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gw gx gy gz ha gs gt paragraph-image"><div class="gs gt gu"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1280/format:webp/1*t3FYZ42yOVa0RKxOf3YDpQ.jpeg 1280w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and 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(-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 640px"/><img alt="" class="bh hb hc c" width="640" height="353" loading="eager" role="presentation"/></picture></div></figure><div><h1 id="7bfc" class="pw-post-title hd he hf bf hg hh hi hj hk hl hm hn ho hp hq hr hs ht hu hv hw hx hy hz ia ib ic id ie if bk" data-testid="storyTitle">NEURON IN NEURAL NETWORK(artificial neuron)</h1><div><div class="speechify-ignore ab cp"><div class="speechify-ignore bh l"><div class="ig ih ii ij ik ab"><div><div class="ab il"><div><div class="bm" aria-hidden="false"><a rel="noopener follow" href="/@vibhusharma_48046?source=post_page---byline--7420068e3055---------------------------------------"><div class="l im in by io ip"><div class="l fj"><img 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ng nh ni nj nk nl nm nn gn bk"><strong class="ms hg">Artificial Neural Networks</strong> (ANNs) are one of the main tools that take advantage of the concept of deep learning. They are an abstract representation of our nervous system, which contains a collection of neurons that communicate with each other through connections called axons</p><p id="5a3d" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk">Similar to the biological one, the artificial neuron consists of the following:</p><ul class=""><li id="5789" class="mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq bk">> it has multiple inputs connection and each inputs connectionwith some weights</li><li id="c324" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk">>it has multiple output connection (which helps to make further calculation</li><li id="0fa4" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk">>it has activation function which helps for making calculation and it make calculation on the basis of input connection signals and gives ouput for further calculation</li></ul><figure class="nx ny nz oa ob ha gs gt paragraph-image"><div class="gs gt nw"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 1100w, https://miro.medium.com/v2/resize:fit:588/format:webp/1*b9yKJvLIKf20UGa5a8G7bg.png 588w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 294px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*b9yKJvLIKf20UGa5a8G7bg.png 640w, https://miro.medium.com/v2/resize:fit:720/1*b9yKJvLIKf20UGa5a8G7bg.png 720w, https://miro.medium.com/v2/resize:fit:750/1*b9yKJvLIKf20UGa5a8G7bg.png 750w, https://miro.medium.com/v2/resize:fit:786/1*b9yKJvLIKf20UGa5a8G7bg.png 786w, https://miro.medium.com/v2/resize:fit:828/1*b9yKJvLIKf20UGa5a8G7bg.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*b9yKJvLIKf20UGa5a8G7bg.png 1100w, https://miro.medium.com/v2/resize:fit:588/1*b9yKJvLIKf20UGa5a8G7bg.png 588w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 294px"/><img alt="" class="bh hb hc c" width="294" height="171" loading="lazy" role="presentation"/></picture></div></figure><p id="7900" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk"><strong class="ms hg">summation function </strong>: <em class="oc">it helps for summing up all the input with respect to their weights make input for activation function for making decision or calculation on the basis of </em><strong class="ms hg"><em class="oc">types of activation function</em></strong></p><p id="2eca" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk"><strong class="ms hg"><em class="oc">X </em>=( A1W1+A2W2 + A3W3+…………. AnWn)</strong></p><p id="0b10" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk">now this <strong class="ms hg"><em class="oc">X </em></strong>is input for the activation function for making calculation or decision</p><p id="2ba2" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk"><strong class="ms hg">WHY WE NEED ACTIVATION FUNCTION ?</strong></p><p id="77e2" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk">neurons helps to perform task by machine by making decision and these decision making queries are solved by activation function (<em class="oc">activation mainly used for decision making</em>)</p><p id="ebb8" class="pw-post-body-paragraph mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn gn bk"><em class="oc">their are various type of activation function :( according to their different type of decision</em></p><ul class=""><li id="e521" class="mq mr hf ms b mt mu mv mw mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq bk"><em class="oc">Step function</em></li><li id="a2f7" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Linear combination</em></li><li id="ed68" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Sigmoid function</em></li><li id="eadd" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Hyperbolic tangent function</em></li><li id="2a6c" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk">S<em class="oc">oftmax function</em></li><li id="1293" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Radial basis function</em></li><li id="8511" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Conic section function</em></li><li id="6a88" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Relu activation function</em></li><li id="73f6" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Leaky ReLU</em></li><li id="7803" class="mq mr hf ms b mt nr mv mw mx ns mz na nb nt nd ne nf nu nh ni nj nv nl nm nn no np nq bk"><em class="oc">Tanh</em></li></ul></div></div></div></div></section></div></div></article></div><div class="ab cb"><div class="ci bh fz ga gb gc"><div class="od oe ab jg"><div class="of ab"><a class="og ay am ao" rel="noopener follow" href="/tag/machine-learning?source=post_page-----7420068e3055---------------------------------------"><div class="oh fj cx oi ge oj ok bf b bg z bk ol">Machine Learning</div></a></div><div class="of 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