabir Kumar Biswas, IIT Kharagpur
Overfitting, Underfitting and Regularization
“underfitting problem. See overfitting occurs when a machine learning algorithms captures or fits or fits the training data too well.”
Closest moment in this session: it teaches the idea without listing it as a key concept.
50:21NPTEL: Practical ML with TensorFlow
Underfitting and Overfitting in Practice
“So what happens if we look at the learning curves, we observe that the training error and validation error both reduce to begin with.”
Closest moment in this session: it teaches the idea without listing it as a key concept.
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Compare reel
5 explanations of this concept, from different sessions, spliced into one video with fl_splice. Each clip is labelled with its speaker.
…/video/upload/so_57.2,eo_74.6,w_1280,h_720,c_fill/l_video:pravaha:1ba747ae-bdb0-43b5-bd11-8a0eda32a02c,fl_spl…/so_0,eo_16.6,w_1280,h_720,c_fill/fl_layer_apply/fl_layer_apply,g_north_west,x_40,y_40,so_63.9,eo_75.2/f_auto:video,q_auto/pravaha/2b17046e-fb2d-4a2c-ad2c-e84a76f8c28a.mp4
- so_57.2
- starts at 57.2 s · and 9 more like it
- eo_74.6
- ends at 74.6 s · and 9 more like it
- w_1280
- 1280 px wide · and 4 more like it
- h_720
- 720 px tall · and 4 more like it
- c_fill
- crops to fill the frame exactly · and 4 more like it
- l_video:pravaha:1ba747ae-bd…
- another video (pravaha/1ba747ae-bdb0-43b5-bd11-8a0eda32a02c) as a layer
- fl_splice
- joins the next clip onto the end of this one · and 3 more like it
- fl_layer_apply
- places the layer defined just before · and 8 more like it
- l_video:pravaha:ff2c7367-57…
- another video (pravaha/ff2c7367-5718-4b86-9d17-04328cf12206) as a layer
- l_video:pravaha:e35b9255-31…
- another video (pravaha/e35b9255-3117-4881-98cb-f7b89dc4d1cc) as a layer
- l_video:pravaha:ff809418-97…
- another video (pravaha/ff809418-9768-43d9-ab11-dd64c8941e4d) as a layer
- l_text:arial_34_bold:1%20%C…
- text layer “1 · IIT Madras (NPTEL)” · and 4 more like it
- co_white
- text colour white · and 4 more like it
- b_rgb:0f766ecc
- background #0f766ecc · and 4 more like it
- g_north_west
- anchored to the top-left corner · and 4 more like it
- x_40
- 40 px from the side · and 4 more like it
- y_40
- 40 px from the edge · and 4 more like it
- f_auto:video
- best format for each device (e.g. AV1, WebM, MP4, WebP)
- q_auto
- AI-chosen quality: smallest file that still looks right
Explanation thumbnail
The frame at that moment, cropped around the speaker by AI.
…/video/upload/so_58.7,c_fill,ar_16:9,w_640,g_auto/f_auto,q_auto/pravaha/2b17046e-fb2d-4a2c-ad2c-e84a76f8c28a.jpg
- so_58.7
- starts at 58.7 s
- c_fill
- crops to fill the frame exactly
- ar_16:9
- aspect ratio 16 : 9
- w_640
- 640 px wide
- g_auto
- AI picks the focus (the speaker or slide), not a blind centre crop
- f_auto
- best format for each device (e.g. AV1, WebM, MP4, WebP)
- q_auto
- AI-chosen quality: smallest file that still looks right
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